An Indoor Positioning Method and System for the Fusion of B1C Signals and UWB Signals

Abstract

The present invention relates to an indoor positioning method and system for fusing B1C signals and UWB signals. The method includes: searching through a B1C signal receiver, and capturing the B1C signal using an improved B1C signal adaptive acquisition algorithm; tracking the B1C signal through a carrier and code tracking loop algorithm, and demodulating to obtain B1C positioning data; when an indoor device sends a positioning request, obtaining the UWB signal through a UWB base station and obtaining the B1C positioning data through an antenna; using the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain UWB indoor positioning data, and substituting the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data. By combining the high penetration and high positioning accuracy of the UWB technology with the B1C signal, the application of the Beidou positioning system in the indoor field is expanded.

Description

Technical Field

[0001] The present invention relates to the field of signal processing, and in particular to an indoor positioning method and system for fusing B1C signals and UWB signals. Background Art

[0002] The Beidou B1C signal is a civilian signal provided by the Chinese Beidou satellite navigation system and belongs to the B1 frequency band. The B1C signal is transmitted by Beidou satellites to provide services such as positioning, navigation, and time confirmation to the public. User receivers determine their positions by measuring the time difference of arrival of the signals and using triangulation. In an outdoor open environment, the B1C signal can well implement the positioning and navigation functions. However, in a complex indoor environment, due to multipath effects and obstruction by obstacles, the B1C signal undergoes phenomena such as refraction and diffraction, and its positioning signal may be severely attenuated, resulting in a decrease in positioning accuracy or positioning failure. UWB is a wireless communication technology that uses non-sinusoidal narrowband pulse signals for data transmission, with a relatively wide bandwidth and low power spectral density, and is commonly used in indoor positioning systems to meet high-precision positioning requirements. By combining the Beidou B1C signal with UWB technology, the application of the B1C signal in the indoor field can be extended.

[0003] Conventional algorithms for fusing Beidou B1C signals and UWB signals achieve seamless positioning by combining the outdoor positioning ability of Beidou B1C signals and the indoor positioning accuracy of UWB signals. That is, in an outdoor environment or an environment with good Beidou signals, the system mainly relies on Beidou B1C signals for positioning; in an indoor environment or an environment where Beidou signals are blocked, it switches to UWB signals for positioning, thereby improving the continuity and accuracy of positioning.

[0004] However, the algorithm for fusing B1C signals and UWB signals does not truly achieve indoor positioning of B1C signals, but only switches between B1C signals and UWB signals. Summary of the Invention

[0005] The present invention provides an indoor positioning method and system for fusing B1C signals and UWB signals, which utilizes an improved B1C signal adaptive acquisition algorithm and an AEKF fusion algorithm for fusing B1C signals and UWB signals, combines the high penetrability and high positioning accuracy of UWB technology with B1C signals, and expands the application of the Beidou positioning system in the indoor field.

[0006] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0007] In a first aspect, there is provided an indoor positioning method for fusing B1C signals and UWB signals, including:

[0008] Search through the B1C signal receiver and capture the B1C signal using an improved B1C signal adaptive acquisition algorithm;

[0009] Track the B1C signal through the carrier and code tracking loop algorithm and demodulate to obtain B1C positioning data;

[0010] When the indoor device sends a positioning request, obtain the UWB signal through the UWB base station and obtain the B1C positioning data through the antenna;

[0011] Utilize the B1C positioning data in the Time Difference of Arrival / Symmetric Double-Sided Two-Way Ranging TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the Adaptive Extended Kalman Filter AEKF fusion algorithm to calculate and obtain the final positioning data.

[0012] Furthermore, the improved B1C signal adaptive acquisition algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm.

[0013] Furthermore, searching through the B1C signal receiver and capturing the B1C signal using an improved B1C signal adaptive acquisition algorithm includes:

[0014] Determine the frequency range of the B1C signal to be captured;

[0015] Use the B1C signal receiver to perform Doppler frequency shift search within the frequency range using a matched filter;

[0016] Multiply the signals to be captured during the search process by the quadrature data carrier respectively, perform correlation operations with the locally generated pseudo-code, and then perform square sum processing to obtain the processed signals;

[0017] According to the processed signals, determine whether the B1C signal is successfully captured through the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm;

[0018] If it is determined that the capture is successful, determine that the signal to be captured is the B1C signal;

[0019] If it is determined that the capture is unsuccessful, determine that the signal to be captured is not the B1C signal.

