CN106507473A - A kind of indoor orientation method and device based on compressed sensing algorithm - Google Patents
A kind of indoor orientation method and device based on compressed sensing algorithm Download PDFInfo
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- CN106507473A CN106507473A CN201610941217.1A CN201610941217A CN106507473A CN 106507473 A CN106507473 A CN 106507473A CN 201610941217 A CN201610941217 A CN 201610941217A CN 106507473 A CN106507473 A CN 106507473A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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Abstract
The embodiment of the invention discloses a kind of indoor orientation method and device based on compressed sensing algorithm, is related to internet of things field.The embodiment of the present invention includes：In area to be targeted deployment signal receiving array；K wireless mobile apparatus constitute K signaling point, the K wireless mobile apparatus and the signal receiving array direction communication, obtain array received signal x；Using compressed sensing algorithm, computation and measurement vector, the first geographical position of the K signaling point is positioned；First geographical position and Mac addresses of corresponding signaling point are transferred to radio network gateway by the K wireless mobile apparatus, and radio network gateway is transmitted to high in the clouds Surveillance center；After K wireless mobile apparatus move to the second geographical position, positioning obtains the second geographical position that quantity is K wireless mobile apparatus；Compare two different geographical position of identical Mac addresses, programme path is simultaneously sent to wireless mobile apparatus and carry out indoor positioning.The embodiment of the present invention is suitable for the environment of only a small amount of sample or sampling high cost.
Description
Technical field
The present invention relates to Internet of Things application, especially, is related to a kind of indoor positioning side based on compressed sensing algorithm
Method and device.
Background technology
In actual applications, people's target interested is only angle-resolved units few in number, thus can be by
It is sparse that target regards distribution in the spatial domain as, points out people that compressed sensing can be applied in location estimation.Based on sparse
The compressive sensing theory that signal is represented breaches Nyquist sampling theorem, it is achieved that the memory space of data and process time
Dual saving, its main thought are when signal is sparse or compressible on certain transform domain, it is possible to use one with
The high dimensional signal for receiving is projected to one with the speed far below nyquist sampling rate by the incoherent calculation matrix of conversion base
Individual low-dimensional spatially, then recovers primary signal by the algorithm for reconstructing high probability of signal, and such projection is included
The enough information of reconstruction signal.
The outstanding advantages of compressed sensing signal be aiming at can rarefaction representation signal, can be by traditional data acquisition and number
Unite two into one according to compression, greatly reduce acquisition time and the memory space of data.Can be fixed by multiple target using compressed sensing
Position estimation problem regards the reconstruction of a sparse vector, the nonzero element of the sparse vector and its position in vector as
Information characterizes the amplitude information and angle information of target respectively, so as to obtain high resolution, good anti-with less sampling
The advantages of making an uproar property.
With radio communication, integrated circuit and MESH（Wireless mesh network）The technology such as network are developed rapidly and increasingly
Maturation, traditional DOA（(Direction of Arrival, signal direction of arrival）Algorithm leads to obtain preferable estimation effect
A large amount of samplings, or method using increase array element quantity are often needed.But so increase data storage, transmission, calculating process
Etc. aspect difficulty, and be not suitable in the scene of only a small amount of sample or sampling high cost.
Content of the invention
Embodiments of the invention provide a kind of indoor orientation method based on compressed sensing algorithm, and can be directed to can sparse table
The signal for showing, traditional data acquisition and data compression are united two into one, and acquisition time and the storage for greatly reducing data is empty
Between, it is applicable in the scene of only a small amount of sample or sampling high cost.
For reaching above-mentioned purpose, embodiments of the invention are adopted the following technical scheme that：
In a first aspect, embodiments of the invention provide a kind of indoor orientation method based on compressed sensing algorithm, including：
S1, in area to be targeted deployment signal receiving array, the signal receiving array is by M beacon（beacon）Point structure
Into；
S2, K wireless mobile apparatus constitute K signaling point, the K wireless mobile apparatus and the signal receiving array
Direction communication, obtains array received signal x；
S3, adopt compressed sensing algorithm, computation and measurement vectorPosition the first of the K signaling point
Geographical position；
First geographical position and Mac addresses of corresponding signaling point are transferred to by S4, the K wireless mobile apparatus
Radio network gateway, radio network gateway are transmitted to high in the clouds Surveillance center；
After S5, K wireless mobile apparatus move to the second geographical position, step S2 to S4 is repeated, positioning is counted
Measure the second geographical position for K wireless mobile apparatus；
S6, two different geographical position for comparing identical Mac addresses, programme path are simultaneously sent to wireless mobile apparatus,
Carry out indoor positioning.
