CN111551180A - Smart phone indoor positioning system and method capable of identifying LOS/NLOS acoustic signals - Google Patents

Smart phone indoor positioning system and method capable of identifying LOS/NLOS acoustic signals Download PDF

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CN111551180A
CN111551180A CN202010441441.0A CN202010441441A CN111551180A CN 111551180 A CN111551180 A CN 111551180A CN 202010441441 A CN202010441441 A CN 202010441441A CN 111551180 A CN111551180 A CN 111551180A
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receiver
smart phone
module
signal
positioning
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CN111551180B (en
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周陬
张国立
段楠
仇洪冰
王玫
宋浠瑜
罗丽燕
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Guilin University of Electronic Technology
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Guilin University of Electronic Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
    • 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

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Abstract

The invention discloses an indoor positioning system and method of a smart phone capable of identifying LOS/NLOS acoustic signals. The system adopts an active positioning scheme, and comprises a smart phone, a receiver and a server which all have network communication capacity; the smart phone is connected with the receiver through the pseudo ultrasonic signal, and the server is communicated with the smart phone and the receiver through a network. The positioning method selects the pseudo-ultrasonic audio signal with the modulation mode of linear frequency modulation Chirp as the positioning signal, improves the anti-jamming capability of the system, and uses a classification algorithm of SVM and DS evidence theory information fusion to identify and discard non-line-of-sight (NLOS) measurement data, thereby constructing a set of more accurate indoor positioning system and method. Because the loudspeaker and the common microphone of the smart phone can send and receive the pseudo ultrasonic positioning signal, the cost of the system is greatly reduced.

Description

Smart phone indoor positioning system and method capable of identifying LOS/NLOS acoustic signals
Technical Field
The invention relates to the field of indoor positioning, in particular to a smart phone indoor positioning system and a smart phone indoor positioning method capable of identifying LOS/NLOS acoustic signals.
Background
With the development of smart phone technology and the popularization of smart phones, people increasingly demand smart phone location-based services. At present, smart phones mainly rely on Global Navigation Satellite System (GNSS) to achieve sub-meter level outdoor positioning accuracy. However, indoor positioning is difficult to be achieved by GNSS due to weak indoor satellite signals.
Currently, indoor positioning technologies based on smart phones are mainly classified into two categories: the first type is an indoor positioning technology based on an external information source, and mainly comprises WiFi, Bluetooth, Ultra Wide Band (UWB) and the like; the second type is an indoor positioning technology based on a natural information source, and the technology only depends on a built-in sensor of a mobile phone to realize the perception of the position of the mobile phone, and mainly comprises inertial navigation, geomagnetic navigation and the like.
WiFi based on smart mobile phones, indoor location technology of bluetooth realizes the location according to Received Signal Strength (RSS), because indoor environment is complicated, receives the interference of other signals very easily, influences positioning accuracy.
The ultra-wideband indoor positioning technology based on the smart phone is high in positioning accuracy. But at present, few smart phones are internally provided with ultra-wideband chips, and ultra-wideband indoor positioning equipment is high in cost and does not have universality.
The inertial navigation technology based on the smart phone only depends on the inertial sensor, and due to the accumulated drift error, the positioning precision is difficult to meet the user requirement.
Disclosure of Invention
The invention aims to solve the problems of high cost and low precision of an indoor positioning technology based on a smart phone, and provides a smart phone indoor positioning system and a smart phone indoor positioning method capable of identifying LOS/NLOS acoustic signals.
Pseudo-ultrasound is audio at frequencies between that of ultrasound and audible sound of the human ear. The pseudo-ultrasound has the advantages of short wavelength, small interference and the like. The invention selects the pseudo-ultrasonic audio signal with the modulation mode of Chirp as the positioning signal, improves the anti-jamming capability of the system, and uses a classification algorithm of SVM and DS evidence theory information fusion to identify and discard non-line-of-sight (NLOS) measurement data, thereby constructing a set of more accurate indoor positioning system. Because the mobile phone loudspeaker and the common microphone can send and receive the pseudo-ultrasonic positioning signal, the cost of the system is greatly reduced.
The technical scheme for realizing the purpose of the invention is as follows:
an indoor positioning system of a smart phone capable of identifying LOS/NLOS acoustic signals adopts an active positioning scheme, the system comprises the smart phone, a receiver and a server which all have network communication capacity, and the smart phone is used as a transmitting end of the system;
the smart phone is connected with the receiver through a pseudo ultrasonic signal, and the server is communicated with the smart phone and the receiver through a network;
the smart phone is used for transmitting a pseudo ultrasonic positioning signal and comprises a Chirp modulation module and an audio output module which are sequentially connected;
the receiver is used for receiving the pseudo-ultrasonic positioning signal and extracting the characteristics, and comprises a WSN synchronization module, a signal acquisition module, a channel gain and delay estimation module and a characteristic extraction module which are connected in sequence;
the server is used for estimating the position coordinates of the smart phone and comprises an SVM-DS classification module and a position resolving module which are sequentially connected.