[0020] Furthermore, according to the processed signals, determining whether the B1C signal is successfully captured through the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm includes:

[0021] Obtain the noise power estimate by calculating the variance of N consecutive correlation values of the processed signals, obtain the noise variance and the noise standard deviation, and calculate and obtain the threshold estimate value;

[0022] Perform non-correlated integration processing on the processed signal to obtain a threshold decision value;

[0023] Input the threshold estimate value and the threshold decision value into the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal capture is successful.

[0024] Furthermore, utilize the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate and obtain the final positioning data, including:

[0025] Set the initial state estimate And the initial covariance matrix , and predict the state and covariance through the state and observation equations. The expressions of the state and observation equations are:

[0026] ;

[0027] Among them, Is the state quantity at time k; Is the measurement quantity; And Are both Gaussian white noises with a mean of zero and variances of R and Q respectively; , Are respectively , Control vectors of; Represents the state transition process; Represents the observation process;

[0028] Predict and estimate the next state to obtain the expression:

[0029] ;

[0030] Among them, Represents the system prior state prediction vector at time k; Represents the covariance prediction matrix at time k; Is the Jacobian matrix of the state transition model at time k - 1; Is the covariance matrix of the process noise;

[0031] Obtain UWB indoor positioning data through the TDOA / SDS-TWR positioning algorithm, and obtain the preset signal arrival time T, signal arrival strength P, target position s, and target speed v as state quantities for modeling to obtain the expression:

[0032] ;

[0033] Among them, is the sampling time of the TOA algorithm, and a state quantity is generated every sampling time interval; is the state quantity at time k-1;

[0034] Set the B1C positioning data to , where i is the base station number; the pseudorange obtained by the TDOA / SDS-TWR positioning algorithm and the predicted distance The difference is used as the measurement value to update the matrix , ;

[0035] Calculate the Kalman gain, and update the state estimate expression by measuring the update of the next state, which is:

[0036] ;

[0037] Among them, is the innovation matrix; is the innovation covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimate vector at time k, which is the latest optimal estimate; represents the covariance update matrix at time k;

[0038] Update the covariance matrix of the process noise and the covariance matrix of the observation noise. The calculation formulas are:

[0039] ;

[0040] ;

[0041] Among them, is the preset adaptive adjustment coefficient;

[0042] After adaptively adjusting the covariance matrix of the process noise and the covariance matrix of the observation noise, continue the iteration to obtain the final state estimate value and get the final positioning data.

[0043] In the second aspect, an indoor positioning system that fuses B1C signals and UWB signals is provided, including:

[0044] The B1C signal receiver and B1C signal transponder in the outdoor part, the antenna and UWB base station in the indoor part; the B1C signal transponder is connected to the antenna through a coaxial cable;

[0045] The B1C signal acquisition module is used to search through a B1C signal receiver and capture the B1C signal using an improved B1C signal adaptive acquisition algorithm.

[0046] The B1C positioning data calculation module is used to track the B1C signal through a carrier and code tracking loop algorithm and demodulate to obtain B1C positioning data.

[0047] The indoor positioning data acquisition module is used to obtain the UWB signal through a UWB base station and obtain the B1C positioning data through an antenna after an indoor device sends a positioning request.

[0048] The indoor positioning module is used to utilize the B1C positioning data in the Time Difference of Arrival / Symmetric Double-Sided Two-Way Ranging TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the Adaptive Extended Kalman Filter AEKF fusion algorithm to calculate and obtain the final positioning data.

[0049] Furthermore, the improved B1C signal adaptive acquisition algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm.

[0050] Furthermore, the B1C signal acquisition module is specifically used to determine the frequency range of the B1C signal to be captured; use a matched filter through the B1C signal receiver to perform Doppler frequency shift search within the frequency range; multiply the signal to be captured during the search process by the quadrature data carrier respectively, perform correlation operations with the locally generated pseudo-code and then perform square sum processing to obtain the processed signal; according to the processed signal, determine whether the B1C signal is successfully captured through the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm; if it is determined that the capture is successful, determine that the signal to be captured is the B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not the B1C signal.

[0051] Furthermore, the B1C signal acquisition module is also used to obtain the noise power estimate by calculating the variance of N consecutive correlation values of the processed signal, obtain the noise variance and the noise standard deviation, and calculate and obtain the threshold estimate value; perform non-correlated integration processing on the processed signal to obtain the threshold decision value; input the threshold estimate value and the threshold decision value into the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is successfully captured.

