B1C signal and UWB signal fused indoor positioning method and system

Abstract

The invention relates to a B1C signal and UWB signal fused indoor positioning method and system, and the method comprises the steps: carrying out the searching through a B1C signal receiver, and carrying out the capturing through an improved B1C signal adaptive capturing algorithm, and obtaining a B1C signal; tracking and demodulating the B1C signal through a carrier and code tracking loop algorithm to obtain B1C positioning data; after indoor equipment sends a positioning request, a UWB signal is obtained through a UWB base station, and B1C positioning data is obtained through an antenna; using the B1C positioning data in a TDOA / SDS-TWR positioning algorithm to obtain UWB indoor positioning data, substituting the B1C positioning data and the UWB indoor positioning data into an AEKF fusion algorithm, and calculating to obtain final positioning data. The high penetrability and high positioning precision of the UWB technology are combined with the B1C signal, and 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 with UWB signals. Background Art

[0002] Beidou B1C signal is a civilian signal provided by China's Beidou satellite navigation system, belonging to the B1 frequency band. B1C signal is transmitted through Beidou satellites to provide positioning, navigation and time confirmation services to the public. The user receiver measures the arrival time difference of the signal and uses triangulation to determine the position. In an open outdoor environment, B1C signal can well realize positioning and navigation functions, but in a complex indoor environment, due to multipath effects and obstacles, B1C signal refraction and diffraction occur, and its positioning signal may be severely attenuated, resulting in reduced positioning accuracy or positioning failure. UWB is a wireless communication technology that uses non-sinusoidal narrowband pulse signals for data transmission. It has a wide bandwidth and low power spectrum density. It is often used in indoor positioning systems to provide high-precision positioning needs. By combining Beidou B1C signal with UWB technology, the application of B1C signal in indoor areas can be expanded.

[0003] The conventional Beidou B1C signal and UWB signal fusion algorithm achieves seamless positioning by combining the outdoor positioning capability of the Beidou B1C signal and the indoor positioning accuracy of the UWB signal. That is, in outdoor or Beidou signal environments, the system mainly relies on Beidou B1C signals for positioning; indoors or in environments where the Beidou signal is blocked, it switches to UWB signals for positioning, thereby improving the continuity and accuracy of positioning.

[0004] However, the B1C signal and UWB signal fusion algorithm does not truly achieve indoor positioning of the B1C signal, but only switches between the B1C signal and the UWB signal. Summary of the invention

[0005] The present invention provides an indoor positioning method and system for fusing B1C signals with UWB signals, utilizing an improved B1C signal adaptive capture algorithm and an AEKF fusion algorithm for fusing B1C signals with UWB signals, combining the high penetration and high positioning accuracy of UWB technology with B1C signals, and expanding the application of Beidou positioning system in indoor fields.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] In a first aspect, a method for indoor positioning by fusing a B1C signal with a UWB signal is provided, comprising:

[0008] Searching is performed through a B1C signal receiver, and the B1C signal is captured using an improved B1C signal adaptive capture algorithm;

[0009] The B1C signal is tracked through the carrier and code tracking loop algorithm, and demodulated to obtain the B1C positioning data;

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

[0011] The B1C positioning data is used in the time difference of arrival / symmetric bilateral two-way ranging TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data. 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.

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

[0013] Further, searching is performed by a B1C signal receiver, and the B1C signal is captured using an improved B1C signal adaptive capture algorithm, including:

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

[0015] Doppler shift search is performed within the frequency range using a matched filter via the B1C signal receiver;

[0016] 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;

[0017] According to the processed signal, whether the B1C signal is captured successfully is determined by using an adaptive non-coherent integration times algorithm and an adaptive threshold adjustment algorithm;

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

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

[0020] Further, according to the processed signal, whether the B1C signal is captured successfully is determined by using an adaptive non-coherent integration number algorithm and an adaptive threshold adjustment algorithm, including:

[0021] The variance of N consecutive correlation values ​​of the processed signal is calculated to obtain the noise power estimate, and the noise variance and noise standard deviation are obtained, and the threshold estimation value is calculated;

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

[0023] The threshold estimation value and the threshold decision value are input into the adaptive non-coherent integration number algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is captured successfully.