The Chirp modulation module of the smart phone is used for generating a pseudo ultrasonic Chirp electrical signal;
and the audio output module is used for generating a periodic pseudo-ultrasonic Chirp audio signal.
The WSN synchronization module of the receiver is used for completing clock synchronization among the receivers;
the signal acquisition module is used for acquiring a pseudo ultrasonic positioning signal sent by the smart phone and performing analog-to-digital conversion;
the channel gain and time delay estimation module is used for estimating the relative channel gain and time delay of the received signal and the arrival time of the signal;
and the characteristic extraction module is used for extracting the characteristics of the received signals.
The SVM-DS classification module of the server is used for screening out the signal arrival time of the receiver in the LOS environment;
and the position calculating module is used for estimating the position coordinates of the smart phone.
A positioning method of an indoor positioning system of a smart phone capable of identifying LOS/NLOS acoustic signals comprises the following steps:
(1) the smart phone sends the pseudo ultrasonic electrical signals generated by the Chirp modulation module to the audio output module, and the audio output module generates periodic pseudo ultrasonic Chirp audio signals;
(2) the receiver completes clock synchronization through the WSN synchronization module; each receiver acquires pseudo-ultrasonic positioning signals through a signal acquisition module and sends the pseudo-ultrasonic positioning signals to a channel gain and delay estimation module and a feature extraction module, the feature extraction module extracts feature vectors and arrival time of received signals according to results obtained by the channel gain and delay estimation module, and then the feature vectors and the arrival time are sent to a server through a network;
(3) the server receives data sent by the receiver and sends the received feature vectors to the SVM-DS classification module, and the classification module screens out the signal arrival time of the receiver in the LOS environment; and the position calculating module selects receiver data under an LOS environment according to the result of the SVM-DS classification module to estimate the position coordinate of the smart phone, and then sends the position coordinate to the smart phone through a network.
Further, in the step (1), the Chirp modulation module generates a pseudo-ultrasonic Chirp electrical signal by using a digital-to-analog converter DAC, and the method includes the following steps:
(111) the amplitude of a pseudo ultrasonic Chirp signal transmitted by the smart phone is A, and the initial frequency is f0A termination frequency of f1Signal length of tsThen, the formula of the pseudo ultrasonic Chirp signal is as follows:
Figure BDA0002504148610000041
(112) setting the sampling frequency to be F according to the Nyquist sampling theorem, and sampling s (t) to obtain a discrete array Num;
(113) converting the data in the array Num into data suitable for DAC input;
(114) and the DAC sequentially collects the data in the array Num at a sampling rate F to generate a pseudo-ultrasonic Chirp electric signal.
Further, the audio output module in step (1) is used for generating a periodic pseudo-ultrasonic Chirp audio signal at a transmitting end of a smart phone, and the method comprises the following steps:
(121) with T as period, where T is Ts+tl+tg,tsFor pseudo-ultrasonic Chirp audio signals, tlFor transmission intervals (related to the positioning range), tgIs a guard interval;
(122) the length generated by a Chirp modulation module is tsThe Chirp electrical signal is sent to a sound production module; (123) t is tl+tgThen, a Chirp electrical signal generated by the Chirp modulation module is sent to the sound production module;
(124) and (5) repeating the steps (122) - (123) until the positioning is finished.
Further, the clock synchronization of the WSN synchronization module in step (2) is a clock synchronization method based on WSN synchronization designed with reference to a TPSN clock synchronization protocol. The purpose is to realize clock synchronization among receivers, and the clock synchronization method comprises the following steps of taking the clock of a first receiver as a reference clock:
(211) the first receiver sends a synchronization data recording time t to the receiver i1iAnd will t1iSending the information to a receiver i, i is 2,3, … N;
(212) after the receiver i receives the synchronous data, the time t is recorded2i,i＝2,3,…N；
(213) Receiver i receives t1iDelaying the transmission of synchronization data to the first receiver by a period of time and recording the time t3i
(214) First is connected toThe receiver receives the synchronous data recording time t4iAnd will t4iSending the data to a receiver i;
(215) receiver i receives ti4And calculating the path delay for transmitting the synchronous data
Figure BDA0002504148610000051
(216) Receiver i calculates a clock offset value from the first receiver
Figure BDA0002504148610000052
(217) Receiver i depends on the offset value
Figure BDA0002504148610000053
Updating the clock;
(218) the clock synchronization of the receivers 2,3, …, N is achieved in turn according to the above steps.
Further, the signal acquisition module in step (2) acquires a pseudo-ultrasonic positioning signal sent by a smart phone, and the steps include:
(221) the signal acquisition module converts an acoustic signal into an electric signal by using a circuit module built by an MEMS microphone;
(222) sending the electric signal to an audio amplification module;
(223) sending the amplified electric signal to a band-pass filter;
(224) sending the filtered signals to an ADC module, and sampling the signals according to the Nyquist sampling theorem;
(225) the collected signals are sent to a FIFO buffer with the length of M.
