UWB equipment position determination method and device in LOS / NLOS environment, and electronic equipment

Abstract

The invention discloses a UWB equipment position determination method and device in an LOS / NLOS environment, and electronic equipment, and belongs to the technical field of computers. The method comprises the following steps: acquiring TOF and RSS information of a target device and a reference base station in response to detecting that the target device establishes a connection with the reference base station; calculating to obtain local optimal estimation of the position state of each sub-filter in all channels according to TOF and RSS information in the sub-filter of the first-level filtering channel; and determining the position of the target equipment in the secondary filtering part according to the local optimal estimation results of all the sub-filters. The method can adapt to LOS / NLOS environment change under the condition that NLOS recognition compensation is not needed, high-precision and high-robustness real-time position estimation and tracking of target equipment are achieved by means of a mixed parameter scheme, and the calculation time is equivalent to that of pure TOF measurement tracking.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a method and device for determining the position of a UWB (Ultra Wide Band) device in a LOS / NLOS (line-of-sight / non-line-of-sight) environment, and an electronic device. Background Art

[0002] UWB signals have gradually become the mainstream technical means for indoor high-precision positioning due to their strong penetration and anti-interference advantages. However, the positioning performance of UWB wireless ad hoc networks varies greatly in different signal propagation environments. For example, in indoor non-line-of-sight environments, the positioning accuracy of UWB target devices based on parameter methods such as TOF (time of flight) or RSS (received signal strength) will be reduced due to the introduction of NLOS errors. This greatly limits the implementation of high-precision positioning and target tracking tasks in complex LOS / NLOS environments.

[0003] According to the currently known technologies, conventional positioning algorithms based on a single parameter are difficult to adapt to a variety of environments. For example, in reference 1 (Ouyang Ningfeng, Zhou Yan, Xu Jianmin. Robust target tracking based on interactive multi-models in LOS / NLOS mixed environments [J]. Computer Application Research, 2013, 30 (11)), in actual use, it is necessary to select appropriate sensor information parameters according to different LOS and NLOS environments. At the same time, the mainstream NLOS mitigation solution adopts NLOS identification and error correction strategies. For example, reference 2 (Zhang Chen. NLOS error identification and suppression algorithm in indoor environment [D]. Jiangsu Province: Nanjing University of Posts and Telecommunications, 2023.) proposes a suppression algorithm that improves the traditional quadratic programming algorithm to solve the problem of non-line-of-sight errors in indoor positioning, identifies and classifies line-of-sight and non-line-of-sight data, and uses the classified data to perform positioning using the proposed residual weighted algorithm. Reference 3 (Li Wenfeng, Zhou Jinglong, Wang Qi, et al. Non-line-of-sight recognition and ranging error suppression of ultra-wideband signals [J]. Control and Decision, 2024, 39(8): 2605-2612.) Based on the position difference between the first path and the strongest path, the power difference between the received signal and the first path, and the distance residual, a non-line-of-sight recognition method with simple calculation and high recognition accuracy is proposed. Based on the biased Kalman filter and maximum likelihood estimation algorithm, a non-line-of-sight error suppression method that integrates the ultra-wideband signal reception strength and signal arrival time is proposed. Reference 4 (Xu Haijian. Research on carrier phase estimation algorithm based on LoS / NLoS identification [D]. Beijing University of Posts and Telecommunications, 2023.) focuses on how to achieve continuous and reliable 5G signal phase tracking in complex indoor multipath environments, and proposes two main algorithms, one is a LoS / NLoS recognition algorithm based on dual-polarization antennas and convolutional neural networks, and the other is a carrier phase estimation algorithm based on LoS / NLoS recognition. However, considering that the LOS and NLOS environments are constantly changing in real environments, it is extremely difficult to identify and correct NLOS in real time, which limits the practical application of mainstream NLOS mitigation solutions. Therefore, the problem of highly robust target tracking and positioning in complex LOS / NLOS changing scenarios is a technical problem that has not yet been solved. Summary of the invention

[0004] In response to the current technical problem of target tracking and positioning with high robustness required in complex LOS / NLOS changing scenarios, the present invention provides a method for determining the position of a UWB device in a LOS / NLOS environment, which can adapt to LOS / NLOS environmental changes without the need for NLOS recognition compensation, and achieve high-precision, high-robustness real-time position estimation and tracking of the target device with the help of a hybrid parameter scheme.