In conjunction with a first aspect, in the first possible implementation of first aspect, positioning the ground of the K signaling point
Reason position, is using compressed sensing algorithm construction spectral function, searches for K maximum spectral peak, calculate direction of arrival and positioned.
In conjunction with a first aspect, in the first possible implementation of first aspect, measuring vector in step S3
Gauss likelihood function be：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli that selects
Calculation matrix, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
In conjunction with a first aspect, in the first possible implementation of first aspect, being changed using MAP in step S3
X and σ is estimated for method2?：
α(t+1)=[φHφ+σ2(t)(|αn (t)|(2-q))-1]-1φHy
=|αn (t)|(2-q)φH(φ|αn (t)|(2-q)φH+σ2(t)I)-1y
Wherein：I be unit matrix, q=[1 ..., Q]
In conjunction with a first aspect, in the first possible implementation of first aspect, the wireless mobile apparatus are indigo plant
Tooth movement equipment；The radio network gateway is bluetooth radio network gateway.Main equipment of the radio network gateway as network, wireless mobile apparatus,
Such as mobile phone constitutes network as being dispersed in the communication range that specifies from node by MANET mode.
In conjunction with a first aspect, in second possible implementation of first aspect, the wireless mobile apparatus pass through
Wave point is connected with radio network gateway, and the wave point is bluetooth wireless interface.
Second aspect, embodiments of the invention provide a kind of indoor positioning device based on compressed sensing algorithm, including：
Signal receiving array module：Area to be targeted is deployed in, by M beacon（beacon）Point is constituted；
Signal receiving module：K wireless mobile apparatus constitute K signaling point, the K wireless mobile apparatus and the letter
Number receiving array direction communication, for obtaining array received signal x；
First Geographic mapping module：Using compressed sensing algorithm, computation and measurement vectorFor fixed
First geographical position of the position K signaling point.
Transport module：By the K wireless mobile apparatus by the first geographical position of corresponding signaling point and Mac ground
Location is transferred to radio network gateway, and radio network gateway is transmitted to high in the clouds Surveillance center；
Second Geographic mapping module：After K wireless mobile apparatus move to the second geographical position, signal is repeated
To transport module, positioning obtains the second geographical position that quantity is K wireless mobile apparatus to receiver module；
Indoor positioning module：For comparing two different geographical position of identical Mac addresses, programme path and send to
Wireless mobile apparatus, carry out indoor positioning.
In conjunction with second aspect, in the first possible implementation of second aspect, the first Geographic mapping module
In, the geographical position of the K signaling point is positioned, is using compressed sensing algorithm construction spectral function, is searched for K maximum spectral peak,
Calculate direction of arrival to be positioned.
In conjunction with second aspect, in the first possible implementation of second aspect, first Geographic mapping
In module, the Gauss likelihood function of measurement vector is：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli that selects
Calculation matrix, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
In conjunction with second aspect, in the first possible implementation of second aspect, first Geographic mapping
In module, x and σ is estimated using MAP alternative manners2：
α(t+1)=[φHφ+σ2(t)(|αn (t)|(2-q))-1]-1φHy
=|αn (t)|(2-q)φH(φ|αn (t)|(2-q)φH+σ2(t)I)-1y
Wherein：I be unit matrix, q=[1 ..., Q]
In conjunction with second aspect, in the first possible implementation of second aspect, the wireless mobile apparatus are indigo plant
Tooth movement equipment；The radio network gateway is bluetooth radio network gateway.
In conjunction with second aspect, in second possible implementation of second aspect, the wireless mobile apparatus pass through
Wave point is connected with radio network gateway, and the wave point is bluetooth wireless interface.
