CN105072581B - A kind of indoor orientation method that storehouse is built based on path attenuation coefficient - Google Patents

A kind of indoor orientation method that storehouse is built based on path attenuation coefficient Download PDF

Info

Publication number
CN105072581B
CN105072581B CN201510531126.6A CN201510531126A CN105072581B CN 105072581 B CN105072581 B CN 105072581B CN 201510531126 A CN201510531126 A CN 201510531126A CN 105072581 B CN105072581 B CN 105072581B
Authority
CN
China
Prior art keywords
mrow
msub
mtr
mtd
mfrac
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201510531126.6A
Other languages
Chinese (zh)
Other versions
CN105072581A (en
Inventor
秦爽
段林甫
聂永峰
吴国栋
仰石
柏思琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Stop Carbon Technology Co.,Ltd.
Original Assignee
Sichuan Xingwang Yunlian Science & Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sichuan Xingwang Yunlian Science & Technology Co Ltd filed Critical Sichuan Xingwang Yunlian Science & Technology Co Ltd
Priority to CN201510531126.6A priority Critical patent/CN105072581B/en
Publication of CN105072581A publication Critical patent/CN105072581A/en
Application granted granted Critical
Publication of CN105072581B publication Critical patent/CN105072581B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H04W16/225—Traffic simulation tools or models for indoor or short range network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Medicines Containing Antibodies Or Antigens For Use As Internal Diagnostic Agents (AREA)

Abstract

The invention discloses a kind of indoor orientation method that storehouse is built based on path attenuation coefficient, this method is divided into two parts of training stage and positioning stage, training stage mainly utilizes known received signal strength and range information, try to achieve each reference mode corresponding attenuation coefficient in the environment, and the mean attenuation coefficient that signal is propagated in environment, positioning stage is mainly the reference mode tried to achieve using the training stage corresponding attenuation coefficient in the environment, try to achieve the distance between this blind node and the distance and blind spot and blind node of corresponding reference mode, and then try to achieve the relative coordinate position of each blind node.This localization method positioning accuracy of the present invention is high, implements simply, system power dissipation is low, and cost is low.