[0005] In a first aspect, the present invention provides a method for determining the location of a UWB device in a LOS / NLOS environment, comprising the following steps:

[0006] Step 1: Obtain the base station's measurement information of the target through TOF and RSS, and fuse the two measurement information;

[0007] Step 2: Perform a first-level filtering calculation on the fused measurement information; the first-level filtering sets J sub-filters, where the number of EKF sub-filters is N, the number of EHF sub-filters is JN, and the input of the first-level filtering is the measurement vector Z at time k k , the optimal state estimate at time k-1 and the posterior estimated covariance matrix For N EKF sub-filters, set the state transfer matrix of different models, and set the noise covariance matrix and process noise to use noise matrices of different sizes as the projection of the LOS environment; for JN EHF sub-filters, set the damping factor and process noise coefficient matrix of different sizes as the projection of the NLOS environment; set the optimal state estimate at J k moments output by J sub-filters and the posterior estimated covariance matrix

[0008] Step 3: Perform secondary filtering calculation on the result of the primary filtering output; the secondary filtering is performed in the federated filter based on the output of each sub-filter in the primary filtering. and Perform overall data fusion to obtain the global optimal positioning estimation result of the target; the secondary filtering first The matrix P(k) is normalized, and the eigenvalues ​​of the matrix P"(k) corresponding to each sub-filter of the first-level filtering are calculated. The weight factors of each sub-filter in the second-level filtering are calculated according to the eigenvalues. Finally, the federal filter calculates the optimal positioning estimation result of the target at the current time k based on the weight factors.

[0009] In the second aspect, the present invention also provides a UWB positioning device in a LOS / NLOS environment, comprising: an acquisition unit, configured to acquire TOF distance information and RSS information between the target device and the reference base station in response to detecting that the target device has established a connection with the reference base station; a first-level multi-channel positioning unit, used to perform the first-level filtering calculation in the method of the present invention, the first-level multi-channel positioning unit is configured to perform a local optimal estimation calculation of the position state of the above-mentioned target device in the sub-filter according to the above-mentioned TOF distance information and RSS information; a determination unit, used to perform the second-level filtering calculation in the method of the present invention, the determination unit is configured to determine the global optimal estimated position result of the target device according to the above-mentioned local optimal estimation result and the second-level filter.

[0010] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the first aspect.

[0011] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0012] Compared with the prior art, the present invention has the following beneficial effects: after the target device and the reference base station establish communication, the method, device, and electronic device of the present invention can have higher positioning accuracy and better robustness in the LOS / NLOS environment without NLOS identification. The present invention fuses the TOF and RSS measurements and uses two filters to work in parallel, respectively using an extended Kalman filter (EKF) and an extended H ∞ The filter (EHF) approximates the LOS and NLOS propagation environments, and then adopts a secondary filtering structure to achieve adaptation to complex LOS / NLOS scenarios without the need for NLOS identification and error correction. The present invention aims at the disadvantages that the EKF effect depends on the model refinement, and the disadvantage that the EHF effect depends on the adjustment of the damping factor parameters. The multi-channel method of multi-domain projection is adopted to calculate the local optimal estimation results in multiple spaces and then use secondary filtering to perform global optimal estimation and solve, which effectively alleviates the inherent shortcomings of each sub-filter. The present invention optimizes the error coefficient allocation structure of the federal filter, cancels the traditional fixed error allocation coefficient, and instead allocates coefficients based on the observability of each sub-filter, thereby improving the stability of the filtering algorithm and its adaptability to the LOS / NLOS environment. The calculation time of the present invention is comparable to that of simple TOF measurement and tracking, and there is no need to add new hardware equipment, so it has good feasibility. In addition, compared with the estimation scheme of a single parameter, the present invention can provide more accurate information about the location of the target node. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the implementation principle of the UWB positioning method under the LOS / NLOS environment of the present invention;

[0014] Figure 2 This is an experimental effect diagram of using the method of the present invention to track the uniform motion of a target along a straight line;

[0015] Figure 3 This is a comparison diagram of the deviations in various directions between the method of the present invention and the simple TOF positioning in one test;