A kind of indoor orientation method and device based on compressed sensing algorithm provided in an embodiment of the present invention, by undetermined
Position regional deployment signal receiving array, K wireless mobile apparatus K signaling point of composition, the K wireless mobile apparatus with described
Signal receiving array direction communication, obtains array received signal x；Using compressed sensing algorithm, computation and measurement vectorPosition the first geographical position of the K signaling point；The K wireless mobile apparatus will be corresponding
First geographical position and Mac addresses of signaling point is transferred to radio network gateway, and radio network gateway is transmitted to high in the clouds Surveillance center；K nothing
After line mobile device moves to the second geographical position, step S2 to S4 is repeated, positioning obtains quantity and sets for K wireless mobile
The second standby geographical position；Two different geographical position of identical Mac addresses are compared, is assigned to by reverse communication link
Wireless mobile apparatus, realize reversely seeking position using the navigation of wireless mobile apparatus, it is adaptable to underground garage navigation system.
The embodiment of the present invention proposes a kind of indoor orientation method based on compressed sensing algorithm, first finds signal receiving array
The high dimensional signal for receiving is projected to one with the speed far below nyquist sampling rate by the calculation matrix of the signal of output
Low-dimensional spatially, then recovers primary signal by the algorithm for reconstructing high probability of signal, and such projection has been contained
The enough information of reconstruction signal, calculates direction of arrival information in conjunction with norm optimization is solved.
A kind of indoor orientation method based on compressed sensing algorithm that the embodiment of the present invention is proposed, outstanding advantages are for can
The signal of rarefaction representation, traditional data acquisition and data compression are united two into one, greatly reduce data the acquisition time and
Memory space.The reconstruction that multiple target, positioning, estimation problem are configured to a sparse vector using compressed sensing, this is dilute
The nonzero element and its positional information in vector for dredging vector characterizes the amplitude information and angle information of target respectively, so as to
The advantages of less sampling obtains high resolution, good noise immunity.Meanwhile, programming is simple, it is easy to carry out indoor positioning.
Description of the drawings
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, below by to be used needed for embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for ability
For the those of ordinary skill of domain, on the premise of not paying creative work, can be other attached to be obtained according to these accompanying drawings
Figure.
Fig. 1 is a kind of schematic flow sheet based on compressed sensing algorithm indoor orientation method that the embodiment of the present invention 1 is provided；
Fig. 2 is that one kind that the embodiment of the present invention 1 is provided receives battle array based on signal in compressed sensing algorithm indoor orientation method
Row receive signal schematic representation；
Fig. 3 is that one kind that the embodiment of the present invention 1 is provided is shown based on aisle layout in compressed sensing algorithm indoor orientation method
Meaning block diagram；
Fig. 4 is individual signals in a kind of algorithm indoor orientation method based on compressed sensing that the embodiment of the present invention 1 is provided
DOA estimation condition figures；
Fig. 5 is two signals in a kind of algorithm indoor orientation method based on compressed sensing that the embodiment of the present invention 1 is provided
DOA estimation condition figures；
Fig. 6 is multiple signals in a kind of algorithm indoor orientation method based on compressed sensing that the embodiment of the present invention 1 is provided
DOA estimation condition figures；
Fig. 7 is that one kind that the embodiment of the present invention 1 is provided is bent based on square error in compressed sensing algorithm indoor orientation method
Line chart；
Fig. 8 is that one kind that the embodiment of the present invention 2 is provided is based on compressed sensing algorithm indoor positioning schematic device.
Specific embodiment
For making those skilled in the art more fully understand technical scheme, below in conjunction with the accompanying drawings and specific embodiment party
Formula is described in further detail to the present invention.It is described in more detail below embodiments of the present invention, the embodiment shows
Example is shown in the drawings, and wherein same or similar label represents same or similar element or there is identical or class from start to finish
Element like function.Embodiment below with reference to Description of Drawings is exemplary, is only used for explaining the present invention, and can not
It is construed to limitation of the present invention.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein（Including technology art
Language and scientific terminology）There is the general understanding identical meaning with the those of ordinary skill in art of the present invention.Should also
It is understood by, those terms defined in such as general dictionary should be understood that the meaning having with the context of prior art
The consistent meaning of justice, and unless defined as here, will not be with idealizing or excessively formal implication is explaining.