Description

A kind of indoor orientation method that storehouse is built based on path attenuation coefficient
Technical field
The invention belongs to wireless network field of locating technology, and in particular to a kind of interior that storehouse is built based on path attenuation coefficient Localization method.
Background technology
With the rise of Internet of Things concept, and mobile computing device developing rapidly and popularizing, wireless sensor network (Wireless Sensing Network, WSN) starts the hot spot as industry research.The target of wireless sensor network be by Original scattered and independent hardware node connects, one shared network of composition, and each node in network, which has, to be received The ability of signalling, and required environmental information is provided, to reach the interaction of user and instrument.So-called environmental information, its It is important that it is exactly spatial positional information to have in, if the current location of node can be obtained, then many practical Property function can be achieved with.Such as：Wireless sensor node is arranged in bulk supermarket, is matched somebody with somebody in each shelf and shopping cart Node is put, such customer can be quickly found out required shelf according to the positional information of shopping cart so that shopping process tends to certainly Dynamicization；A set of physical signs monitor is carried with hospital or community's arrangement wireless sensor node, each patient, and Patient location can be obtained at any time, holds therapic opportunity in time to monitoring center, monitoring center by wireless network real-time delivery； Wireless sensor node is arranged in museum, tourist allots small-sized transceiver, and intelligent guide system can be according to the current of tourist Position real-time broadcast different content, and from the limitation of travelling route.
In wireless sensor network, conventional mapping methods are first to estimate the distance between movement station and base station, Ran Houtong The estimation that some algorithms based on dimensional measurement realize positional information is crossed, therefore, distance estimations are the bases of location algorithm.To Up to the time (Time of Arrival, TOA) measure in, the distance between signal node be by the measuring signal propagation time with What the product of signal velocity obtained；In reaching time-difference (Time Difference of Arrival, TDOA) measurement, no Time differences of multiple receiving nodes is reached to obtain by measuring signal with the distance between signal node difference；Received signal strength The propagation loss model that (Time Difference of Arrival, TDOA) measures then basis signal derives that signal node is straight The distance connect.Although above-mentioned conventional mapping methods have algorithm it is simple, it is easy to implement, it is relatively low to System Hardware Requirement the features such as, But since the estimation adjusted the distance easily is subject to multipath effect, the shadow of the factor such as the signal attenuation of nlos environment and particular surroundings Ring, the propagation of signal is difficult that accurate prediction is provided in a manner of model, and distance estimations have large error, result in positional information Estimation it is inaccurate.General such method only can be suitably used for the localizing environment of outdoor, and positioning accuracy indoors will substantially reduce.
Fast-developing a kind of localization method is then to build storehouse recognition methods using fingerprint identification technology now, and is generally adopted Storehouse information is built with based on received signal strength.Build storehouse recognition methods and be divided into offline and online two stages.Off-line phase, it is fixed Position system gathers in each coordinate known point and stores signal strength, as building storehouse parameter.On-line stage, alignment system will measure To the signal strength that collects of signal strength and off-line phase matched, so as to provide positional information.Common matching is calculated Method has, probabilistic method (Probabilistic Methods), rank field method (k-Nearest-Neighbor, kNN), nerve net Network (Neural Networks), support vector machines (Support Vector Machine, SVM), minimum polygon (Smallest M-vertex Polygon, SMP) method.Although the above method has the accurate advantage of positioning result, its from Line number is a complicated process according to the storehouse stage is built, and computing capability and memory capacity to system hardware propose very high request, System cost is added, and the location requirement of emergency case can not be tackled.It is indoor fixed that general such method only can be suitably used for Position environment, outdoor utility this system positioning cost considerably beyond positioning accuracy raising.
The content of the invention
It is an object of the invention to provide a kind of indoor orientation method of environment adaptive channel model, solves in the prior art The problem of positioning accuracy is low, implementation is complicated, of high cost.
In order to achieve the above object, the present invention adopts the following technical scheme that：
The localization method of the present invention is divided into two parts of training stage and positioning stage.
In the present invention, sub-fraction node possesses a priori location information, referred to as reference mode, remaining node to be positioned Positional information it is unknown, referred to as blind node.Whether all nodes, have the co-ordinate position information of priori without considering it, all need Received signal strength measurement is carried out to the node in its effective propagation path, and section is converted to by maximum Likelihood Distance between point.
In the training stage, the unknown blind node of a coordinate moves in the environment of required positioning along random walk, reference Node sends signal, blind node receive signal and tracer signal from transmitting node, along random walk tracer signal intensity Purpose is the received signal strength of each point in abundant collection environment, to try to achieve the fading channel system that can completely describe environment Number, by certain deformation to channel attenuation model, using known distance between the received signal strength of each node and each node, The channel fading coefficient for portraying current environment is deduced, is deformed by formula, each reference mode can be tried to achieve at the same time in environment In corresponding attenuation coefficient, and in environment signal propagate mean attenuation coefficient；