[0016] Figure 4 This is an empirical CDF comparison diagram of the positioning deviation between the method of the present invention and the simple TOF positioning in one test;

[0017] Figure 5 This is a comparison chart of the mean deviations in each direction between the method of the present invention and the TOF positioning alone in 5 tests;

[0018] Figure 6 This is a comparison chart of the standard deviations in each direction between the method of the present invention and the simple TOF positioning in 5 tests;

[0019] Figure 7 This is the tracking trajectory effect diagram of the three algorithms in the LOS / NLOS mixed environment;

[0020] Figure 8 It is a comparison diagram of the horizontal and vertical coordinate errors of the method of the present invention and the simple TOF positioning when the NLOS error obeys an exponential distribution with a mean of 1 in a LOS / NLOS mixed environment;

[0021] Fig. 9 It is an empirical CDF comparison diagram of the abscissa and ordinate positioning deviations of the method of the present invention and pure TOF positioning in one test in a mixed LOS / NLOS environment;

[0022] Fig.10 is a comparison chart of the deviation mean values ​​of the abscissa and ordinate of the method of the present invention and the TOF positioning alone in 5 tests in a mixed LOS / NLOS environment;

[0023] Fig.11 It is a comparison diagram of the standard deviations in each direction of the method of the present invention and the simple TOF positioning when the NLOS error obeys the N(3,4) distribution in the LOS / NLOS mixed environment;

[0024] Fig.12 is a schematic diagram of a UWB base station positioning device in a LOS / NLOS environment according to an embodiment of the present invention;

[0025] Fig.13 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0027] Assume that the target moves in the three-dimensional space within the monitoring area of ​​m base stations (BS), and define the state vector X of the target motion at time k: k for:

[0028]

[0029] where p k =[x(k),y(k),z(k)] T Indicates the coordinates of the target in the x, y, and z directions. is the moving speed of the target in the x, y, and z directions. The superscript T indicates transposition. The state equation of the target is as follows:

[0030]

[0031] In the formula, is the prior estimate of the motion state vector of the target at time k; is the optimal estimate of the motion state vector of the target at time k-1; the matrix F k-1 is the state transfer matrix from time k-1 to time k, describing the motion form of the tracking target between two consecutive time steps; B k-1 represents the noise Jacobian matrix at time k-1; process noise W k Describes the unmodeled random acceleration of a moving target.

[0032] Figure 1 This is the principle flow of the present invention. Based on the TOF and RSS fusion measurement model, the design is based on the extended H ∞ The two-stage weighted filtering robust tracking algorithm of the filter and extended Karl filter. The overall principle is: in this example, J different sub-filters are set, including N EKF sub-filters and JN EHF sub-filters. The state result and covariance matrix of the global optimal estimate calculated at time k-1 are used as the input of all sub-filters at time k; each sub-filter calculates its own local optimal estimate state result and covariance matrix; then the local optimal estimate state result and covariance matrix calculated by all sub-filters are input into the second-stage federated filter for information fusion, and the federated filter weighted calculation is performed according to the observability to obtain the global optimal estimate state result at time k.

[0033] A method for determining the position of a UWB device in a LOS / NLOS environment according to an embodiment of the present invention includes the following three steps.

[0034] Step 1: Obtain the base station's measurement information of the target through TOF and RSS, and fuse the two measurement information.

[0035] On the one hand, TOF measurement is performed. The TOF measurement value Y monitored by the base station BS at time k k for:

[0036]

[0037] In the formula, y k,m is the mth base station BS m The TOF observation distance between the tracked target and the ranging equation is as follows:

[0038]

[0039] Among them, BS m =[x BS ,y BS ,z BS ] T represents the location coordinates of the mth base station, and the measurement noise v at time k k,m is Gaussian white noise, following the distribution is the noise variance, and η is the positive non-line-of-sight error caused by NLOS propagation.

[0040] On the other hand, RSS measurement is performed. The RSS measurement equation of the base station at time k is:

[0041]

[0042] In the formula, RSS k is the received signal strength measured by the receiver, r k,m is the received signal strength measured by the receiver of the mth base station at time k; PL(·) is the signal strength calculation function with respect to distance; d 0 is the reference distance; PL(d 0 ) is the signal strength at the distance from the sending node, which is generally obtained from the hardware description definition; n is the signal attenuation exponent, which is usually 2 to 4; ξ is random noise, which obeys Gaussian distribution.