[0052] Furthermore, the indoor positioning module is specifically used to set the initial state estimate and the initial covariance matrix , and predict the state and covariance by obtaining the state and observation equations. The expressions of the state and observation equations are:

[0053] ;

[0054] Among them, is the state quantity at time k; is the measured quantity; and are both Gaussian white noises with zero mean and variances R and Q respectively; , are respectively , 's control vectors; represents the state transition process; represents the observation process;

[0055] Predict and estimate the next state, and obtain the expression:

[0056] ;

[0057] Among them, represents the system prior state prediction vector at time k; represents the covariance prediction matrix at time k; is the Jacobian matrix of the state transition model at time k - 1; is the covariance matrix of the process noise;

[0058] Obtain UWB indoor positioning data through the TDOA / SDS - TWR positioning algorithm, and obtain the preset time of arrival T of the signal, signal arrival strength P, target position s and target speed v as state quantities for modeling, and obtain the expression:

[0059] ;

[0060] Among them, is the sampling time of the TOA algorithm, and a state quantity is generated every sampling time interval; is the state quantity at time k - 1;

[0061] Set the B1C positioning data as , where i is the base station number; set the difference between the pseudorange obtained by the TDOA / SDS - TWR positioning algorithm and the predicted distance as the measurement value to update the matrix , ;

[0062] Calculate the Kalman gain, and update the state estimate expression by measuring the next state, resulting in:

[0063] ;

[0064] Among them, is the innovation matrix; is the innovation covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimation vector at time k, which is the latest optimal estimate; represents the covariance update matrix at time k;

[0065] Covariance matrix for updating the process noise and the covariance matrix of the observation noise The calculation formula of is:

[0066] ;

[0067] ;

[0068] where, is a preset adaptive adjustment coefficient;

[0069] The covariance matrix of the process noise and the covariance matrix of the observation noise After adaptive adjustment, continue to iterate to obtain the final state estimation value and get the final positioning data.

[0070] The beneficial effects achieved by the present invention:

[0071] Search through the B1C signal receiver, and use the improved B1C signal adaptive acquisition algorithm to acquire the B1C signal; track the B1C signal through the carrier and code tracking loop algorithm, and demodulate to obtain the B1C positioning data; when the indoor device sends a positioning request, obtain the UWB signal through the UWB base station and obtain the B1C positioning data through the antenna; use the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data. By using the improved B1C signal adaptive acquisition algorithm and the AEKF fusion algorithm for the fusion of B1C signals and UWB signals, the high penetration and high positioning accuracy of the UWB technology are combined with the B1C signal, expanding the application of the Beidou positioning system in the indoor field. Description of the Drawings

[0072] Figure 1 is the flowchart of the indoor positioning method for the fusion of B1C signals and UWB signals of the present invention;

[0073] Figure 2Structural diagram of the indoor positioning system for the fusion of B1C signal and UWB signal of the present invention; Detailed implementation manners

[0074] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and cannot be used to limit the protection scope of the present invention.

[0075] As Figure 1 shown, an embodiment of the present invention provides an indoor positioning method for the fusion of B1C signal and UWB signal, including:

[0076] 101. Search through a B1C signal receiver, and capture the B1C signal using an improved B1C signal adaptive capture algorithm;

[0077] In this embodiment, the indoor positioning system for the fusion of B1C signal and UWB signal includes a B1C signal receiver and a B1C signal transponder in the outdoor part, an antenna and a UWB base station in the indoor part. The B1C signal transponder is connected to the antenna through a coaxial cable. Among them, the B1C signal receiver and the B1C signal transponder adopt a SYN2309 type Global Navigation Satellite System (GNSS) signal transponder, and the UWB base station adopts a UWB radar development kit MAX2001 module; the SYN2309 type GNSS signal transponder supports the forwarding of B1I / B2I / B3I / B1C / B2a / B2b signals, and the satellite model frequency range that can be received is 1560 MHz - 1620 MHz, and it has a built-in GNSS signal receiver, whose timing accuracy is less than or equal to 20 ns, the positioning accuracy is less than or equal to 1 m, and the maximum number of output channels of the forwarded signal is 16; the UWB radar development kit MAX2001 module consists of an on-board ceramic antenna, an STM32F103T8U6 single-chip microcomputer main control chip, an HS-HW handset that can perform real-time ranging data calculation, and a UWB radar integrated in the MAX2001 module; the UWB radar can emit pulse signals with a bandwidth of 0.5 GHz, a center frequency of 4 GHz, and a maximum gain of -23.12 dbm;