Further, the channel gain and delay estimation module in step (2) is configured to estimate a relative path gain, a relative path delay, and an arrival time of the received signal, and includes the steps of:
(231) receiver i reads data x in the FIFO buffernAnd calculating the energy sum E of the signals in the FIFO bufferi,
Figure BDA0002504148610000061
(232) When E isiIs greater than the threshold value and EiThe value of (A) is less variable in time, when the received signal in the FIFO buffer is xi(t) recording the time t at that timei0；
(233) Calculating xi(t) cross-correlation value with reference signal s (t)
Figure BDA0002504148610000062
(234) For cross correlation value
Figure BDA0002504148610000063
The amplitude of the signal is normalized and the extreme value of the peak envelope is extracted to obtain
Figure BDA0002504148610000064
Wherein the gain of the jth extreme is αijWith a time delay of
Figure BDA0002504148610000065
Figure BDA0002504148610000066
Is noise;
(235) calculating a first arrival path of a receiver i
Figure BDA0002504148610000067
Figure BDA0002504148610000068
Wherein
Figure BDA0002504148610000069
Is composed of
Figure BDA00025041486100000610
Maximum value of (1);
(236) calculating the time of arrival of the signal at receiver i
Figure BDA00025041486100000611
(237) Calculating relative path gain estimated value of receiver i by using first arrival path as starting time of received signal
Figure BDA00025041486100000612
And relative path delay estimate
Figure BDA00025041486100000613
Figure BDA00025041486100000614
Further, the feature extraction module in step (2) is configured to extract nine acoustic features of the signal received by the receiver i, and includes the steps of:
(241) based on relative path gain estimates
Figure BDA00025041486100000615
And relative path delay estimate
Figure BDA00025041486100000616
Extracting the average excess time delay tau of the received signalmedAnd root mean square delay spread τrms；
Figure BDA0002504148610000071
(242) Based on relative path delay estimates
Figure BDA0002504148610000072
Calculating kurtosis k and skewness s
Figure BDA0002504148610000073
Wherein r is a pair
Figure BDA0002504148610000074
Result of one-dimensional interpolation, μrAnd σrIs the mean and standard deviation of r, E [. cndot.)]Is a mathematical expectation operator;
(243) based on relative path gain estimates
Figure BDA0002504148610000075
Calculation of Rice K factor KR
Figure BDA0002504148610000076
Wherein k isdIs equal to
Figure BDA0002504148610000077
Sigma is
Figure BDA0002504148610000078
Standard deviation of (d);
(244) based on relative path gain estimates
Figure BDA0002504148610000079
In the interval
Figure BDA00025041486100000710
Number of times fλCalculating the average frequency g of the relative channel gain frequencymAnd root mean square frequency grms,λ＝1,2,3,…,n
Figure BDA00025041486100000711
(245) According to f ═ fλN calculating kurtosis k of frequency distributionfDeviation of sum sf
Figure BDA00025041486100000712
Wherein mufAnd σfMean and standard deviation of f;
(246) the feature vector x of the receiver ii＝[τmed,τrms,k,s,KR,gm,grms,kf,sf]And the arrival time t of the signaliAnd sending the data to a server.
Further, the SVM-DS classification module in step (3) is configured to filter out the signal arrival time of the receiver in LOS environment, and includes the steps of:
(311) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM1 with a kernel function rbf to obtain BPA 1;
(312) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM2 with a kernel function of poly to obtain BPA 2;
(313) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM3 with a kernel function of linear to wait until BPA 3;
(314) performing DS fusion on BPA1, BPA2 and BPA3 and outputting a judgment result l of a receiver i, wherein l is 1 and is an LOS environment, and l is-1 and is an NLOS environment;
(315) reordering the receivers in the LOS environment to obtain receivers lj, j being 1,2,3, …, m, wherein m is less than or equal to N;
(316) according to the above process, the arrival time t of the receiver lj in the LOS environment can be obtainedlj,j＝1,2,3,…,m,m≤N。
Further, the position calculating module in step (3) is used for estimating the position coordinates of the transmitting end of the smart phone, and the steps include:
(321) with the arrival time t of receiver l1l1For reference, the arrival time difference Δ t is calculatedlj＝tlj-tl1,j＝2,3…m；
(322) According to the time difference of arrival DeltatljAnd the position coordinates of the corresponding receiver lj, and the position coordinates of the smart phone are estimated by using a TDOA hyperbolic positioning algorithm;
the TDOA hyperbolic positioning algorithm comprises the following formula:
Figure BDA0002504148610000081
j＝2,3,…,m,r0is the Euclidean distance, r, between the smartphone and receiver l1jIs the Euclidean distance, Deltar, between the smart phone and the external sound source ijFor the difference in distance between the smartphone to receiver l1 and to receiver lj, (x, y, z) is the smartphone position coordinate, (x)0,y0,z0) Is the position coordinate of receiver l1, (x)j,yj,zj) As position coordinates of the receiver lj, Δ tljIs the difference in arrival time of receiver lj and receiver l1, c is the speed of sound propagation in air;
(323) and returning the estimated position coordinates of the smart phone to the smart phone, so that accurate positioning can be realized.