[0024] Furthermore, the B1C positioning data is used in the TDOA / SDS-TWR positioning algorithm to obtain the UWB indoor positioning data. The B1C positioning data and the UWB indoor positioning data are substituted into the AEKF fusion algorithm to calculate the final positioning data, including:

[0025] 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:

[0026] ;

[0027] in, is the state quantity at time k; is the measured quantity; and They are all Gaussian white noise with zero mean and variances of R and Q respectively; , They are , The control vector of Indicates the state transition process; Represents the observation process;

[0028] Predict and estimate the next state, and the expression is:

[0029] ;

[0030] in, 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;

[0031] 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:

[0032] ;

[0033] in, 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;

[0034] Set B1C positioning data to , 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 , ;

[0035] Calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows:

[0036] ;

[0037] in, is the new information 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;

[0038] Update the covariance matrix of the process noise The covariance matrix with the observation noise The calculation formula is:

[0039] ;

[0040] ;

[0041] in, is the preset adaptive adjustment coefficient;

[0042] The covariance matrix of the process noise is The covariance matrix with the observation noise After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

[0043] In the second aspect, an indoor positioning system integrating B1C signal and UWB signal is provided, including:

[0044] 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;

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

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

[0047] Indoor positioning data acquisition module, used to obtain UWB signals through UWB base stations and B1C positioning data through antennas when indoor devices send positioning requests;

[0048] 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 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 the final positioning data.

[0049] Furthermore, the improved B1C signal adaptive capture 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; perform Doppler frequency shift search within the frequency range by using a matched filter through a B1C signal receiver; multiply the signals to be captured in the search process with the orthogonal data carrier respectively, perform correlation operations 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 an adaptive non-coherent integration number algorithm and an adaptive threshold adjustment algorithm; if it is determined that the capture is successful, determine that the signal to be captured is a B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not a B1C signal.

[0051] Furthermore, the B1C signal acquisition module is also used to calculate the variance of N consecutive correlation values ​​of the processed signal to obtain a noise power estimate, and obtain the noise variance and noise standard deviation, and calculate a threshold estimate; 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.

[0052] Furthermore, 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:

[0053] ;

[0054] in, is the state quantity at time k; is the measured quantity; and They are all Gaussian white noise with zero mean and variances of R and Q respectively; , They are , The control vector of Indicates the state transition process; Represents the observation process;

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

[0056] ;

[0057] in, 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;

[0058] 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:

[0059] ;

[0060] in, 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;

[0061] Set B1C positioning data to , 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 , ;

[0062] Calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows:

[0063] ;

[0064] in, is the new information 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] Update the covariance matrix of the process noise The covariance matrix with the observation noise The calculation formula is:

[0066] ;

[0067] ;

[0068] in, is the preset adaptive adjustment coefficient;

[0069] The covariance matrix of the process noise is The covariance matrix with the observation noise After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

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

[0071] Search through the B1C signal receiver, and use the improved B1C signal adaptive capture algorithm to capture 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, substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm, and calculate the final positioning data. The improved B1C signal adaptive capture algorithm and the AEKF fusion algorithm that fuses the B1C signal and the UWB signal are used to combine the high penetration and high positioning accuracy of the UWB technology with the B1C signal, expanding the application of the Beidou positioning system in the indoor field. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of the indoor positioning method of the present invention by fusing B1C signal and UWB signal;

[0073] Figure 2It is a structural diagram of the indoor positioning system of the present invention that integrates B1C signal and UWB signal; DETAILED DESCRIPTION

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

[0075] like Figure 1 As shown, an embodiment of the present invention provides an indoor positioning method for fusing B1C signals with UWB signals, including:

[0076] 101, searching through a B1C signal receiver, and capturing a B1C signal using an improved B1C signal adaptive capture algorithm;

[0077] In this embodiment, the indoor positioning system integrating B1C signal and UWB signal includes a B1C signal receiver and a B1C signal repeater in the outdoor part, an antenna and a UWB base station in the indoor part, and the B1C signal repeater is connected to the antenna through a coaxial cable. The B1C signal receiver and the B1C signal repeater adopt the SYN2309 global navigation satellite system (GlobalNavigation Satellite The SYN2309 GNSS signal repeater supports B1I / B2I / B3I / B1C / B2a / B2b signal forwarding, can receive satellite models with a frequency range of 1560MHz-1620MHz, and has a built-in GNSS signal receiver with a timing accuracy of less than or equal to 20ns, a positioning accuracy of less than or equal to 1m, and a maximum of 16 forwarding signal output channels. The MAX2001 module of the UWB radar development kit consists of an onboard ceramic antenna, an STM32F103T8U6 single-chip microcomputer main control chip, an HS-HW handheld device that can perform real-time ranging data calculation, and a UWB radar integrated in the MAX2001 module. The UWB radar can transmit pulse signals with a bandwidth of 0.5GHz, a center frequency of 4GHz, and a maximum gain of -23.12dbm.