[0078] Based on the description of the above indoor positioning system and equipment for the fusion of B1C signal and UWB signal, search through a B1C signal receiver, and capture the B1C signal using an improved B1C signal adaptive capture algorithm;

[0079] The improved B1C signal adaptive capture algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm;

[0080] The capture process of the B1C signal is specifically as follows:

[0081] Determine the frequency range of the B1C signal to be captured;

[0082] Use a B1C signal receiver to perform Doppler frequency shift search within the frequency range using a matched filter;

[0083] Multiply the signals to be captured during the search process by orthogonal data carriers respectively, perform correlation operations and accumulate over one code period with the locally generated pseudo-code (i.e., PN code) respectively to obtain two signals I and Q. The expressions of the two signals are:

[0084] ;

[0085] ;

[0086] The input signal is expressed as R(T); n(t) is expressed as Gaussian random white noise with a mean of 0 and a variance of ; n represents the number of sampling points within the accumulation time of one code period; T represents the sampling period; represents the locally generated PN code, is the local carrier frequency;

[0087] Signals I and Q are first correlated with the locally generated PN code, and then squared and summed to obtain the processed signal R0;

[0088] Calculate the variance of N consecutive correlation values of the processed signal R0 to estimate the noise power and obtain the noise variance and the noise standard deviation , and calculate and obtain the threshold estimate value ;

[0089] Perform non-correlated integration processing on the processed signal R0 to obtain the threshold decision value I. The calculation formula of I is:

[0090] ;

[0091] Input the threshold estimate value and the threshold decision value I into the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is successfully captured; if it is determined that the capture is successful, determine that the signal to be captured is the B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not the B1C signal.

[0092] The specific principles of the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm are:

[0093] Let the initial threshold coefficient be , , the step size for adjusting the number of non-coherent integrations is 1. After starting to work, the threshold decision value will be judged: if the threshold decision value is less than the threshold estimate value, it is regarded that the signal has not been captured, and the counter is incremented by 1. When the counter is greater than 3, it enters the judgment of the next link; if the threshold decision value is greater than the threshold estimate value, it is regarded that the signal has been captured, and it enters the judgment of the next link;

[0094] When the threshold decision value is less than the threshold estimate value, the phenomenon of false alarms caused by noise interference needs to be excluded. Therefore, it is set that when the counter value exceeds 3, it is determined that the current number of non-coherent integrations cannot meet the required signal-to-noise ratio gain, and the number of non-coherent integrations needs to be gradually increased; when the counter value does not exceed 3, the system parameters remain unchanged; the maximum number of non-coherent integrations is set , and the judgment condition is given ; when the condition is satisfied, it means that the current number of non-coherent integrations does not exceed the maximum number of non-coherent integrations , and at this time, the operation of increasing the number of non-coherent integrations is performed: , ; when the condition is not satisfied, it means that the current number of non-coherent integrations has reached the maximum number of non-coherent integrations, and at this time, the system parameters remain unchanged;

[0095] When the threshold decision value is greater than the threshold estimate value, a preset standard that is X times the threshold value is set: when the threshold decision value exceeds this preset standard, it is regarded that the integration time is too long, and the number of non-coherent integrations should be gradually reduced; when the threshold decision value is greater than the threshold estimate value but not greater than the preset standard, the system parameters remain unchanged; in the experiment, X is set to 2, that is, 2 times the threshold; in order to ensure that the number of non-coherent integrations is not less than the set minimum number of non-coherent integrations , the judgment condition is set ; when the condition is satisfied, it means that the current number of non-coherent integrations is not less than the minimum number of non-coherent integrations , and at this time, the operation of reducing the number of non-coherent integrations is performed: , ; when the condition is not satisfied, it means that the current number of non-coherent integrations has reached the minimum number of non-coherent integrations, and at this time, the system parameters remain unchanged.