Embodiments of the invention provide a kind of indoor orientation method based on compressed sensing algorithm, and can be directed to can sparse table
The signal for showing, traditional data acquisition and data compression are united two into one, and acquisition time and the storage for greatly reducing data is empty
Between, it is applicable in the scene of only a small amount of sample or sampling high cost.
For reaching above-mentioned purpose, embodiments of the invention are adopted the following technical scheme that：
In a first aspect, embodiments of the invention provide a kind of indoor orientation method based on compressed sensing algorithm, such as Fig. 1 institutes
Show, including：
S1, in area to be targeted deployment signal receiving array, the signal receiving array is by M beacon（beacon）Point structure
Into；
S2, K wireless mobile apparatus constitute K signaling point, the K wireless mobile apparatus and the signal receiving array
Direction communication, obtains array received signal x；
As shown in Figures 2 and 3, the embodiment of the present invention includes：By the stop that K spacing is d（Array element）Constitute signal to connect
Receive array, M beacon（beacon）Launch point, corresponding angle of incidence are θ, and θ is the angle with normal direction, the noise of each array element
It is white Gaussian noise, between each array element, noise is separate, and noise is also separate with signal.
Array received signal can be expressed as：
Wherein, αkT () is signal amplitude information, akFor steering vector, if during using uniform linear array,It follows that it is multiple multiple sines to receive signal
Cumulative, and every string a of array manifold matrix of signalkA locus θ of target is answeredk, whole array prevalence matrix bag
The whole azimuth information of target is contained.As in the range of actual spatial domain, target to be observed only occupies a small amount of angle-resolved unit,
Therefore observed object is sparse distribution in the range of spatial domain, thus is applied in location estimation for compressed sensing and provides theory
Foundation.
In this step, by K wireless mobile apparatus transmission signal, M beacon（beacon）Point constitutes signal and receives
Array received feedback information are to wireless mobile apparatus；In area to be targeted, all of wireless mobile apparatus can be direct
With each beacon（beacon）Point is communicated, and the MAC Address of each wireless mobile apparatus can be conveniently entered and be left from group
Net mode constitutes network.
S3, adopt compressed sensing algorithm, computation and measurement vectorPosition the first of the K signaling point
Geographical position；
To present in space, K narrow band signal carries out location estimation when, using compressed sensing principle, arrow can be measured
Measure and be
Wherein,Be select gaussian random or bernoulli calculation matrix, x for K array element received signal vector, Ψ
It is sparse basis array, α is sparse coefficient vector.The building method of Ψ is described below.
It is P to assume that each array element obtains the length of primary signal, in order to obey openness principle, it is assumed that P ＞ ＞ K, will
The Spacial domain decomposition of consideration is { θ1,θ2,…θQ, and assume that each possible position corresponds to a potential signal, dive
Much bigger than in esse radiation source number of target number, so as to construct Q × 1 comprising realistic objective signal
The sparse signal α of dimension, only has K position of realistic objective to have an element of non-zero in α, and other positions to be zero, Ψ be LP
× Q sparse basis arrays, can be unrelated with actual DOA directions by the direction vector of any potential target as its column vector, because
And the structure of echo signal array manifold matrix can transform sparse basis array Ψ as, thus DOA estimation problems are reformed into
Solve and contain sparse coefficient problem, the dimension of Ψ determines estimated accuracy size,Be select LQ × LP dimension gaussian random or
Bernoulli calculation matrix, it is LQ that initial data is tieed up dimensionality reduction from LP thus, and matrix Θ is calculation matrixWith array manifold matrix
The product of the two, constitutes new random matrix, because any random matrix is incoherent with unit base, it is possible to see Θ
Make the product of random matrix and unit matrix, meet RIP criterions, it is ensured that the effectiveness for DOA estimations being carried out using restructing algorithm
And robustness, therefore the present invention solves DOA estimation problems using compressive sensing theory, is exactly to be determined by known measurement vector
Array manifold matrix reconstructing echo signal vector, wherein front K maximum reconstruct component is exactly observed spatially reality
The reconstruction signal of the echo signal that border is present, so as to according to { θ1,θ2,…θQAnd the one-to-one relationship of α obtain echo signal
DOA estimates, you can obtain reconstruction signal to solve norm optimization：
Directly Optimization Solution underdetermined system of equations formula, finds maximum of which K value, obtains DOA angles by determining its position.