The invention has the beneficial effects that: the intelligent mobile phone indoor positioning system capable of identifying LOS/NLOS acoustic signals can meet positioning requirements in small and medium indoor scenes. The invention adopts the pseudo-ultrasonic Chirp signal as the positioning signal, and has high positioning precision and low cost. The system adopts a classification algorithm combining SVM and DS evidence theory to identify and discard NLOS data, and only LOS data is adopted for positioning, so that the positioning performance of the system is improved.
Drawings
FIG. 1 is a block diagram of an indoor positioning system of a smart phone capable of recognizing LOS/NLOS acoustic signals according to the present invention;
FIG. 2 is a block diagram of the internal modules of the smart phone of the system of the present invention;
FIG. 3 is a timing diagram of a smartphone transmitting a pseudo-ultrasonic locating signal in the system of the present invention;
FIG. 4 is a block diagram of the internal modules of the receiver in the system of the present invention;
FIG. 5 is a schematic diagram of the synchronization module clock synchronization of the WSN receiver in the system of the present invention;
FIG. 6 is a block diagram of the internal modules of the server in the system of the present invention;
FIG. 7 is a diagram illustrating the classification steps of the SVM-DS classification module of the server in the system of the present invention.
In the figure, 1 smart phone, 11 Chirp modulation module and 12 audio output module
The system comprises a receiver 2, a WSN synchronization module 21, a signal acquisition module 22, a channel gain and time delay estimation module 23, a feature extraction module 24, a first receiver 2-1, a second receiver 2-2, a third receiver 2-3 and a fourth receiver 2-4;
3 server, 31SVM-DS classification module, 32 position resolving module.
Detailed Description
The present invention will be further described with reference to the following drawings and examples, but the present invention is not limited thereto.
Examples
Referring to fig. 1, an intelligent mobile phone indoor positioning system capable of recognizing LOS/NLOS acoustic signals adopts an active positioning scheme, the system includes an intelligent mobile phone 1, a receiver 2 and a server 3, all of which have network communication capability, and the intelligent mobile phone 1 is used as a transmitting terminal of the system;
the smart phone 1 is connected with the receiver 2 through a pseudo ultrasonic signal, and the server 3 is communicated with the smart phone 1 and the receiver 2 through a network.
Referring to fig. 2, the smartphone 1 is configured to transmit a pseudo ultrasonic positioning signal, and includes a Chirp modulation module 11 and an audio output module 12 that are connected in sequence;
the Chirp modulation module 11 of the smart phone 1 is used for generating a pseudo ultrasonic Chirp electrical signal;
and the audio output module 12 is used for generating a periodic pseudo-ultrasonic Chirp audio signal.
Referring to fig. 4, the receiver 2 is configured to receive a pseudo-ultrasonic positioning signal and perform feature extraction, and includes a WSN synchronization module 21, a signal acquisition module 22, a channel gain and delay estimation module 23, and a feature extraction module 24, which are connected in sequence;
the WSN synchronization module 21 of the receiver 2 is configured to complete clock synchronization between the receivers;
the signal acquisition module 22 is used for acquiring a pseudo ultrasonic positioning signal sent by the smart phone 1 and performing analog-to-digital conversion;
a channel gain and delay estimation module 23, configured to estimate a relative channel gain and delay of the received signal and an arrival time of the signal;
a feature extraction module 24, configured to perform feature extraction on the received signal;
the receiver 2 of the present embodiment has four receivers, including a first receiver 2-1, a second receiver 2-2, a third receiver 2-3, and a fourth receiver 2-4.
Referring to fig. 6, the server 3 is configured to estimate the position coordinates of the smartphone 1, and includes an SVM-DS classification module 31 and a position calculation module 32, which are connected in sequence;
the SVM-DS classification module 31 of the server 3 is configured to filter out the signal arrival time of the receiver in the LOS environment;
and the position calculating module 32 is used for estimating the position coordinates of the smart phone 11.
Referring to fig. 1-7, a positioning method of a smart phone indoor positioning system capable of recognizing LOS/NLOS acoustic signals includes the following steps:
(1) the smart phone 1 sends the pseudo ultrasonic electrical signals generated by the Chirp modulation module 11 to the audio output module 12, and the audio output module 12 generates periodic pseudo ultrasonic Chirp audio signals;
(2) the receiver 2 completes clock synchronization through the WSN synchronization module 21; the four receivers 2-1, 2-2, 2-3 and 2-4 of the receiver 2 acquire pseudo ultrasonic positioning signals through a signal acquisition module 22 and send the pseudo ultrasonic positioning signals to a channel gain and delay estimation module 23 and a feature extraction module 24, the feature extraction module 24 extracts feature vectors and arrival time of received signals according to results obtained by the channel gain and delay estimation module 23 and then sends the feature vectors and the arrival time to the server 3 through a network;
(3) the server 3 receives the data sent by the receiver 2 and sends the received feature vectors to the SVM-DS classification module 31, and the classification module 31 screens out the signal arrival time of the receiver in an LOS environment; the position calculation module 32 selects data of the receiver 2 in the LOS environment to estimate the position coordinates of the smart phone 1 according to the result of the SVM-DS classification module 31, and then sends the position coordinates to the smart phone 1 through a network.