Positioning stage, the quantity of reference mode and position are without changing, signal transmitting and reception mode and training stage phase Together, one or more blind nodes to be positioned, all sections including blind node and reference mode are added at the same time in the environment Point sends signal at the same time, and each blind node receives the signal strength for coming from remaining node, and tracer signal from transmitting section Point.When signal comes from reference mode, the reference mode tried to achieve using the training stage corresponding attenuation coefficient in the environment, knot Maximum likelihood range estimating equation is closed, tries to achieve this blind node and the distance of corresponding reference mode, to come from another blind when signal During node, then the mean attenuation coefficient for the signal propagation tried to achieve using the training stage, with reference to maximum likelihood range estimating equation, is asked The distance between blind node must be corresponded to, after the distance between each node is accurately estimated in the environment, just utilizes maximum likelihood coordinate Estimation formulas tries to achieve the relative coordinate position of each blind node.
Location algorithm comprises the following steps：
Step 1：Path attenuation system of each signal reference mode in application environment is tried to achieve by measured signal intensity data Number
Reference mode uses wide band direct sequence spread spectrum transceiver with blind node, by battery-powered, launches narrow band power Signal, the signal antenna of receiver is maintained at 1.5 meters of height, and each signal transmitter is sent along random walk in doors Narrow band power signal measure, while record the distance on random walk between each measurement point and signal transmitter, define The signal strength measurement for j-th of signal transmitter that ith measurement point receives is p on random walkR(i, j) [dB], it is fixed Ith measurement point path loss measurement corresponding with j-th of signal transmitter is signal transmission power P on adopted random walkT The difference of (i, j) [dB] and signal strength measurement, and be expressed as
Define indoor environment under channel propagation model be
Wherein PLmol(d) [dB] is the path loss prediction value when signal receiver and transmitter distance are known quantity d, PL(d0) [dB] be in reference distance d0The path loss measurement at place, usually takes d in the case of indoor short-distance transmission0=1m, this When can also pass through free path loss model and calculate PL (d0) [dB], n is channel fading coefficient, and which depict signal strength The relation of path attenuation speed and distance, all path loss values are all free path loss value (the Free Space with 1 meter Path Loss, FSPL) as reference, its decibel of expression formula is
Wherein c represents the light velocity, and d is 1 meter of reference distance, and f is emission signal frequency,
Defining path attenuation coefficient vector is
N=[n1n2 … nm]
nj(j=1,2 ..., m) has corresponded to path attenuation coefficient of each signal transmitter indoors in environment, thus, It can be expressed as in the Systems with Linear Observation equation of ith measurement point
Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is that to obey zero equal It is worth the interchannel noise of Gaussian Profile, Systems with Linear Observation equation can be further written as
And simplification is written as
xi=Hin+wi, i=1,2 ..., L
After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) Synthesize as next dimension isVector
And N × m observing matrixes H is defined accordingly and N-dimensional observation noise vector w is as follows
Thus, total observational equation on random walk can be written as
X=Hn+w
To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, construction is estimated MeteringIt is performance indicator
Reach minimum, solve for
Step 2：The average path attenuation coefficient in application environment is tried to achieve by measured signal intensity data
It can be expressed as in the Systems with Linear Observation equation of ith measurement point
Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is to obey average It is that zero variance is σ2Gaussian Profile interchannel noise, Systems with Linear Observation equation can further be written as
And simplification is written as
xi=nHi+wi, i=1,2 ..., L
After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) Synthesize as next dimension isVector
And it is as follows to define N × 1 measurement vector H and N-dimensional observation noise vector w accordingly
Thus, total observational equation on random walk can be written as
X=nH+w
To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, construction is estimated MeteringIt is performance indicator
Reach minimum, solve for
Step 3：Each node implements point-to-point signal strength measurement, and calculates mutual distance
Starting to position, node used mutually measures the received signal strength value in its effective propagation path in environment, with Exemplified by i-node receives the signal that j nodes are launched in application environment, it is P that it, which measures the signal strength of gained,i,j, its distance is most Maximum-likelihood estimate is
Wherein d0It is reference distance, p0It is the received signal strength measured under reference distance, and n is path attenuation coefficient, when When received signal comes from reference mode, using the corresponding path attenuation coefficient of this reference mode, if coming from blind node, Using average path attenuation coefficient, if the signal transmission between certain two node is in disarmed state, by its mutual distance Default value is set to, generally takes application environment length and wide average distance.
Step 4：Finally calculate blind node position
Each node measurement and record other nodes transmission received signal strength value after, using maximal possibility estimation Method, estimates the position of each blind node, and formula is as follows
Wherein, zi=(xi,yi), θ=[z1z2 … zn] n blind node coordinate is represented,It is its maximal possibility estimation Value, can be in the hope of by steepest descent method.
Beneficial effects of the present invention：