[0096] 102, the B1C signal is tracked through the carrier and code tracking loop algorithm, and the B1C positioning data is obtained by demodulation;

[0097] In this embodiment, after the successful acquisition of the B1C signal, the acquired B1C signal is tracked: the input B1C signal is mixed with the sine and cosine signals with a 90° phase difference generated inside the receiver terminal, so as to separate the carrier in the signal; then it is respectively subjected to correlation operations with the early, middle, and late three-way pseudo-codes generated by the pseudo-code generator. The three-way pseudo-codes differ by 0.5 chip in sequence. Each branch needs to go through an integration and clearing process, and the results are sent to the code loop discriminator; the code loop discriminator needs to discriminate the signals of different branches respectively to obtain the code phase difference; the filter in the pseudo-code tracking loop filters the output signal of the code loop discriminator to reduce its error and noise; the result of the carrier tracking loop filter assists the code loop with a proportionality coefficient K and is finally adjusted in real time by the pseudo-code generator; at the same time, after the signal in the carrier tracking loop filter passes through the code loop discriminator, the phase difference and frequency difference of the signal are obtained; then, after the phase difference and frequency difference information pass through the carrier tracking loop filter, they are used to adjust the carrier NCO to generate different carrier signals.

[0098] 103. When the indoor device sends a positioning request, it obtains the UWB signal through the UWB base station and obtains the B1C positioning data through the antenna.

[0099] In this embodiment, when the indoor device needs to perform positioning and sends a positioning request, it obtains the UWB signal through the UWB base station and obtains the B1C positioning data through the antenna indoors.

[0100] 104. Utilize the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data. Substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data.

[0101] In this embodiment, Time Difference of Arrival / Symmetrical Double-Sided Two Way Ranging (TDOA / SDS-TWR) is an algorithm for UWB positioning. Utilizing the B1C positioning data in the TDOA / SDS-TWR positioning algorithm can achieve indoor assisted positioning of the UWB signal; the Adaptive Extended Kalman Filter (AEKF) algorithm is an advanced iterative algorithm for state estimation based on the Kalman filter.

[0102] The calculation process of the final positioning data is as follows:

[0103] I. Set the initial state estimate And the initial covariance matrix , and predict the state and covariance using the state and observation equations. The expressions for the state and observation equations are:

[0104] ;

[0105] where, is the state quantity at time k; is the measurement quantity; and are both Gaussian white noises with zero mean and variances R and Q respectively; , are respectively , 's control vectors; represents the state transition process; represents the observation process;

[0106] II. Predict and estimate the next state, and obtain the expression:

[0107] ;

[0108] where, represents the system prior state prediction vector at time k; represents the covariance prediction matrix at time k; is the Jacobian matrix of the state transition model at time k - 1; is the covariance matrix of the process noise;

[0109] III. Obtain UWB indoor positioning data through the TDOA / SDS-TWR positioning algorithm, and use the preset time of arrival T, signal arrival strength P, target position s, and target velocity v as state quantities for modeling, and obtain the expression:

[0110] ;

[0111] where, is the sampling time of the TOA algorithm, and a state quantity is generated every sampling time interval; is the state quantity at time k - 1;

[0112] Set the B1C positioning data as , where i is the base station number; set the pseudorange obtained by the TDOA / SDS-TWR positioning algorithm minus the predicted distance as the measurement value to update the matrix , ;

[0113] IV. Calculate the Kalman gain, and update the next state through measurement, thereby updating the state estimation expression:

[0114] ;

[0115] Among them, is the innovation matrix; is the innovation covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimation vector at time k, which is the latest optimal estimate; represents the covariance update matrix at time k;

[0116] V. Update the covariance matrix of the process noise and the covariance matrix of the observation noise The calculation formula is:

[0117] ;

[0118] ;

[0119] Among them, is the preset adaptive adjustment coefficient;

[0120] After adaptively adjusting the covariance matrix of the process noise and the covariance matrix of the observation noise, continue to iterate to obtain the final state estimation value and get the final positioning data.

[0121] The beneficial effects of the embodiments of the present invention are as follows:

[0122] Search through the B1C signal receiver, and capture the B1C signal using the improved B1C signal adaptive acquisition algorithm; track the B1C signal through the carrier and code tracking loop algorithm, and demodulate to obtain the B1C positioning data; when the indoor device sends a positioning request, obtain the UWB signal through the UWB base station and obtain the B1C positioning data through the antenna; use the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data. By using the improved B1C signal adaptive acquisition algorithm and the AEKF fusion algorithm for the fusion of B1C signals and UWB signals, the high penetration and high positioning accuracy of the UWB technology are combined with the B1C signal, expanding the application of the Beidou positioning system in the indoor field.

[0123] Combined with the indoor positioning method for the fusion of B1C signal and UWB signal described in the above embodiments, the indoor positioning system for the fusion of B1C signal and UWB signal will be described below through embodiments.