[0078] Based on the description of the indoor positioning system and device for integrating the B1C signal and the UWB signal, the B1C signal is searched by the B1C signal receiver and captured by using the improved B1C signal adaptive capture algorithm;

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

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

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

[0082] Doppler shift search is performed within the frequency range using a matched filter via the B1C signal receiver;

[0083] The signals to be captured in the search process are multiplied with the orthogonal data carrier respectively, and the correlation operation and accumulation of one code period are performed with the locally generated pseudo code (i.e. PN code) to obtain two signals I and Q. The expressions of the two signals are:

[0084] ;

[0085] ;

[0086] The input signal is represented by R(T); n(t) is represented by a mean of 0 and a variance of Gaussian random white noise; n represents the number of sampling points within the accumulation time of a code cycle; 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] The variance of N consecutive correlation values ​​of the processed signal R0 is calculated to estimate the noise power and obtain the noise variance. and noise standard deviation , and calculate the threshold estimate ;

[0089] The processed signal R0 is processed by non-correlated integration to obtain the threshold decision value I; the calculation formula of I is:

[0090] ;

[0091] Threshold estimate The threshold decision value I is input into the adaptive incoherent integration times algorithm and the adaptive threshold adjustment algorithm to determine whether the B1C signal is captured successfully; if the capture is determined to be successful, the signal to be captured is determined to be a B1C signal; if the capture is determined to be unsuccessful, the signal to be captured is determined not to be a B1C signal.

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

[0093] Assume the initial threshold coefficient is , , the non-coherent integration times are adjusted in steps of 1; after starting work, the threshold decision value will be judged: if the threshold decision value is less than the threshold estimation value, it is considered that the signal has not been captured, and the counter is increased by 1. When the counter is greater than 3, the next link is entered for judgment; if the threshold decision value is greater than the threshold estimation value, it is considered that the signal has been captured, and the next link is entered for judgment;

[0094] When the threshold judgment value is less than the threshold estimation value, it is necessary to eliminate the missed alarm caused by noise interference. Therefore, when the counter value exceeds 3, it is determined that the current number of incoherent integrations cannot meet the required signal-to-noise ratio gain, and the number of incoherent integrations needs to be gradually increased; when the counter value does not exceed 3, the system parameters do not change; set the maximum number of incoherent integrations , and give the judgment conditions ; When the condition is met, it means that the current number of incoherent integration times does not exceed the maximum number of incoherent integration times , at this time, the operation of increasing the number of incoherent integrations is performed: , ; When the condition is not met, it means that the current number of incoherent integrations has reached the maximum number of incoherent integrations, and the system parameters remain unchanged;

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

[0096] 102, tracking the B1C signal through a carrier and code tracking loop algorithm, and demodulating to obtain B1C positioning data;

[0097] In this embodiment, after confirming that the B1C signal is captured successfully, the captured B1C signal is tracked: the input B1C signal is mixed with the sine and cosine signals with a phase difference of 90° generated inside the receiver terminal, so as to separate the carrier in the signal; then, the three pseudo codes of leading, intermediate and lagging generated by the pseudo code generator are correlated respectively, and the three pseudo codes differ by 0.5 code chips respectively. Each branch needs to be integrated and cleared, and the result is sent to the code loop discriminator; the code loop discriminator needs to discriminate the signals of different branches respectively, so as 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 the proportional 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 the phase difference and frequency difference information passes through the carrier tracking loop filter, and is 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 a UWB signal through a UWB base station and obtains B1C positioning data through an indoor antenna.

[0100] 104, 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 and obtain 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. The B1C positioning data can be used in the TDOA / SDS-TWR positioning algorithm to achieve indoor auxiliary positioning of UWB signals. The Adaptive Extended Kalman Filter (AEKF) algorithm is an advanced iterative algorithm that performs state estimation based on Kalman filtering.