Referring to fig. 1 to 3, the Chirp modulation module 11 generates a pseudo-ultrasonic Chirp electrical signal by using a digital-to-analog converter DAC, and includes the steps of:
(111) the amplitude of a pseudo ultrasonic Chirp signal emitted by the smart phone 1 is A-1, and the initial frequency is f016kHz, termination frequency f120kHz, signal length tsThe formula of the pseudo-ultrasonic Chirp signal is 02 s:
Figure BDA0002504148610000121
(112) setting the sampling frequency to be F-44.1 kHz according to the Nyquist sampling theorem, and sampling s (t) to obtain a discrete array Num;
(113) converting the data in the array Num into data suitable for DAC input;
(114) and the DAC sequentially acquires the data in the array Num at a sampling rate F of 44.1kHz to generate a pseudo-ultrasonic Chirp electric signal.
Referring to fig. 2-3, the audio output module 12 is used for the transmitting end of the smart phone 1 to generate a periodic pseudo-ultrasonic Chirp audio signal, and includes the following steps:
(121) with T as period, where T is Ts+tl+tg,ts0.2s is a pseudo ultrasonic Chirp positioning signal, tl0.05s is the transmission interval (related to the positioning range), tg0.15s is a guard interval;
(122) sending a Chirp electrical signal with the length of 0.2s generated by a Chirp modulation module into a sound production module;
(123) after 0.2s, sending a Chirp electrical signal generated by the Chirp modulation module into a sound production module;
(124) and (5) repeating the steps (122) - (1233) until the positioning is finished.
Referring to fig. 1 and fig. 4 to 5, the clock synchronization of the WSN synchronization module 21 refers to the TPSN clock synchronization protocol, taking four receivers as an example, the clock of the first receiver 2-1 is the reference clock, and the clock synchronization of the second receiver 2-2 is taken as an example, and the steps include:
(211) the first receiver 2-1 sends a synchronized data recording time t to the second receiver 2-212And will t12To a second receiver 2-2;
(212) the second receiver 2-2 is connected withAfter the synchronous data is received, the time t is recorded22；
(213) The second receiver 2-2 receives t12Delaying the transmission of synchronization data to the first receiver 2-1 by a period of time and recording the time t32
(214) The first receiver 2-1 receives the synchronous data recording time t42And will t42To a second receiver 2-2;
(215) the second receiver 2-2 receives t42And calculating the path delay for transmitting the synchronous data
Figure BDA0002504148610000131
(216) The second receiver 2-2 calculates a clock offset value from the first receiver 2-1
Figure BDA0002504148610000132
(217) The second receiver 2-2 is based on the offset value
Figure BDA0002504148610000134
And updating the clock.
(218) The clock synchronization of the receivers 2-3,2-4 is realized in sequence according to the above steps.
Referring to fig. 4, the signal acquisition module 22 is configured to acquire a pseudo-ultrasonic Chirp positioning signal sent by the transmitting end of the smart phone 1, and includes the following steps:
(221) the signal acquisition module 22 converts the acoustic signal into an electrical signal by using a circuit module built by the MEMS microphone;
(222) sending the electric signal to an audio amplification module;
(223) sending the amplified electric signal to a band-pass filter;
(224) sending the filtered signals to an ADC module, and sampling the signals according to the Nyquist sampling theorem;
(225) the collected signals are sent to a FIFO buffer with the length of M.