[0124] As Figure 2 shown, an embodiment of the present invention provides an indoor positioning system for the fusion of B1C signal and UWB signal, including:

[0125] The B1C signal receiver 201 and the B1C signal transponder 202 in the outdoor part, the antenna 203 and the UWB base station 204 in the indoor part; the B1C signal transponder 202 is connected to the antenna 203 through a coaxial cable;

[0126] The B1C signal acquisition module 205 is used to search through the B1C signal receiver 201 and capture the B1C signal using an improved B1C signal adaptive acquisition algorithm;

[0127] The B1C positioning data calculation module 206 is used to track the B1C signal through the carrier and code tracking loop algorithm and demodulate to obtain the B1C positioning data;

[0128] The indoor positioning data acquisition module 207 is used to obtain the UWB signal through the UWB base station 204 and obtain the B1C positioning data through the antenna 203 after the indoor device sends a positioning request;

[0129] The indoor positioning module 208 is used to utilize the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data.

[0130] Preferably, combined with Figure 2 the embodiment shown, the improved B1C signal adaptive acquisition algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm.

[0131] Preferably, combined with Figure 2 the embodiment shown, the B1C signal acquisition module 205 is specifically used to determine the frequency range of the B1C signal to be captured; perform Doppler frequency shift search within the frequency range through the B1C signal receiver using a matched filter; multiply the signal to be captured during the search process by the quadrature data carrier respectively, perform correlation operations with the locally generated pseudo-code and then perform square sum processing to obtain the processed signal; determine whether the B1C signal is successfully captured according to the processed signal through the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm; if it is determined that the capture is successful, determine that the signal to be captured is the B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not the B1C signal.

[0132] Preferably, in combination with Figure 2 the illustrated embodiment, the B1C signal acquisition module 205 is further configured to obtain the variance of consecutive N correlation values of the processed signal to estimate the noise power, obtain the noise variance and the noise standard deviation, and calculate and obtain a threshold estimate value; perform non-correlated integration processing on the processed signal to obtain a threshold decision value; input the threshold estimate value and the threshold decision value into an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm to determine whether the B1C signal is successfully captured.

[0133] Preferably, in combination with Figure 2 the illustrated embodiment, the indoor positioning module 208 is specifically configured to set an initial state estimate and an initial covariance matrix , and predict the state and covariance by obtaining the state and observation equations. The expressions of the state and observation equations are:

[0134] ;

[0135] wherein, is the state quantity at time k; is the measurement quantity; and are both Gaussian white noises with a mean of zero and variances of R and Q respectively; , are respectively , control vectors; represents the state transition process; represents the observation process;

[0136] Predict and estimate the next state to obtain the expression:

[0137] ;

[0138] wherein, represents the system prior state prediction vector at time k; represents the covariance prediction matrix at time k; is the Jacobian matrix of the state transition model at time k-1; is the covariance matrix of the process noise;

[0139] Obtain UWB indoor positioning data through the TDOA / SDS-TWR positioning algorithm, and obtain the preset signal arrival time T, signal arrival strength P, target position s, and target speed v as state quantities for modeling to obtain the expression:

[0140] ;

[0141] wherein, is the sampling time of the TOA algorithm, and a state quantity is generated every sampling time interval; is the state quantity at time k-1;

[0142] Set the B1C positioning data to , where i is the base station number; set the pseudorange obtained by the TDOA / SDS-TWR positioning algorithm and the predicted distance The difference is used as the measurement value to update the matrix , ;

[0143] Calculate the Kalman gain, and update the state estimate expression by measuring the update of the next state, which is:

[0144] ;

[0145] Among them, is the innovation matrix; is the innovation covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimate vector at time k, which is the latest optimal estimate; represents the covariance update matrix at time k;

[0146] Update the covariance matrix of the process noise and the covariance matrix of the observation noise, and the calculation formula is:

[0147] ;

[0148] ;

[0149] Among them, is the preset adaptive adjustment coefficient;

[0150] After adaptively adjusting the covariance matrix of the process noise and the covariance matrix of the observation noise, continue to iterate to obtain the final state estimate value and obtain the final positioning data.

[0151] The beneficial effects of the embodiments of the present invention are:

[0152] Search through the B1C signal receiver, and capture the B1C signal using an improved B1C signal adaptive acquisition algorithm; track the B1C signal through the carrier and code tracking loop algorithm, and demodulate to obtain B1C positioning data; when the indoor device sends a positioning request, obtain the UWB signal through the UWB base station, and obtain the B1C positioning data through the antenna; utilize the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, and substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data. The improved B1C signal adaptive acquisition algorithm and the AEKF fusion algorithm for the fusion of B1C signal and UWB signal are utilized, combining the high penetration and high positioning accuracy of the UWB technology with the B1C signal, and expanding the application of the Beidou positioning system in the indoor field.