[0043] Then, TOF-RSSI measurement fusion is performed. Information fusion is performed in the form of measurement expansion, and the measurement equation is established using TOF and RSSI information:

[0044]

[0045] Among them, Z k is the measurement vector, the measurement matrix H k,TOF and H k,RSS It can be calculated from equation (4) and equation (5) respectively, where ε is the noise vector.

[0046] Step 2: Perform a first-level filtering calculation on the fused measurement information.

[0047] At the current time k, let the input of the first-level filtering part be the measurement information Z k , the optimal state estimate at time k-1 and The corresponding covariance matrix In addition, the filtering calculation process also requires the use of sensors such as IMU to obtain acceleration information for status updates.

[0048] The first-level filtering adopts the idea of ​​multi-domain projection and multi-channel calculation. Figure 1As shown, the first-stage filtering part adopts a multi-channel form, using multiple EKF models to approximate the LOS environment and multiple EHF models to approximate the NLOS environment. The total number of sub-filters is set to J, the number of EKF sub-filters is N, and the number of EHF sub-filters is JN.

[0049] Will and Substitute the 1st to Nth EKF filters respectively, corresponding to the LOS sub-model, and the EKF equation is as follows:

[0050]

[0051] in, is the prior estimated covariance matrix at time k, is the posterior estimated covariance matrix at time k-1, Q k-1 is the noise covariance matrix. The calculation equation of the EKF correction update phase is:

[0052]

[0053] Among them, K k is the gain matrix, G k is the measurement matrix, G k =[H k,TOF H k,RSS ] T , R k is the measurement noise matrix, C k is the measurement noise Jacobian matrix, is the posterior estimated state vector at time k, is the posterior estimated covariance matrix at time k, I is the identity matrix, and the superscript -1 indicates the inverse matrix. From the above, we can get the state estimation and covariance of the LOS submodel under the action of EKF

[0054] F of multiple EKF sub-filters k-1 The matrix is ​​substituted using models with different degrees of refinement. k-1 and W k Noise matrices of different sizes are used to address the problem of inaccurate system noise estimation, and the model noise space closest to the actual situation is searched in multiple domain spaces.

[0055] Corresponding to the NLOS sub-model, we first need to assume that the process noise matrix w k and measurement noise v k Energy is bounded. Given a damping factor γ, the equations used for the EHF filters from N+1 to J are as follows:

[0056]

[0057]

[0058] Among them, L k is the coefficient matrix, S k is the EHF gain matrix, M is the process noise coefficient matrix, G k-1 , R k-1 are the measurement matrix and measurement noise matrix at time k-1 respectively. Based on this, the EHF state estimation can be obtained by analogy: and covariance

[0059] The damping factors γ and M of multiple EHF sub-filters are set to different sizes, and the disadvantage of EHF over-reliance on coefficient tuning is alleviated through multi-domain projection.

[0060] Step 3: Perform secondary filtering calculation on the output of the primary filtering.

[0061] This part of the input is the multiple calculated by the J sub-filters of the first-level filtering and This part performs overall data fusion in the federated filter based on the error covariance matrix of each sub-filter and the optimal state estimate to obtain the global optimal positioning estimation result, such as Figure 1 The secondary filtering part is shown in FIG.

[0062] The first-level filter is calculated The matrix can reflect the size of the system state estimation error. Let p ij is the estimated error covariance matrix of the Kalman filter Elements in . Calculate the matrix The eigenvalue of is used as the observability measurement factor. The matrix P is the matrix Since the size of the eigenvalues ​​of the P matrix is ​​unbounded, and different system states or linear combinations of states lack dimensional uniformity, in order to avoid confusion when comparing eigenvalues, the matrix P(k) at time k is normalized as follows:

[0063]

[0064] Among them, P(k) represents the estimated error covariance matrix, P(0) represents the initial value of P(k), m is the dimension of the covariance matrix P(k), and Tr(·) is the matrix trace. At this time, the eigenvalue of the P"(k) matrix corresponding to the j-th subfilter is assumed to be The weight factor of the sub-filter in the secondary filtering based on the eigenvalue calculation is as follows:

[0065]