First：The training stage of the present invention is to utilize known received signal strength and range information, and it is required fixed to try to achieve Position environment channel decay cover half type really, compensate for attenuation coefficient in basic skills cannot reflect true environment and caused by distance Evaluated error, so as to improve positioning accuracy；
Second：The self-organizing network positioning system that the present invention is built is that multiple blind node positions are carried out while estimated, energy Enough realize to the requirement of real-time of each blind node location estimation in alignment system, meanwhile, it is new blind in the case of positioning accuracy is ensured The addition of node, by increasing capacitance it is possible to increase position required range information so that positioning accuracy improves, and need not be to the section that newly adds Point does any setting, has given full play to self-organizing network characteristic；
3rd：The present invention is much smaller than due to only building storehouse, required hardware resource to environmental attenuation coefficient and builds storehouse localization method.And Compare and improve 20% in the range of two meters with basic fixed position method, positioning accuracy；
4th：In the present invention, most range informations being converted to by received signal strength information come from respectively Measurement between a blind node, so the reference mode quantity required in the present invention is effectively reduced, is not influencing positioning accuracy In the case of, system power dissipation, cost is effectively controlled；
5th：The present invention only need to carry out one-shot measurement to localizing environment, after obtaining channel parameter, can realize accurate online Positioning in real time, needs to build storehouse offline or field intensity curve learning method is compared, be easy to implement with method with tradition, required priori number The characteristics of according to amount minimum, the requirement to positioning system hardware is greatly reduced, low cost, the requirement of low-power consumption can be reached；
6th：The obtained channel parameter of the present invention contains two classes, first, each reference mode is in application environment Dissemination channel parameter, parameter is different and different for transmitter, has good pairing characteristic；Second, application environment is averaged Channel parameter, propagates measurement, the application of two class parameters can effectively improve system positioning accurate mainly for the signal between blind node Degree, realizes the pairing for specific environment, in the present invention, can realize mobile node positioning and ipc monitor work(at the same time Energy.
Brief description of the drawings
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is for the present invention and basic fixed position method and compared with building the cumulative errors function of storehouse localization method；
Fig. 2 is position error and blind node Figure of the quantitative relationship.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other without creative efforts Embodiment, belongs to the scope of protection of the invention.
A kind of embodiment of the present invention, in the present embodiment, sub-fraction node possesses a priori location information, referred to as ginseng Node is examined, the positional information of remaining node to be positioned is unknown, referred to as blind node.Whether all nodes, have first without considering it The co-ordinate position information tested, is required for carrying out received signal strength measurement to the node in its effective propagation path, and by most Maximum-likelihood method of estimation is converted to euclidean distance between node pair.In cellular network location information estimating method and local positioning system, Range information needed for positioning is only between blind node and reference mode, and in the present embodiment, the overwhelming majority is by receiving The range information that signal strength information is converted to comes from the measurement between each blind node.
Experimental situation is an indoor office room environmental, and at 30 meters or so, the average path of environment declines room-size length and width It is 3.4 to subtract coefficient, and reference node points are 4, in the overall indoor environment that one is not split by wall, at each angle of ceiling Put off and put reference mode, and provide its coordinate, in positioning indoors, coordinate is traditionally arranged to be relative coordinate, and the present embodiment is determined Bit stream journey, can be divided into two parts of training stage and positioning stage.
In the training stage, the unknown blind node of a coordinate moves in the environment of required positioning along random walk, reference Node sends signal, blind node receive signal and tracer signal from transmitting node.Along random walk tracer signal intensity Purpose is the received signal strength of each point in abundant collection environment, to try to achieve the fading channel system that can completely describe environment Number.By certain deformation to channel attenuation model, using known distance between the received signal strength of each node and each node, Derive the channel fading coefficient for portraying current environment.Deformed by formula, each reference mode can be tried to achieve at the same time in the environment Corresponding attenuation coefficient, and the mean attenuation coefficient that signal is propagated in environment, so as to fully describe current environment, and obtain Positional parameter be only one group of vector, by taking single indoor environment arranges four reference modes as an example, the element of this group vector Number only 5, has been significantly smaller than the received signal strength amount of storage of each point in the environment built in the identification of storehouse and stored.And trained Journey is taken also much smaller than building storehouse recognition positioning method.