[0153] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0154] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0155] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to generate a computer-implemented process, thereby providing instructions for implementing the steps specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks.

[0157] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention pending approval of the application.

Claims

1. An indoor positioning method for fusing B1C signals with UWB signals, characterized in that: An indoor positioning system for integrating B1C signals and UWB signals is applied, wherein the indoor positioning system for integrating B1C signals and UWB signals comprises an outdoor B1C signal receiver and a B1C signal repeater, an indoor antenna and a UWB base station, wherein the B1C signal repeater is connected to the antenna via a coaxial cable, and the method comprises: Searching by the B1C signal receiver, and capturing the B1C signal using an improved B1C signal adaptive capture algorithm; Tracking the B1C signal through a carrier and code tracking loop algorithm, and demodulating to obtain B1C positioning data; When the indoor device sends a positioning request, the UWB signal is obtained through the UWB base station, and the B1C positioning data is obtained through the antenna; The B1C positioning data is used in the time difference of arrival / symmetric bilateral two-way ranging TDOA / SDS-TWR positioning algorithm to obtain UWB indoor positioning data, and the B1C positioning data and the UWB indoor positioning data are substituted into the adaptive extended Kalman filter AEKF fusion algorithm to calculate the final positioning data.

2. The indoor positioning method of the fusion of B1C signal and UWB signal according to claim 1, characterized in that: The improved B1C signal adaptive capture algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm.

3. The indoor positioning method of the fusion of B1C signal and UWB signal according to claim 2 is characterized in that: The step of searching by the B1C signal receiver and capturing the B1C signal using an improved B1C signal adaptive capture algorithm comprises: Determine the frequency range of the B1C signal that needs to be captured; Performing a Doppler shift search within the frequency range by using a matched filter through the B1C signal receiver; The signals to be captured in the search process are multiplied by the orthogonal data carrier respectively, correlated with the locally generated pseudo code and then squared and processed to obtain the processed signal; According to the processed signal, determining whether the B1C signal is captured successfully by using the adaptive incoherent integration number algorithm and the adaptive threshold adjustment algorithm; If it is determined that the capture is successful, determining that the signal to be captured is the B1C signal; If it is determined that the capture is unsuccessful, it is determined that the signal to be captured is not the B1C signal.

4. The indoor positioning method of the fusion of B1C signal and UWB signal according to claim 3 is characterized in that: The step of determining whether the B1C signal is successfully captured by using the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm according to the processed signal includes: Calculating the variance of N consecutive correlation values ​​of the processed signal to obtain a noise power estimate, and obtaining a noise variance and a noise standard deviation, and calculating a threshold estimate; Performing non-correlated integration processing on the processed signal to obtain a threshold decision value; The threshold estimation value and the threshold decision value are input into the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is captured successfully.

5. The indoor positioning method of combining B1C signal and UWB signal according to any one of claims 1 to 4, characterized in that: The method of using the B1C positioning data in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data, substituting the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm to calculate the final positioning data includes: Setting the initial state estimate With the initial covariance matrix , and the state and observation equations are obtained to predict the state and covariance. The expressions of the state and observation equations are: ； Among them, the is the state quantity at time k; is the measured quantity; With the are all Gaussian white noises with zero mean and variances R and Q respectively; , Respectively , The control vector of Represents the state transfer process; Represents the observation process; Predict and estimate the next state, and the expression is: ； Among them, the represents the system prior state prediction vector at time k; represents the covariance prediction matrix at time k; is the Jacobian matrix of the state transition model at time k-1; is the covariance matrix of process noise; The UWB indoor positioning data is obtained through the TDOA / SDS-TWR positioning algorithm, and the preset signal arrival time T, signal arrival strength P, target position s and target speed v are obtained as state quantities for modeling, and the expression is obtained as follows: ； Among them, the is the sampling time of the TOA algorithm, and a state quantity is generated every time a sampling time interval passes; is the state quantity at time k-1; Set the B1C positioning data as , i is the base station number; the pseudorange obtained by the TDOA / SDS-TWR positioning algorithm Distance from prediction The difference is used as the measurement value to update the matrix , ; Calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows: ； Among them, the is the new information matrix; is the new information covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimation vector at time k, which is the latest optimal estimation; represents the covariance update matrix at time k; Update the covariance matrix of the process noise The covariance matrix of the observation noise is The calculation formula is: ； ； Among them, the is the preset adaptive adjustment coefficient; The covariance matrix of the process noise is The covariance matrix of the observation noise is After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