[0066] in, is the factor corresponding to the i-th eigenvalue of the j-th sub-filter, κ j is the error allocation coefficient matrix of the jth sub-filter. All J sub-filters satisfy the following conditions:

[0067]

[0068] Then read the distributed preliminary fusion results, construct the error covariance matrix of the federated filter and the system as a whole, and perform real-time coefficient allocation based on the error allocation coefficient matrix calculation results. The federated filter equation is as follows:

[0069]

[0070] Update the overall error covariance matrix of the system at time k as follows:

[0071]

[0072] Among them, P i,k is the error covariance matrix of the i-th filter at time k, is the optimal state estimate of the i-th filter at time k; is the optimal estimation state after fusion, P g,k-1 , P g,k are the error covariance matrices of the entire system at time k-1 and time k respectively, Represents the estimated error covariance matrix of the i-th filter at time k, which serves as the initial value of the covariance matrix of the first-level filter in the next epoch.

[0073] The present invention fuses the TOF and RSS measurements and uses two filters to work in parallel, respectively using an extended Kalman filter and an extended H ∞ The LOS and NLOS propagation environments are approximated by filtering, and then the global optimal estimate is obtained by federal filtering based on the observability of the approximation results of each sub-filter. It has high positioning accuracy and good stability, and the calculation time is comparable to that of simple TOF measurement and tracking.

[0074] The hybrid solution in this embodiment can provide more accurate information about the location of the target node than the estimation solution of a single parameter. The positioning solution of multiple parameter hybrid depends on the accuracy requirements and complexity constraints.

[0075] The degree of refinement and accuracy of the state equation transfer model and the incremental model in the present invention will affect the accuracy of the algorithm. In actual use, inertial navigation devices and other equipment with different accuracies can be introduced to improve the adaptability of the method of the present invention to the environment and maintain positioning accuracy.

[0076] The present invention is not limited to UWB signals, but can be extended to wireless ad hoc networks of other signal types, such as Bluetooth, Wifi, etc.

[0077] The implementation process of the present invention can be further expanded to the positioning of indoor and outdoor intersection areas, and hybrid solutions can be used for joint positioning with outdoor sensors such as GNSS and 5G to achieve full coverage and seamless connection of complex wireless positioning in indoor and outdoor areas.

[0078] The present invention is experimented to verify the technical effects that can be achieved.

[0079] During the simulation, it is assumed that four base stations track the mobile target. The x-coordinate of the base station is BSx = [15m, 15m, 25m, 25m], the y-coordinate of the base station is BSy = [20m, 110m, 20m, 110m], the initial position of the mobile target is (20m, 100m), the sampling interval T is 1s, and the tracking time t is 200s.

[0080] Figure 2 To track the target moving at a constant speed along a straight line. Assuming that the measurement noise is Gaussian white noise in the LOS case, the ranging error noise of exponential distribution and non-zero mean Gaussian distribution is set in the NLOS environment. In the experiment, the method of the present invention (TOF-RSS) is compared with the tracking algorithm based on EHF and EKF under the condition of simple TOA measurement. After 5 Monte Carlo simulations, the average value of the standard deviation (STD) is taken as the accuracy evaluation index.

[0081] Figure 3 For the comparison of positioning methods, the overall positioning result shows that the TOF-RSS joint tracking algorithm is better than the simple TOF positioning algorithm.

[0082] Figure 4 The empirical CDF comparison diagram of the horizontal and vertical positioning deviations of one experiment shows that the convergence speed of the CDF error curve of the TOF-RSS joint tracking algorithm is significantly faster than that of the TOF method in all directions. Therefore, the performance of the TOF-RSS joint tracking algorithm is significantly better than that of the simple TOF positioning algorithm.

[0083] Figure 5 The figure is a comparison chart of the mean deviation of the horizontal and vertical coordinates of 5 experiments. 5 groups of experiments were carried out under the same trajectory. Compared with the pure TOF positioning algorithm, the deviation of the TOF-RSS joint tracking algorithm in the X and Y directions is more stable, so the noise resistance is also better.

[0084] Figure 6 The figure shows the standard deviation comparison of the horizontal and vertical coordinates of 5 experiments. 5 groups of experiments were carried out under the same trajectory. Compared with the pure TOF positioning algorithm, the standard deviation of the TOF-RSS joint tracking algorithm in the X and Y directions is smaller and more stable, so it has better noise resistance.