Positioning stage, the quantity of reference mode and position are without changing, signal transmitting and reception mode and training stage phase Together.Add one or more blind nodes to be positioned, all sections including blind node and reference mode at the same time in the environment Point sends signal at the same time, and each blind node receives the signal strength for coming from remaining node, and tracer signal from transmitting section Point.When signal comes from reference mode, the reference mode tried to achieve using the training stage corresponding attenuation coefficient in the environment, knot Maximum likelihood range estimating equation is closed, tries to achieve this blind node and the distance of corresponding reference mode, to come from another blind when signal During node, then the mean attenuation coefficient for the signal propagation tried to achieve using the training stage, with reference to maximum likelihood range estimating equation, is asked The distance between blind node must be corresponded to.After the distance between each node is accurately estimated in the environment, maximum likelihood coordinate is just utilized Estimation formulas tries to achieve the relative coordinate position of each blind node.
Comprised the following steps using location algorithm measure blind spot position：
Step 1：Path attenuation system of each signal reference mode in application environment is tried to achieve by measured signal intensity data Number
Reference mode uses wide band direct sequence spread spectrum transceiver with blind node, by battery-powered, launches narrow band power Signal, the signal antenna of receiver is maintained at 1.5 meters of height, and each signal transmitter is sent along random walk in doors Narrow band power signal measure, while record the distance on random walk between each measurement point and signal transmitter, define The signal strength measurement for j-th of signal transmitter that ith measurement point receives is p on random walkR(i, j) [dB], it is fixed Ith measurement point path loss measurement corresponding with j-th of signal transmitter is signal transmission power P on adopted random walkT The difference of (i, j) [dB] and signal strength measurement, and be expressed as
Define indoor environment under channel propagation model be
Wherein PLmol(d) [dB] is the path loss prediction value when signal receiver and transmitter distance are known quantity d, PL(d0) [dB] be in reference distance d0The path loss measurement at place, usually takes d in the case of indoor short-distance transmission0=1m, this When can also pass through free path loss model and calculate PL (d0) [dB], n is channel fading coefficient, and which depict signal strength The relation of path attenuation speed and distance, all path loss values are all free path loss value (the Free Space with 1 meter Path Loss, FSPL) as reference, its decibel of expression formula is
Wherein c represents the light velocity, and d is 1 meter of reference distance, and f is emission signal frequency,
Defining path attenuation coefficient vector is
N=[n1n2 … nm]
nj(j=1,2 ..., m) has corresponded to path attenuation coefficient of each signal transmitter indoors in environment, thus, It can be expressed as in the Systems with Linear Observation equation of ith measurement point
Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is that to obey zero equal It is worth the interchannel noise of Gaussian Profile, Systems with Linear Observation equation can be further written as
And simplification is written as
xi=Hin+wi, i=1,2 ..., L
After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) Synthesize as next dimension isVector
And N × m observing matrixes H is defined accordingly and N-dimensional observation noise vector w is as follows
Thus, total observational equation on random walk can be written as
X=Hn+w
To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, construction is estimated MeteringIt is performance indicator
Reach minimum, solve for
Step 2：The average path attenuation coefficient in application environment is tried to achieve by measured signal intensity data
It can be expressed as in the Systems with Linear Observation equation of ith measurement point
Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is to obey average It is that zero variance is σ2Gaussian Profile interchannel noise, Systems with Linear Observation equation can further be written as
And simplification is written as
xi=nHi+wi, i=1,2 ..., L
After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) Synthesize as next dimension isVector
And it is as follows to define N × 1 measurement vector H and N-dimensional observation noise vector w accordingly
Thus, total observational equation on random walk can be written as
X=nH+w
To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, construction is estimated MeteringIt is performance indicator
Reach minimum, solve for
Step 3：Each node implements point-to-point signal strength measurement, and calculates mutual distance
Starting to position, node used mutually measures the received signal strength value in its effective propagation path in environment, with Exemplified by i-node receives the signal that j nodes are launched in application environment, it is P that it, which measures the signal strength of gained,i,j, its distance is most Maximum-likelihood estimate is
Wherein d0It is reference distance, p0It is the received signal strength measured under reference distance, and n is path attenuation coefficient, when When received signal comes from reference mode, using the corresponding path attenuation coefficient of this reference mode, if coming from blind node, Using average path attenuation coefficient, if the signal transmission between certain two node is in disarmed state, by its mutual distance Default value is set to, generally takes application environment length and wide average distance.
Step 4：Finally calculate blind node position
In each node measurement and after recording the received signal strength value of other nodes transmission, the present invention using it is maximum seemingly Right method of estimation, estimates the position of each blind node, and formula is as follows
Wherein, zi=(xi,yi), θ=[z1z2 … zn] n blind node coordinate is represented,It is its maximal possibility estimation Value, can be in the hope of by steepest descent method.
It will be seen from figure 1 that the present invention has similar cumulative errors distribution function, but this compared with building storehouse localization method Hardware resource needed for invention is compared and carried with basic fixed position method, positioning accuracy in the range of two meters much smaller than storehouse localization method is built It is high by 20%.
From fig. 2 it can be seen that with the increase of blind node quantity, positioning accuracy is also increasing, and works as blind node quantity When continuing to increase to 16 nodes, the improvement change of positioning accuracy tends towards stability.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention With within principle, any modification, equivalent replacement, improvement and so on, should all be included in the protection scope of the present invention god.