Referring to fig. 4, the channel gain and delay estimation module 23 is configured to estimate a relative path gain, a relative path delay and an arrival time of a received signal, and includes the steps of:
(231) receiver i reads data x in the FIFO buffernAnd calculating the energy sum E of the signals in the FIFO bufferi,
Figure BDA0002504148610000133
(232) When E isiIs greater than the threshold value and EiThe value of (A) is less variable in time, when the received signal in the FIFO buffer is xi(t) recording the time t at that timei0；
(233) Calculating xi(t) cross-correlation value with reference signal s (t)
Figure BDA0002504148610000141
(234) For cross correlation value
Figure BDA0002504148610000142
The amplitude of the signal is normalized and the extreme value of the peak envelope is extracted to obtain
Figure BDA0002504148610000143
Wherein the gain of the jth extreme is αjWith a time delay of
Figure BDA0002504148610000144
Figure BDA0002504148610000145
Is noise;
(235) calculating a first arrival path of a receiver i
Figure BDA0002504148610000146
Figure BDA0002504148610000147
Wherein
Figure BDA0002504148610000148
Is composed of
Figure BDA0002504148610000149
Maximum value of (1);
(236) calculating the time of arrival of the signal of receiver i
Figure BDA00025041486100001410
(237) Calculating relative path gain estimated value of receiver i by using first arrival path as starting time of received signal
Figure BDA00025041486100001411
And relative path delay estimate
Figure BDA00025041486100001412
Figure BDA00025041486100001413
Referring to fig. 4, the feature extraction module 24 is configured to extract nine acoustic features of a signal received by the receiver i, and includes the steps of:
(241) based on relative path gain estimates
Figure BDA00025041486100001414
And relative path delay estimate
Figure BDA00025041486100001415
Extracting the average excess time delay tau of the received signalmedAnd root mean square delay spread τrms；
Figure BDA00025041486100001416
(242) Based on relative path delay estimates
Figure BDA00025041486100001417
Calculating kurtosis k and skewness s
Figure BDA00025041486100001418
Wherein r is a pair
Figure BDA0002504148610000151
Result of one-dimensional interpolation, μrAnd σrIs the mean and standard deviation of r, E [. cndot.)]Is a mathematical expectation operator;
(243) based on relative path gain estimates
Figure BDA0002504148610000152
Calculation of Rice K factor KR
Figure BDA0002504148610000153
Wherein k isdIs equal to
Figure BDA0002504148610000154
Sigma is
Figure BDA0002504148610000155
Standard deviation of (d);
(244) based on relative path gain estimates
Figure BDA0002504148610000156
In the interval
Figure BDA0002504148610000157
Number of times fλCalculating the average frequency g of the relative channel gain frequencymAnd root mean square frequency grms,λ＝1,2,3,…,n
Figure BDA0002504148610000158
(245) According to f ═ fλN calculating the kurtosis k of the frequency distributionfDeviation from harmonyDegree sf
Figure BDA0002504148610000159
Wherein mufAnd σfMean and standard deviation of f;
(246) the feature vector x of the receiver ii＝[τmed,τrms,k,s,KR,gm,grms,kf,sf]And the arrival time t of the signaliAnd sending the data to a server.
Referring to fig. 1, 6-7, the SVM-DS classification module 31 is used to filter out the signal arrival times of the receivers in LOS environment (for example, four receivers, the third receiver in NLOS environment), and includes the following steps:
(311) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM1 with a kernel function rbf to obtain BPA1, wherein i is 1,2,3 and 4;
(312) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM2 with a kernel function of poly to obtain BPA2, wherein i is 1,2,3 and 4;
(313) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM3 with a kernel function of linear to wait until BPA3, i is 1,2,3 and 4;
(314) performing DS fusion on BPA1, BPA2 and BPA3 and outputting a judgment result l of a receiver i (l is 1 in LOS environment and l is-1 in NLOS environment);
(315) reordering the receivers in the LOS environment to obtain receivers lj, j being 1,2, 3;
(316) according to the above process, the arrival time t of the receiver lj in the LOS environment can be obtainedljJ is 1,2,3 wherein tl1＝t1，tl2＝t2,tl3＝t4。
Referring to fig. 1 and 6, the position calculating module 32 is configured to estimate the position coordinates of the transmitting end of the smartphone 1, and includes the following steps:
(321) by the arrival time tl1For reference, the arrival time difference Δ t is calculatedlj＝tlj-tl1,j＝2,3,4；
(322) According to the time difference of arrival DeltatljAnd the position coordinates of the corresponding receiver lj, and the position coordinates of the smartphone 1 are estimated by using a TDOA hyperbolic positioning algorithm;
the TDOA hyperbolic positioning algorithm comprises the following formula:
Figure BDA0002504148610000161
j＝2,3,…,4,r0is the Euclidean distance, r, between the smartphone 1 and the receiver l1jIs the Euclidean distance, Deltar, between the smartphone 1 and the external sound source ijFor the distance difference between smartphone 1 to receiver l1 and to receiver lj, (x, y, z) is the smartphone 1 position coordinate, (x0,y0,z0) Is the position coordinate of receiver l1, (x)j,yj,zj) As position coordinates of the receiver lj, Δ tljIs the difference in arrival time of receiver lj and receiver l1, c is the speed of sound propagation in air;
(323) and returning the estimated position coordinates of the smart phone 1 to the smart phone 1, so that accurate positioning can be realized.
The positioning method of the indoor positioning system adopts the technical scheme that the intelligent mobile phone 1 actively sounds, so that the real-time performance is good, the development cost is low, and the universality of the system is improved.

Claims (10)

1. A smart phone indoor positioning system capable of identifying LOS/NLOS acoustic signals comprises a smart phone, a receiver and a server, wherein the smart phone has network communication capacity and is used as a transmitting end of the system;
the method is characterized in that: the smart phone is connected with the receiver through a pseudo ultrasonic signal, and the server is communicated with the smart phone and the receiver through a network;
the smart phone is used for transmitting a pseudo ultrasonic positioning signal and comprises a Chirp modulation module and an audio output module which are sequentially connected;
the receiver is used for receiving the pseudo-ultrasonic positioning signal and extracting the characteristics, and comprises a WSN synchronization module, a signal acquisition module, a channel gain and delay estimation module and a characteristic extraction module which are connected in sequence;
the server is used for estimating the position coordinates of the smart phone and comprises an SVM-DS classification module and a position resolving module which are sequentially connected.