[0085] When in a mixed LOS / NLOS environment, the measurement process error includes not only the standard measurement error, but also the random error caused by NLOS propagation. Due to the obstruction of obstacles, the transmission channel between the base station and the target is in a LOS environment, NLOS environment, or a mixed LOS / NLOS environment for some time periods, and the distribution of the target motion relative to the environmental information of each base station is random.

[0086] Figure 7 Tracking trajectories for three algorithms in mixed LOS / NLOS environments. Figure 8 This is a comparison chart of the horizontal and vertical coordinate errors of the method of the present invention and pure TOF positioning when the NLOS error obeys an exponential distribution with a mean of 1 in a LOS / NLOS mixed environment. If the NLOS error obeys an exponential distribution, the tracking trajectory of the TOF-RSS joint tracking algorithm is more consistent with the true trajectory, and its positioning accuracy is significantly higher than that of the pure TOF tracking algorithm. At the same time, the convergence speed is faster, and the positioning and tracking performance is more stable.

[0087] Fig. 9 This is an empirical CDF comparison diagram of the horizontal and vertical coordinate positioning deviations of the method of the present invention and pure TOF positioning in one test in a LOS / NLOS mixed environment. It can be seen that the performance of the TOF-RSS joint tracking algorithm is significantly better than that of the pure TOF positioning algorithm.

[0088] Fig.10 This is a comparison chart of the mean deviations of the horizontal and vertical coordinates of the method of the present invention and the TOF positioning method in five tests in a mixed LOS / NLOS environment. If the NLOS error is a Gaussian noise with a non-zero mean and follows the N(3,4) distribution, five groups of measurement experiments are performed on the same trajectory. Compared with the TOF positioning algorithm alone, the TOF-RSS joint tracking algorithm has more stable deviations in both the X and Y directions, so it has better noise resistance, stronger robustness and higher tracking accuracy.

[0089] Fig.11 It is the STD value of the deviation in each direction of the method of the present invention and the pure TOF positioning when the NLOS error obeys the N(3,4) distribution in the LOS / NLOS mixed environment. Five groups of measurement experiments were carried out under the same trajectory. It can be seen that the STD mean of the TOF-RSS joint tracking algorithm at each epoch is smaller than that of the pure TOF positioning algorithm. The STD mean comparison is shown in Table 2. Compared with the pure TOF positioning algorithm, the TOF-RSS joint tracking algorithm has a smaller STD mean in the X and Y directions, is relatively stable, and has better noise resistance.

[0090] Table 1 STD mean of 5 experiments

[0091] TOF-RSS TOF improve Deviation in X direction 0.2675m 0.6819m 60.77％ Y direction deviation 0.1113m 0.2298m 51.56％

[0092] Further references Fig.12 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a UWB base station positioning device in a LOS / NLOS environment. These device embodiments are similar to Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0093] like Fig.12 As shown, the UWB positioning device 300 of some embodiments includes: an acquisition unit 301, a primary multi-channel positioning unit 302 and a determination unit 303. Among them, the acquisition unit 301 is configured to acquire TOF distance information and RSS information between the target device and the reference base station in response to detecting that the target device has established a connection with the reference base station; the primary multi-channel positioning unit 302 is configured to perform a local optimal estimation calculation of the position state of the above-mentioned target device in the sub-filter according to the above-mentioned TOF distance information and RSS information; the determination unit 303 is configured to determine the global optimal estimated position result of the target device according to the above-mentioned local optimal estimation result and the secondary filter.

[0094] It is understood that the units described in the device 300 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 300 and the units contained therein, and will not be described in detail here.

[0095] Reference below Fig.13 , which shows a structural schematic diagram of an electronic device 400 suitable for implementing some embodiments of the present disclosure. Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0096] like Fig.13 As shown, the electronic device 400 may include a processing device (such as a central processing unit, a graphics processor, etc.)

[0097] 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage device 408 to the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0098] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 4 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0099] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0100] It should be noted that the computer-readable medium described above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0101] In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0102] The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0103] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0104] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to detecting that the signal tag establishes a connection with the target base station, obtains the distance information between the signal tag and the target base station; performs coarse positioning of the target base station and the signal tag according to the distance information to obtain a coarse position set; determines the target base station position and the signal tag position according to the coarse position set and the distance information.