Claims (1)

  1. A kind of 1. indoor orientation method that storehouse is built based on path attenuation coefficient, it is characterised in that：Including training stage and positioning rank Section,
    The training stage is specially：The unknown blind node of one coordinate moves in the environment of required positioning along random walk, Reference mode sends signal, blind node receive signal and tracer signal from transmitting node, by channel attenuation model Deformation, using known distance between the received signal strength of each node and each node, derives the fading channel system of current environment Number, is deformed by formula, can try to achieve each reference mode corresponding attenuation coefficient in the environment at the same time, and signal passes in environment The mean attenuation coefficient broadcast；
    Step 1：Path attenuation coefficient of each signal reference mode in application environment is tried to achieve by measured signal intensity data
    Reference mode uses wide band direct sequence spread spectrum transceiver with blind node, by battery-powered, launches narrow band power signal, The signal antenna of receiver is maintained at 1.5 meters of height, and along random walk each signal transmitter is sent in doors narrow Band power signal measures, while records the distance on random walk between each measurement point and signal transmitter, and definition is random The signal strength measurement for j-th of signal transmitter that ith measurement point receives is p on pathR(i, j) [dB], define with Ith measurement point path loss measurement corresponding with j-th of signal transmitter is signal transmission power P on machine pathT(i,j) The difference of [dB] and signal strength measurement, and be expressed as
    <mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>P</mi> <mi>T</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>P</mi> <mi>R</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <msub> <mi>P</mi> <mi>T</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <msub> <mi>P</mi> <mi>R</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Define indoor environment under channel propagation model be
    <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <msub> <mi>nlog</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <mi>d</mi> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> </mrow>
    Wherein PLmol(d) [dB] is the path loss prediction value when signal receiver and transmitter distance are known quantity d, PL (d0) [dB] be path loss measurement at reference distance d0, d0=1m is usually taken in the case of indoor short-distance transmission, this When can also pass through free path loss model and calculate PL (d0) [dB], n is channel fading coefficient, and which depict signal strength The relation of path attenuation speed and distance, all path loss values are all free path loss value (the Free Space with 1 meter Path Loss, FSPL) as reference, its decibel of expression formula is
    <mrow> <mi>F</mi> <mi>S</mi> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <mi>d</mi> <mi>B</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>20</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <mrow> <mn>4</mn> <mi>&amp;pi;</mi> </mrow> <mi>c</mi> </mfrac> <mi>d</mi> <mi>f</mi> <mo>)</mo> </mrow> </mrow>
    Wherein c represents the light velocity, and d is 1 meter of reference distance, and f is emission signal frequency,
    Defining path attenuation coefficient vector is
    N=[n1 n2 … nm]
    nj(j=1,2 ..., m) has corresponded to path attenuation coefficient of each signal transmitter indoors in environment, thus, at i-th The Systems with Linear Observation equation of measurement point can be expressed as
    <mrow> <mi>i</mi> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <msub> <mi>n</mi> <mn>1</mn> </msub> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <msub> <mi>n</mi> <mn>2</mn> </msub> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <msub> <mi>n</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </msub> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is that to obey zero-mean high The interchannel noise of this distribution, Systems with Linear Observation equation can be further written as
    And simplification is written as
    xi=Hin+wi, i=1,2 ..., L
    After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) synthesis As next dimension isVector
    <mrow> <mi>x</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    And N × m observing matrixes H is defined accordingly and N-dimensional observation noise vector w is as follows
    <mrow> <mi>H</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>H</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>H</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>H</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <mi>w</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>w</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Thus, total observational equation on random walk can be written as
    X=Hn+w
    To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, estimator is constructed It is performance indicator
    Reach minimum, solve for
    <mrow> <mover> <mi>n</mi> <mo>^</mo> </mover> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>H</mi> <mi>T</mi> </msup> <mi>H</mi> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>H</mi> <mi>T</mi> </msup> <mi>x</mi> <mo>;</mo> </mrow>
    Step 2：The average path attenuation coefficient in application environment is tried to achieve by measured signal intensity data
    It can be expressed as in the Systems with Linear Observation equation of ith measurement point
    <mrow> <mi>i</mi> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <mi>n</mi> <mi> </mi> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <mi>n</mi> <mi> </mi> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>=</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>+</mo> <mn>10</mn> <mi>n</mi> <mi> </mi> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein Ni(Ni≤ m) signal receiving strength that illustrates in ith measurement point effectively measures number, and w is that to obey average be zero Variance is σ2Gaussian Profile interchannel noise, Systems with Linear Observation equation can further be written as