When noise is added, formula（2）Can be rewritten as：
Wherein, v is that the noise component(s) for obeying circle multiple Gauss distribution is tieed up in LQ × 1.
Then optimized algorithm formula（3）It is changed into
Wherein, σ is the component relevant with noise, can pass through BP（Backpropagation algorithm, back propagation
Algorithm）、MOP（Multi-objective programming, multiple objective programming）Scheduling algorithm is solved.Can be with from above analysis
See, the structure of array manifold matrix is determined by the rarefaction of echo signal, different rarefaction method for expressing will be produced
Whether the array prevalence matrix of different structure, rarefaction method for expressing appropriate, will dominate whether preferably can carry out sparse
Reconstruct.In embodiments of the present invention, the signal reconstruction problem of compressed sensing will be solved using bayes method, be that compressed sensing is carried
For theoretical frame and method for solving.
First geographical position and Mac addresses of corresponding signaling point are transferred to by S4, the K wireless mobile apparatus
Radio network gateway, radio network gateway are transmitted to high in the clouds Surveillance center；
After S5, K wireless mobile apparatus move to the second geographical position, step S2 to S4 is repeated, positioning is counted
Measure the second geographical position for K wireless mobile apparatus；
After wireless mobile apparatus changing position, the position for finding out original place is wanted（That is the first geographical position）, then again
Secondary location Calculation is carried out according to compressed sensing algorithm, obtain each the second geographical location information of wireless mobile apparatus, then by nothing
The second geographical location information of the Mac address informations of line mobile device and wireless mobile apparatus, is sent to high in the clouds by radio network gateway
Surveillance center, is stored；Specially：When high in the clouds carries out remotely control to wireless mobile apparatus, by various instructions according to
The data form packing of setting, is sent to wireless mobile apparatus by wave point.
S6, two different geographical position for comparing identical Mac addresses, programme path are simultaneously sent to wireless mobile apparatus,
Carry out indoor positioning.
In conjunction with a first aspect, in the first possible implementation of first aspect, positioning the ground of the K signaling point
Reason position, is using compressed sensing algorithm construction spectral function, searches for K maximum spectral peak, calculate direction of arrival and positioned.
In conjunction with a first aspect, in the first possible implementation of first aspect, in step S3, it is assumed that introduce
Noise separate, and obey average be zero, variance is σ2Gauss distribution, the probability of sparse signal x obeys exponential,
Then formula（4）Gauss likelihood function can be expressed as：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli survey that selects
Moment matrix, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
In conjunction with a first aspect, in the first possible implementation of first aspect, using MAP side in step S3
Method estimates x and σ2：
Wherein, y | α, σ2Obey CN (φ α, σ2I),f(σ2)∝1.
Formula（7）Both sides take negative logarithm, can obtain
From formula（8）It can be seen that working as q=1, last is changed into 2 | | α | |1- 2P, with wide variety of L1 norm constraints phase
Similar.
Assume initial estimateX and σ is tried to achieve in order to circulate2Optimal value, will using point
Step iterative method fixes σ2Optimize x, fixed x optimizes σ2.Solution procedure is as follows：
1）G is minimized according to α firstq(α,η(t)), multiple derivative (the d/d α of orderH)gq(α,σ2(t)) it is zero to obtain α(t+1).The party
Method causes following nonlinear equation：
It is difficult to obtain α from above formula(t+1), to formula（4）, formula（6）And formula（7）Carry out simple transformation to obtain
[φHφ+σ2(t)(|αn (t)|(2-q))-1]α-φHy=0 （10）
Then
α=[φHφ+σ2(t)(|αn (t)|(2-q))-1]-1φHy （11）
Using formula（12）It is iterated computing to obtain：
α(t+1)=[φHφ+σ2(t)(|αn (t)|(2-q))-1]-1φHy
=|αn (t)|(2-q)φH(φ|αn (t)|(2-q)φH+σ2(t)I)-1y （12）
2）Secondly, according to σ2Minimize gq(α,σ2(t)).Make (d/d σ2)gq(α,σ2(t)) 0 being equal to, can cause：
In conjunction with a first aspect, in the first possible implementation of first aspect, the wireless mobile apparatus are indigo plant
Tooth movement equipment；The radio network gateway is bluetooth radio network gateway.Main equipment of the radio network gateway as network, wireless mobile apparatus,
Such as mobile phone constitutes network as being dispersed in the communication range that specifies from node by MANET mode.