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

[0103] 1. 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:

[0104] ;

[0105] in, is the state quantity at time k; is the measured quantity; and They are all Gaussian white noise with zero mean and variances of R and Q respectively; , They are , The control vector of Indicates the state transition process; Represents the observation process;

[0106] Second, predict and estimate the next state, and get the expression:

[0107] ;

[0108] in, 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;

[0109] 3. Obtain UWB indoor positioning data through 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, and the expression is:

[0110] ;

[0111] in, 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;

[0112] Set B1C positioning data to , 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 , ;

[0113] Fourth, calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows:

[0114] ;

[0115] in, is the new information 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] 5. Update the covariance matrix of process noise The covariance matrix with the observation noise The calculation formula is:

[0117] ;

[0118] ;

[0119] in, is the preset adaptive adjustment coefficient;

[0120] The covariance matrix of the process noise is The covariance matrix with the observation noise After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

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

[0122] Search through the B1C signal receiver, and use the improved B1C signal adaptive capture algorithm to capture 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, substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm, and calculate the final positioning data. The improved B1C signal adaptive capture algorithm and the AEKF fusion algorithm that fuses the B1C signal and the UWB signal are used to combine the high penetration and high positioning accuracy of the UWB technology with the B1C signal, expanding the application of the Beidou positioning system in the indoor field.

[0123] In combination with the indoor positioning method by fusing B1C signals with UWB signals described in the above embodiments, an indoor positioning system by fusing B1C signals with UWB signals is described below through embodiments.

[0124] like Figure 2 As shown, an embodiment of the present invention provides an indoor positioning system that integrates B1C signals and UWB signals, including:

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

[0126] A 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 capture algorithm;

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

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

[0129] The indoor positioning module 208 is used to use 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, and calculate the final positioning data.

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

[0131] Preferably, combined Figure 2 In the illustrated embodiment, 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 by using a matched filter through a 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 captured successfully through an adaptive non-coherent integration number algorithm and an adaptive threshold adjustment algorithm; if it is determined that the capture is successful, determine that the signal to be captured is a B1C signal; if it is determined that the capture is unsuccessful, determine that the signal to be captured is not a B1C signal.

[0132] Preferably, combined Figure 2 In the illustrated embodiment, the B1C signal acquisition module 205 is further used to obtain a noise power estimate by calculating the variance of N consecutive correlation values ​​of the processed signal, and to obtain a noise variance and a noise standard deviation, and to calculate a threshold estimate; to perform non-correlated integration processing on the processed signal to obtain a threshold decision value; and to 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, combined Figure 2 In the embodiment shown, the indoor positioning module 208 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:

[0134] ;

[0135] in, is the state quantity at time k; is the measured quantity; and They are all Gaussian white noise with zero mean and variances of R and Q respectively; , They are , The control vector of Indicates the state transition process; Represents the observation process;

[0136] Predict and estimate the next state, and the expression is:

[0137] ;

[0138] in, 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;

[0139] 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:

[0140] ;

[0141] in, 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;

[0142] Set B1C positioning data to , 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 , ;

[0143] Calculate the Kalman gain and obtain the update of the next state through measurement, so as to update the state estimation expression as follows:

[0144] ;

[0145] in, is the new information 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;

[0146] Update the covariance matrix of the process noise The covariance matrix with the observation noise The calculation formula is:

[0147] ;

[0148] ;

[0149] in, is the preset adaptive adjustment coefficient;

[0150] The covariance matrix of the process noise is The covariance matrix with the observation noise After the adaptive adjustment, the iteration is continued to obtain the final state estimation value and the final positioning data.

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

[0152] Search through the B1C signal receiver, and use the improved B1C signal adaptive capture algorithm to capture 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, substitute the B1C positioning data and the UWB indoor positioning data into the AEKF fusion algorithm, and calculate the final positioning data. The improved B1C signal adaptive capture algorithm and the AEKF fusion algorithm that fuses the B1C signal and the UWB signal are used to combine the high penetration and high positioning accuracy of the UWB technology with the B1C signal, expanding the application of the Beidou positioning system in the indoor field.

[0153] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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 flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

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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