    <mrow> <msub> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>PL</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> <mo>-</mo> <mi>P</mi> <mi>L</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mi>d</mi> <mi>B</mi> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>&amp;times;</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mi>n</mi> <msub> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>10</mn> <msub> <mi>log</mi> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>N</mi> </mrow> </msub> <msub> <mi>d</mi> <mn>0</mn> </msub> </mfrac> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>&amp;times;</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>w</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    And simplification is written as
    xi=nHi+wi, i=1,2 ..., L
    After the Systems with Linear Observation equation of ith measurement point is constructed, L measurement vector x of wholei(i=1,2 ..., L) synthesis As next dimension isVector
    <mrow> <mi>x</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    And it is as follows to define N × 1 measurement vector H and N-dimensional observation noise vector w accordingly
    <mrow> <mi>H</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>H</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>H</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>H</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <mi>w</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>w</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>w</mi> <mi>L</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Thus, total observational equation on random walk can be written as
    X=nH+w
    To make the mean square error of estimator reach minimum, according to the construction of Linear least square estimation amount rule, estimator is constructed It is performance indicator
    <mrow> <mi>J</mi> <mrow> <mo>(</mo> <mover> <mi>n</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <mover> <mi>n</mi> <mo>^</mo> </mover> <mi>H</mi> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <mover> <mi>n</mi> <mo>^</mo> </mover> <mi>H</mi> <mo>)</mo> </mrow> </mrow>
    Reach minimum, solve for
    <mrow> <mover> <mi>n</mi> <mo>~</mo> </mover> <mo>=</mo> <mfrac> <mrow> <msup> <mi>H</mi> <mi>T</mi> </msup> <mi>x</mi> </mrow> <mrow> <mo>(</mo> <msup> <mi>H</mi> <mi>T</mi> </msup> <mi>H</mi> <mo>)</mo> </mrow> </mfrac> <mo>;</mo> </mrow>
    The positioning stage is specially：The quantity of reference mode and position are without changing, signal transmitting and reception mode and training Stage is identical, one or more blind nodes to be positioned is added at the same time in the environment, including blind node and reference mode All nodes send signal at the same time, and each blind node receives the signal strength for coming from remaining node, and tracer signal from Transmitting node, when signal comes from reference mode, the reference mode corresponding decay in the environment tried to achieve using the training stage Coefficient, with reference to maximum likelihood range estimating equation, tries to achieve this blind node and the distance of corresponding reference mode, when signal comes from separately During one blind node, then the mean attenuation coefficient for the signal propagation tried to achieve using the training stage, with reference to maximum likelihood distance estimations Formula, tries to achieve the distance between corresponding blind node, after the distance between each node is accurately estimated in the environment, utilizes maximum likelihood Coordinate estimation formulas tries to achieve the relative coordinate position of each blind node；
    Step 3：Each node implements point-to-point signal strength measurement, and the specific method for calculating mutual distance is：
    Start to position, node used mutually measures the received signal strength value in its effective propagation path in environment, using ring I-node receives the signal that j nodes are launched in border, and it is P that it, which measures the signal strength of gained,i,j, the maximal possibility estimation of its distance It is worth and is
    <mrow> <msub> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <msub> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>j</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <msup> <mrow> <mo>(</mo> <mfrac> <msub> <mi>p</mi> <mn>0</mn> </msub> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>n</mi> </mfrac> </msup> </mrow>
    Wherein d0It is reference distance, p0It is the received signal strength measured under reference distance, and n is path attenuation coefficient, when being connect When being come from reference mode by signal, using the corresponding path attenuation coefficient of this reference mode, if coming from blind node, apply Average path attenuation coefficient, if the signal transmission between certain two node is in disarmed state, its mutual distance is set to Default value, generally takes application environment length and wide average distance；
    Step 4：Finally calculating blind node position is specially：
    Each node measurement and record other nodes transmission received signal strength value after, using maximal possibility estimation side Method, estimates the position of each blind node, and formula is as follows
    <mrow> <mover> <mi>&amp;theta;</mi> <mo>~</mo> </mover> <mo>=</mo> <munder> <mi>argmin</mi> <mi>&amp;theta;</mi> </munder> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>m</mi> <mo>+</mo> <mi>n</mi> </mrow> </munderover> <munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>&amp;Element;</mo> <mi>H</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </munder> <mrow> <mi>j</mi> <mo>&lt;</mo> <mi>i</mi> </mrow> </munder> <msup> <mrow> <mo>(</mo> <mi>ln</mi> <mfrac> <msubsup> <mover> <mi>d</mi> <mo>~</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> <mrow> <msup> <mi>d</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <msub> <mi>z</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>z</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Wherein, zi=(xi,yi), xiFor the x-axis coordinate value of the blind node, yiFor the y-axis coordinate value of the blind node, θ=[z1 z2 … zn] n blind node coordinate is represented,It is its maximum likelihood estimator, can be in the hope of by steepest descent method.
CN201510531126.6A 2015-08-26 2015-08-26 A kind of indoor orientation method that storehouse is built based on path attenuation coefficient Active CN105072581B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510531126.6A CN105072581B (en) 2015-08-26 2015-08-26 A kind of indoor orientation method that storehouse is built based on path attenuation coefficient