In conjunction with a first aspect, in second possible implementation of first aspect, the wireless mobile apparatus pass through
Wave point is connected with radio network gateway, and the wave point is bluetooth wireless interface.
As shown in Figure 4, it is considered to the DOA estimation conditions of individual signals.Individual signals incide 10 array element uniform lines from far field
Battle array, 4 ° of azimuth, scattering coefficient is 1, half λ/2 of the array element distance for signal highest frequency corresponding wavelength.The length of discrete signal
Spend for N=2232, observing matrixUsing bernoulli matrix, recovery algorithms are that this section proposes algorithm.The quantity of compression sampling point is M
=215, M<<N, adds the white Gaussian noise that average is that 0 variance is 1 in the signal, and signal to noise ratio is 10dB.
As shown in figure 5, two incoherent signals incide 10 array element even linear arrays from far field, 4 ° of azimuth, scatters by 18 °
Coefficient is Isosorbide-5-Nitrae, and half λ/2 of the array element distance for signal highest frequency corresponding wavelength, when signal to noise ratio is 10dB, carry out 10 independences
The spatial domain Power estimation output contrast of Monte Carlo experiment.
As shown in fig. 6, multiple incoherent signal DOA estimation conditions, -20 ° of azimuth, -10 °, -5 °, -2 °, 0 °, 4 °, 8 °,
10 °, 20 °, 23 °, 30 °, 30 °, scattering coefficient is 7,11,8,3,2,6,1,5,10,9,4,12, and signal to noise ratio 10dB carries out 10 times
Independent Monte Carlo experiment, Fig. 6 are exported for spatial domain Power estimation, it can be seen that under multiple target, remaining to after CS compressions
The angle position at target place is successfully estimated.
As shown in fig. 7, the DOA of algorithm estimates root-mean-square error with measurement data relation curve.Calculation matrix is respectively adopted
Random Gaussian matrix and bernoulli matrix, other specification are identical with experiment one.
MIMO for even linear array（Multiple-Input Multiple-Output, multi-input multi-output system）Many
Input multi output radar signal model, the DOA for estimating signal using compression sensing method can accurately estimate target with high probability
DOA.And its MSE（Mean Square Error, mean square error）Increase with the increase of measurement data, and estimable
Target number also increases with the increase of measurement data.Can be suitable under low signal-to-noise ratio environment, and significantly reduce operand；
Compared with traditional DOA algorithm for estimating, the algorithm can carry out high resolution DOA estimation to any coherence's signal, and estimate tool
There are higher resolving power and more excellent estimation performance, be a kind of very strong super resolution algorithm of adaptability.
Second aspect, embodiments of the invention provide a kind of indoor positioning device based on compressed sensing algorithm, such as Fig. 8 institutes
Show, including：
Signal receiving array module：Area to be targeted is deployed in, by M beacon（beacon）Point is constituted；
Signal receiving module：K wireless mobile apparatus constitute K signaling point, the K wireless mobile apparatus and the letter
Number receiving array direction communication, for obtaining array received signal x；
First Geographic mapping module：Using compressed sensing algorithm, computation and measurement vectorFor fixed
First geographical position of the position K signaling point.
Transport module：By the K wireless mobile apparatus by the first geographical position of corresponding signaling point and Mac ground
Location is transferred to radio network gateway, and radio network gateway is transmitted to high in the clouds Surveillance center；
Second Geographic mapping module：After K wireless mobile apparatus move to the second geographical position, signal is repeated
To transport module, positioning obtains the second geographical position that quantity is K wireless mobile apparatus to receiver module；
Indoor positioning module：For comparing two different geographical position of identical Mac addresses, programme path and send to
Wireless mobile apparatus, carry out indoor positioning.