6. An indoor positioning system integrating B1C signal and UWB signal, characterized in that: include: The outdoor part includes a B1C signal receiver and a B1C signal repeater, and the indoor part includes an antenna and a UWB base station; the B1C signal repeater is connected to the antenna via a coaxial cable; A B1C signal acquisition module, used to search through the B1C signal receiver and capture the B1C signal using an improved B1C signal adaptive capture algorithm; A B1C positioning data calculation module is used to track the B1C signal through a carrier and code tracking loop algorithm, and demodulate to obtain B1C positioning data; An indoor positioning data acquisition module, used to obtain a UWB signal through the UWB base station and obtain the B1C positioning data through the antenna after the indoor device sends a positioning request; The indoor positioning module is used to use the B1C positioning data in the arrival time difference / symmetric bilateral two-way ranging TDOA / SDS-TWR positioning algorithm to obtain UWB indoor positioning data, substitute the B1C positioning data and the UWB indoor positioning data into the adaptive extended Kalman filter AEKF fusion algorithm, and calculate the final positioning data.

7. The indoor positioning system integrating B1C signal and UWB signal according to claim 6, characterized in that: The improved B1C signal adaptive capture algorithm includes an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm.

8. The indoor positioning system integrating B1C signal and UWB signal according to claim 7, characterized in that: The B1C signal acquisition module is specifically used to determine the frequency range of the B1C signal to be captured; perform Doppler frequency shift search within the frequency range by using a matched filter through the B1C signal receiver; multiply the signals to be captured in the search process with the orthogonal data carrier respectively, perform correlation operation with the locally generated pseudo code, and then perform square sum processing to obtain a processed signal; based on the processed signal, determine whether the B1C signal is successfully captured by using the adaptive non-coherent integration number algorithm and the adaptive threshold adjustment algorithm; if it is determined that the capture is successful, determine that the signal to be captured is the B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not the B1C signal.

9. The indoor positioning system integrating B1C signal and UWB signal according to claim 8, characterized in that: The B1C signal acquisition module is also used to obtain the variance of N consecutive correlation values ​​of the processed signal to obtain a noise power estimate, obtain the noise variance and the noise standard deviation, and calculate a threshold estimate; Performing non-correlated integration processing on the processed signal to obtain a threshold decision value; The threshold estimation value and the threshold decision value are input into the adaptive non-coherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is captured successfully.

10. The indoor positioning system integrating B1C signal and UWB signal according to any one of claims 6 to 9, characterized in that: The indoor positioning module is specifically used to set the initial state estimation With the initial covariance matrix , and the state and observation equations are obtained to predict the state and covariance. The expressions of the state and observation equations are: ； Among them, the is the state quantity at time k; is the measured quantity; With the are all Gaussian white noises with zero mean and variances R and Q respectively; , Respectively , The control vector of Represents the state transfer process; Represents the observation process; Predict and estimate the next state, and the expression is: ； Among them, the represents the system prior state prediction vector at time k; represents the covariance prediction matrix at time k; is the Jacobian matrix of the state transition model at time k-1; is the covariance matrix of process noise; The UWB indoor positioning data is obtained through the TDOA / SDS-TWR positioning algorithm, and the preset signal arrival time T, signal arrival strength P, target position s and target speed v are obtained as state quantities for modeling, and the expression is obtained as follows: ； Among them, the is the sampling time of the TOA algorithm, and a state quantity is generated every time a sampling time interval passes; is the state quantity at time k-1; Set the B1C positioning data as , i is the base station number; the pseudorange obtained by the TDOA / SDS-TWR positioning algorithm Distance from prediction The difference is used as the measurement value to update the matrix , ; Calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows: ； Among them, the is the new information matrix; is the new information covariance matrix; is the Kalman gain matrix; is the Jacobian matrix of the observation model; is the covariance matrix of the observation noise; represents the system posterior state estimation vector at time k, which is the latest optimal estimation; represents the covariance update matrix at time k; Update the covariance matrix of the process noise The covariance matrix of the observation noise is The calculation formula is: ； ； Among them, the is the preset adaptive adjustment coefficient; The covariance matrix of the process noise is The covariance matrix of the observation noise is After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

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