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510531126.6A CN105072581B (en) 2015-08-26 2015-08-26 A kind of indoor orientation method that storehouse is built based on path attenuation coefficient

Publications (2)

Publication Number Publication Date
CN105072581A CN105072581A (en) 2015-11-18
CN105072581B true CN105072581B (en) 2018-04-20

Family

ID=54501835

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510531126.6A Active CN105072581B (en) 2015-08-26 2015-08-26 A kind of indoor orientation method that storehouse is built based on path attenuation coefficient

Country Status (1)

Country Link
CN (1) CN105072581B (en)

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107305247B (en) * 2016-04-25 2021-04-20 华为技术有限公司 Channel model formula correction method, device and equipment
CN105911540B (en) 2016-06-01 2021-04-16 北京京东方多媒体科技有限公司 Wireless positioning system and exercise training system
CN106060782A (en) * 2016-07-21 2016-10-26 刘儿兀 Indoor positioning system and method based on distributed antenna system and measurement report (MR)
CN106992822A (en) * 2017-03-29 2017-07-28 国网重庆市电力公司电力科学研究院 A kind of localization method of the blind node of wireless sensor network
CN107040876A (en) * 2017-03-31 2017-08-11 上海斐讯数据通信技术有限公司 A kind of localization method and server based on WIFI
CN107071897B (en) * 2017-04-07 2020-04-10 辽宁工程技术大学 Wi-Fi indoor positioning method based on ring
CN107181543B (en) * 2017-05-23 2020-10-27 张一嘉 Three-dimensional indoor passive positioning method based on propagation model and position fingerprint
CN107820206B (en) * 2017-11-15 2020-04-14 玉林师范学院 Non-line-of-sight positioning method based on signal intensity
CN108882155B (en) * 2018-07-10 2020-11-10 深圳无线电检测技术研究院 Particle swarm algorithm-based blind signal power and blind source position determination method and system
CN110519685A (en) * 2019-08-22 2019-11-29 湖南红太阳新能源科技有限公司 Indoor orientation method, device and medium based on WiFi
CN110933629B (en) * 2019-11-26 2021-06-15 通号万全信号设备有限公司 Method for measuring transmission characteristics of wireless equipment
CN113765599A (en) * 2020-06-04 2021-12-07 普天信息技术有限公司 Radio direction finding positioning method and device
CN112857369A (en) * 2020-07-02 2021-05-28 王世琳 Indoor positioning system based on optical communication

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103941225A (en) * 2014-04-21 2014-07-23 哈尔滨工业大学 Indoor fingerprint positioning system simulation method based on Matlab2008

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103941225A (en) * 2014-04-21 2014-07-23 哈尔滨工业大学 Indoor fingerprint positioning system simulation method based on Matlab2008

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Accurate Path-Loss Exponent Correcting Location Method;BAI Si-qi等;《IEEE》;20140915;第472-475页 *
利用步行惯性导航的室内定位融合算法研究;柏思琪等;《现代电子技术》;20150801;第1-4页 *

Also Published As

Publication number Publication date
CN105072581A (en) 2015-11-18

Similar Documents

Publication Publication Date Title
CN105072581B (en) A kind of indoor orientation method that storehouse is built based on path attenuation coefficient
CN103402258B (en) Wi-Fi (Wireless Fidelity)-based indoor positioning system and method
Kim et al. Distance estimation with weighted least squares for mobile beacon-based localization in wireless sensor networks
CN104333903A (en) Indoor multi-object positioning system and method based on RSSI (receiver signal strength indicator) and inertia measurement
Yang et al. Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application
CN109444814A (en) A kind of indoor orientation method based on bluetooth and RFID fusion positioning
CN102364983B (en) RSSI (Received Signal Strength Indicator) ranging based WLS (WebLogic Server) node self-positioning method in wireless sensor network
Trogh et al. Enhanced indoor location tracking through body shadowing compensation
CN104053129A (en) Wireless sensor network indoor positioning method and device based on sparse RF fingerprint interpolations
CN109212476A (en) A kind of RFID indoor positioning algorithms based on DDPG
CN110636436A (en) Three-dimensional UWB indoor positioning method based on improved CHAN algorithm
CN104507097A (en) Semi-supervised training method based on WiFi (wireless fidelity) position fingerprints
Eldeeb et al. Optimal placement of access points for indoor positioning using a genetic algorithm
Shuo et al. Design of an experimental indoor position system based on RSSI
Ahmad et al. Fuzzy-logic based localization for mobile sensor Networks
Goonjur et al. Enhanced the weighted centroid localization algorithm based on received strength signal in indoor wireless sensor network
CN107801168B (en) Outdoor self-adaptive passive target positioning method
CN104955148A (en) Positioning method of wireless sensor network using symmetrical propagation of electromagnetic wave
Cao A localization algorithm based on particle swarm optimization and quasi-newton algorithm for wireless sensor networks
Rana et al. Indoor Positioning using DNN and RF Method Fingerprinting-based on Calibrated Wi-Fi RTT
CN108225321A (en) A kind of indoor orientation method under the auxiliary based on mobile node
Iacono et al. Distance measurement characterization for ultra wide band indoor localization systems
Mahardhika et al. Improving indoor positioning systems accuracy in closed buildings with kalman filter and feedback filter
Ahmed et al. Nakagami-m distribution of RSSI in shadowing pathloss model for indoor localization
Assayag et al. A model-based BLE indoor positioning system using particle swarm optimization

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20220823

Address after: C6-2-1, Building 5, East District, No. 10, Northwest Wangdong Road, Haidian District, Beijing 100000

Patentee after: Beijing Stop Carbon Technology Co.,Ltd.

Address before: No. 1715, 17th Floor, Unit 1, Building 2, No. 1700, North Section of Tianfu Avenue, High-tech Zone, Chengdu, Sichuan 610000

Patentee before: SICHUAN AIROCOV TECHNOLOGY Co.,Ltd.

TR01 Transfer of patent right