2. The recognizable LOS/NLOS acoustic signal smart phone indoor positioning system of claim 1, wherein:
the Chirp modulation module of the smart phone is used for generating a pseudo ultrasonic Chirp electrical signal;
the audio output module is used for generating a periodic pseudo-ultrasonic Chirp audio signal;
the WSN synchronization module of the receiver is used for completing clock synchronization among the receivers;
the signal acquisition module is used for acquiring a pseudo ultrasonic positioning signal sent by the smart phone and performing analog-to-digital conversion;
the channel gain and time delay estimation module is used for estimating the relative channel gain and time delay of the received signal and the arrival time of the signal;
the characteristic extraction module is used for extracting the characteristics of the received signals;
the SVM-DS classification module of the server is used for screening out the signal arrival time of the receiver in the LOS environment;
and the position calculating module is used for estimating the position coordinates of the smart phone.
3. The positioning method of the smart phone indoor positioning system according to any one of claims 1-2, wherein the positioning method comprises the following steps:
(1) the smart phone sends the pseudo ultrasonic electrical signals generated by the Chirp modulation module to the audio output module, and the audio output module generates periodic pseudo ultrasonic Chirp audio signals;
(2) the receiver completes clock synchronization through the WSN synchronization module; each receiver acquires pseudo-ultrasonic positioning signals through a signal acquisition module and sends the pseudo-ultrasonic positioning signals to a channel gain and delay estimation module and a feature extraction module, the feature extraction module extracts feature vectors and arrival time of received signals according to results obtained by the channel gain and delay estimation module, and then the feature vectors and the arrival time are sent to a server through a network;
(3) the server receives data sent by the receiver and sends the received feature vectors to the SVM-DS classification module, and the classification module screens out the signal arrival time of the receiver in the LOS environment; and the position calculating module selects receiver data under an LOS environment according to the result of the SVM-DS classification module to estimate the position coordinate of the smart phone, and then sends the position coordinate to the smart phone through a network.
4. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the Chirp modulation module utilizes a digital-to-analog converter (DAC) to generate a pseudo-ultrasonic Chirp positioning signal, and the method comprises the following steps of:
(111) the amplitude of a pseudo ultrasonic Chirp signal transmitted by the smart phone is A, and the initial frequency is f0A termination frequency of f1Signal length of tsThen, the formula of the pseudo ultrasonic Chirp signal is as follows:
Figure FDA0002504148600000021
(112) setting the sampling frequency to be F according to the Nyquist sampling theorem, and sampling s (t) to obtain a discrete array Num;
(113) converting the data in the array Num into data suitable for DAC input;
(114) and the DAC sequentially collects the data in the array Num at a sampling rate F to generate a pseudo-ultrasonic Chirp electric signal.
5. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the audio output module in the step (1) is used for generating a periodic pseudo-ultrasonic Chirp positioning signal at a transmitting end of a smart phone, and the audio output module comprises the following steps:
(121) with T as period, where T is Ts+tl+tg,tsFor pseudo-ultrasonic Chirp localization signals, tlFor transmission intervals (related to the positioning range), tgIs a guard interval;
(122) the length generated by a Chirp modulation module is tsThe Chirp electrical signal is sent to a sound production module;
(123)tl+tgthen, a Chirp electrical signal generated by the Chirp modulation module is sent to the sound production module;
(124) and (5) repeating the steps (122) - (123) until the positioning is finished.
6. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the clock synchronization method of the WSN synchronization module in step (2) takes the clock of the first receiver as a reference clock, and the clock synchronization method includes the steps of:
(211) the first receiver sends a synchronization data recording time t to the receiver i1iAnd will t1iSending the information to a receiver i, i is 2,3, … N;
(212) after the receiver i receives the synchronous data, the time t is recorded2i,i＝2,3,…N；
(213) Receiver i receives t1iDelaying the transmission of synchronization data to the first receiver by a period of time and recording the time t3i
(214) The first receiver receives the synchronous data recording time t4iAnd will t4iSending the data to a receiver i;
(215) receiver i receives ti4And calculating the path delay for transmitting the synchronous data
Figure FDA0002504148600000031
(216) Receiver i calculates a clock offset value from the first receiver
Figure FDA0002504148600000032
(217) Receiver i depends on the offset value
Figure FDA0002504148600000033
Updating the clock;
(218) the clock synchronization of the receivers 2,3, …, N is achieved in turn according to the above steps.
7. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the channel gain and delay estimation module in step (2) is configured to estimate a relative path gain, a relative path delay, and an arrival time of a received signal, and includes:
(231) receiver i reads data x in the FIFO buffernAnd calculating the energy sum E of the signals in the FIFO bufferi,
Figure FDA0002504148600000041
(232) When E isiIs greater than the threshold value and EiThe value of (A) is less variable in time, when the received signal in the FIFO buffer is xi(t) recording the time t at that timei0；
(233) Calculating xi(t) cross-correlation value with reference signal s (t)
Figure FDA0002504148600000042
(234) For cross correlation value
Figure FDA0002504148600000043
The amplitude of the signal is normalized and the extreme value of the peak envelope is extracted to obtain
Figure FDA0002504148600000044
Wherein the gain of the jth extreme is αjWith a time delay of
Figure FDA0002504148600000045
Figure FDA0002504148600000046
Is noise;
(235) calculating a first arrival path of a receiver i
Figure FDA0002504148600000047
Figure FDA0002504148600000048
Wherein
Figure FDA0002504148600000049
Is composed of
Figure FDA00025041486000000410
Maximum value of (1);
(236) calculating the time of arrival of the signal of receiver i
Figure FDA00025041486000000411
(237) Calculating relative path gain estimated value of receiver i by using first arrival path as starting time of received signal
Figure FDA00025041486000000412
And relative path delay estimate
Figure FDA00025041486000000413
Figure FDA00025041486000000414
8. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the feature extraction module in step (2) is configured to extract nine acoustic features of a signal received by a receiver i, and the steps include:
(241) based on relative path gain estimates
Figure FDA00025041486000000415
And relative path delay estimate
Figure FDA00025041486000000416
Extracting the average excess time delay tau of the received signalmedAnd root mean square delay spread τrms；
Figure FDA0002504148600000051
(242) Based on relative path delay estimates
Figure FDA0002504148600000052
Calculating kurtosis k and skewness s
Figure FDA0002504148600000053
Wherein r is a pair
Figure FDA0002504148600000054
Result of one-dimensional interpolation, μrAnd σrIs the mean and standard deviation of r, E [. cndot.)]Is a mathematical expectation operator;
(243) based on relative path gain estimates
Figure FDA0002504148600000055
Calculation of Rice K factor KR
Figure FDA0002504148600000056
Wherein k isdIs equal to
Figure FDA0002504148600000057
Sigma is
Figure FDA0002504148600000058
Standard deviation of (d);
(244) based on relative path gain estimates
Figure FDA0002504148600000059
In the interval
Figure FDA00025041486000000510
Number of times fλCalculating the average frequency g of the relative channel gain frequencymAnd root mean square frequency grms,λ＝1,2,3,…,n
Figure FDA00025041486000000511
(245) According to f ═ fλN calculating the kurtosis k of the frequency distributionfDeviation of sum sf
Figure FDA00025041486000000512
Wherein mufAnd σfMean and standard deviation of f;
(246) the feature vector x of the receiver ii＝[τmed,τrms,k,s,KR,gm,grms,kf,sf]And the arrival time t of the signaliAnd sending the data to a server.
9. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the SVM-DS classification module in the step (3) is used for screening out the signal arrival time of the receiver in the LOS environment, and the steps comprise:
(311) eigenvectors x to be transmitted by receiver iiSending the data to a support vector machine SVM1 with a kernel function rbf to wait until BPA1 is reached;
(312) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM2 with a kernel function of poly to obtain BPA 2;
(313) eigenvectors x to be transmitted by receiver iiSending the data into a support vector machine SVM3 with a kernel function of linear to wait until BPA 3;
(314) performing DS fusion on BPA1, BPA2 and BPA3 and outputting a judgment result l of a receiver i, wherein l is 1 and is an LOS environment, and l is-1 and is an NLOS environment;
(315) reordering the receivers in the LOS environment to obtain receivers lj, j being 1,2,3, …, m, wherein m is less than or equal to N;
(316) according to the above process, the arrival time t of the receiver lj in the LOS environment can be obtainedlj,j＝1,2,3,…,m,m≤n。
10. The positioning method of the smart phone indoor positioning system according to claim 3, wherein:
the position calculating module is used for estimating the position coordinate of the transmitting end of the smart phone, and the method comprises the following steps:
(321) by the arrival time tl1For reference, the arrival time difference Δ t is calculatedlj＝tlj-tl1,j＝2,3…m；
(322) According to the time difference of arrival DeltatljAnd the position coordinates of the corresponding receiver lj, and the position coordinates of the smart phone are estimated by using a TDOA hyperbolic positioning algorithm;
the TDOA hyperbolic positioning algorithm comprises the following formula:
Figure FDA0002504148600000061
j＝2,3,…,m,r0is the Euclidean distance, r, between the smartphone and receiver l1jIs the Euclidean distance, Deltar, between the smart phone and the external sound source ijFor the difference in distance between the smartphone to receiver l1 and to receiver lj, (x, y, z) is the smartphone position coordinate, (x)0,y0,z0) Is the position coordinate of receiver l1, (x)j,yj,zj) As position coordinates of the receiver lj, Δ tljIs the difference in arrival time of receiver lj and receiver l1, c is the speed of sound propagation in air;
(323) and returning the estimated position coordinates of the smart phone to the smart phone, so that accurate positioning can be realized.
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