In conjunction with second aspect, in the first possible implementation of second aspect, the first Geographic mapping module
In, the geographical position of the K signaling point is positioned, is using compressed sensing algorithm construction spectral function, is searched for K maximum spectral peak,
Calculate direction of arrival to be positioned.
In conjunction with second aspect, in the first possible implementation of second aspect, first Geographic mapping
In module, the Gauss likelihood function of measurement vector is：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli survey that selects
Moment matrix, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
In conjunction with second aspect, in the first possible implementation of second aspect, first Geographic mapping
In module, x and σ is estimated using MAP alternative manners2：
α(t+1)=[φHφ+σ2(t)(|αn (t)|(2-q))-1]-1φHy
=|αn (t)|(2-q)φH(φ|αn (t)|(2-q)φH+σ2(t)I)-1y
Wherein：I be unit matrix, q=[1 ..., Q]
In conjunction with second aspect, in the first possible implementation of second aspect, the wireless mobile apparatus are indigo plant
Tooth movement equipment；The radio network gateway is bluetooth radio network gateway.
In conjunction with second aspect, in second possible implementation of second aspect, the wireless mobile apparatus pass through
Wave point is connected with radio network gateway, and the wave point is bluetooth wireless interface.
A kind of method of indoor positioning device based on compressed sensing algorithm provided in an embodiment of the present invention and dress
Put, based on compressive sensing theory, the docking collection of letters number carries out time domain data compression sampling, builds compressed sensing model so that
Perceive matrix and meet RIP conditions, by building calculation matrix, in conjunction with improved sparse signal method for reconstructing, by less sample
This quantity constructs the high-resolution location estimation method of performance improvement reconstructing sparse echo signal vector.
A kind of indoor orientation method and device based on compressed sensing algorithm provided in an embodiment of the present invention, by undetermined
Position regional deployment signal receiving array, K wireless mobile apparatus K signaling point of composition, the K wireless mobile apparatus with described
Signal receiving array direction communication, obtains array received signal x；Using compressed sensing algorithm, computation and measurement vectorPosition the first geographical position of the K signaling point；The K wireless mobile apparatus will be corresponding
First geographical position and Mac addresses of signaling point is transferred to radio network gateway, and radio network gateway is transmitted to high in the clouds Surveillance center；K nothing
After line mobile device moves to the second geographical position, step S2 to S4 is repeated, positioning obtains quantity and sets for K wireless mobile
The second standby geographical position；Two different geographical position of identical Mac addresses are compared, is assigned to by reverse communication link
Wireless mobile apparatus, realize reversely seeking position using the navigation of wireless mobile apparatus, it is adaptable to underground garage navigation system.
The embodiment of the present invention proposes a kind of indoor orientation method based on compressed sensing algorithm, first finds signal receiving array
The high dimensional signal for receiving is projected to one with the speed far below nyquist sampling rate by the calculation matrix of the signal of output
Low-dimensional spatially, then recovers primary signal by the algorithm for reconstructing high probability of signal, and such projection has been contained
The enough information of reconstruction signal, calculates direction of arrival information in conjunction with norm optimization is solved.
A kind of indoor orientation method based on compressed sensing algorithm that the embodiment of the present invention is proposed, outstanding advantages are for can
The signal of rarefaction representation, traditional data acquisition and data compression are united two into one, greatly reduce data the acquisition time and
Memory space.The reconstruction that multiple target, positioning, estimation problem are configured to a sparse vector using compressed sensing, this is dilute
The nonzero element and its positional information in vector for dredging vector characterizes the amplitude information and angle information of target respectively, so as to
The advantages of less sampling obtains high resolution, good noise immunity.Meanwhile, programming is simple, it is easy to carry out indoor positioning.
Each embodiment in this specification is described by the way of going forward one by one, identical similar portion between each embodiment
Divide mutually referring to what each embodiment was stressed is the difference with other embodiment.Especially for equipment reality
For applying example, as which is substantially similar to embodiment of the method, so describing fairly simple, related part is referring to embodiment of the method
Part explanation.
The above, the only specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, the change or replacement that can readily occur in all are answered
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be defined by scope of the claims.
Claims (10)
1. a kind of indoor orientation method based on compressed sensing algorithm, it is characterised in that include：
S1, in area to be targeted deployment signal receiving array, the signal receiving array is made up of M Beacon Point (beacon)；
S2, K wireless mobile apparatus constitute K signaling point, and the K wireless mobile apparatus are direct with the signal receiving array
Communication, obtains array received signal x；
S3, adopt compressed sensing algorithm, computation and measurement vectorPosition the first geographical position of the K signaling point
Put；
First geographical position and Mac addresses of corresponding signaling point are transferred to wirelessly by S4, the K wireless mobile apparatus
Gateway, is transmitted to high in the clouds Surveillance center by the radio network gateway；
After S5, K wireless mobile apparatus move to the second geographical position, step S2 to S4 is repeated, positioning obtains quantity for K
Second geographical position of individual wireless mobile apparatus；
S6, two different geographical position for comparing identical Mac addresses, programme path simultaneously send to wireless mobile apparatus, carry out
Indoor positioning.
2. a kind of indoor orientation method based on compressed sensing algorithm according to claim 1, it is characterised in that positioning institute
The geographical position of K signaling point is stated, and is using compressed sensing algorithm construction spectral function, is searched for K maximum spectral peak, ripple is calculated up to side
To being positioned.
3. a kind of indoor orientation method based on compressed sensing algorithm according to claim 1, it is characterised in that：The step
In rapid S3, the Gauss likelihood function of measurement vector is：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli measurement square that selects
Battle array, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
4. a kind of indoor orientation method based on compressed sensing algorithm according to claim 1, it is characterised in that：The step
X and σ is estimated using MAP estimation alternative manner (MAP) in rapid S32?：
Wherein：I be unit matrix, q=[1 ..., Q].
5. a kind of indoor orientation method based on compressed sensing algorithm according to claim 1, it is characterised in that the nothing
Line mobile device is Bluetooth mobile equipment；The radio network gateway is bluetooth radio network gateway.
6. a kind of indoor orientation method based on compressed sensing algorithm according to claim 5, it is characterised in that the nothing
Line mobile device is connected with radio network gateway by wave point, and the wave point is bluetooth wireless interface.
7. a kind of device of the indoor orientation method based on compressed sensing algorithm, it is characterised in that include：
Signal receiving array module：Area to be targeted is deployed in, is made up of M Beacon Point (beacon)；
Signal receiving module：K wireless mobile apparatus constitute K signaling point, and the K wireless mobile apparatus are connect with the signal
Array direction communication is received, for obtaining array received signal x；
First Geographic mapping module：Using compressed sensing algorithm, computation and measurement vectorFor positioning
State the first geographical position of K signaling point.
Transport module：First geographical position of corresponding signaling point and Mac addresses are passed by the K wireless mobile apparatus
Defeated to radio network gateway, radio network gateway is transmitted to high in the clouds Surveillance center；
Second Geographic mapping module：After K wireless mobile apparatus move to the second geographical position, signal reception is repeated
To transport module, positioning obtains the second geographical position that quantity is K wireless mobile apparatus to module；
Indoor positioning module：For comparing two different geographical position of identical Mac addresses, programme path is simultaneously sent to wireless
Mobile device, carries out indoor positioning.
8. a kind of indoor positioning device based on compressed sensing algorithm according to claim 7, it is characterised in that the first ground
In reason location positioning module, the geographical position of the K signaling point is positioned, is using compressed sensing algorithm construction spectral function, is searched
The maximum spectral peak of K, rope, calculates direction of arrival and is positioned.
9. a kind of indoor positioning device based on compressed sensing algorithm according to claim 7, it is characterised in that：Described
In one Geographic mapping module, the Gauss likelihood function of measurement vector is：
Wherein：σ2For noise variance, α is sparse coefficient vector, It is the gaussian random or bernoulli measurement square that selects
Battle array, Ψ are sparse basis arrays, and Q is the number that spatial domain divides.
10. a kind of indoor positioning device based on compressed sensing algorithm according to claim 7, it is characterised in that：Described
In first Geographic mapping module, x and σ is estimated using MAP alternative manners2：
Wherein：I be unit matrix, q=[1 ..., Q].
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