[0105] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0106] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor includes an acquisition unit, a positioning unit, and a determination unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the receiving unit may also be described as "a unit that acquires the distance information between the above-mentioned signal tag and the above-mentioned target base station in response to detecting that the signal tag establishes a connection with the target base station."

[0108] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0109] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for determining the location of a UWB device in a LOS / NLOS environment, characterized in that: When the target moves within the monitoring area of ​​m base stations, the base station determines the state vector of the target movement, including the following steps: Step 1: Obtain the base station's measurement information of the target through TOF and RSS, and fuse the two measurement information. Let the fused measurement vector at time k be Z k ;TOF stands for time of flight, RSS stands for received signal strength; Step 2: Perform a first-level filtering calculation on the fused measurement information; the first-level filtering sets J sub-filters, where the number of EKF filters is N, the number of EHF filters is JN, and the input of the first-level filtering is the measurement vector Z at time k k , the optimal state estimate at time k-1 and the posterior estimated covariance matrix For N EKF filters, set the state transfer matrix of different models, and set the noise covariance matrix and process noise to use noise matrices of different sizes as the projection of the LOS environment; for JN EHF filters, set the damping factor and process noise coefficient matrix of different sizes as the projection of the NLOS environment; set the optimal state estimate at J k moments output by J sub-filters and the posterior estimated covariance matrix EKF stands for Extended Kalman Filter, and EHF stands for Extended H ∞ Filtering; Step 3: Perform secondary filtering calculation on the result of the primary filtering output; the secondary filtering is performed in the federated filter based on the output of each sub-filter in the primary filtering. and Perform overall data fusion to obtain the global optimal positioning estimation result of the target; the secondary filtering first The matrix P(k) is normalized, and the eigenvalues ​​of the matrix P"(k) corresponding to each sub-filter of the first-level filtering are calculated. The weight factors of each sub-filter in the second-level filtering are calculated according to the eigenvalues. Finally, the federal filter calculates the optimal positioning estimation result of the target at the current time k based on the weight factors.

2. The method according to claim 1, characterized in that The step 2 comprises: Let the measurement vector at time k be H k,TOF and H k,RSS The measurement information of the base station on the target obtained by TOF and RSS respectively, and the measurement matrix G k =[H k,TOF H k,RSS ] T ; is the prior estimate of the motion state vector of the target at time k; ε is the noise vector; (21) and Substituting into the 1st to Nth EKF filters respectively, the EKF filter is calculated as follows: in, is the prior estimated covariance matrix at time k, F k-1 is the state transfer matrix from time k-1 to time k, Q k-1 is the noise covariance matrix, B k-1 represents the noise Jacobian matrix at time k-1, and the superscript T represents the transpose; The calculation equation of the EKF correction update phase is: Among them, K k is the gain matrix, R k is the measurement noise matrix, C k is the measurement noise Jacobian matrix, is the posterior estimated state vector at time k, is the posterior estimated covariance matrix at time k, I is the unit matrix, and the superscript -1 indicates the inverse matrix. The above calculation yields the LOS submodel under the action of EKF. and (2.2) Let the process noise matrix w k and measurement noise v k The energy is bounded. When the damping factor γ is given, the calculation from the N+1th to the Jth EHF filter is as follows: Among them, L k is the coefficient matrix, S k is the EHF gain matrix, M is the process noise coefficient matrix, G k-1 , R k-1 are the measurement matrix and measurement noise matrix at time k-1 respectively; Z k-1 is the fusion measurement vector at time k-1; the above calculation shows that the NLOS sub-model under the action of EHF and 3. The method according to claim 1, characterized in that The step three comprises: (31) The matrix P(k) at time k is normalized as follows: Where P(0) represents the initial value of P(k), m is the dimension of P(k), and Tr(·) is the matrix trace; (32) The eigenvalue of the matrix P" (k) corresponding to the j-th sub-filter is calculated as The weight factor of the j-th sub-filter in the secondary filtering is calculated based on the eigenvalue as follows: in, is the factor corresponding to the i-th eigenvalue of the j-th sub-filter, κ j is the error distribution coefficient matrix of the jth sub-filter; all J sub-filters satisfy: (3) The federated filter calculates the optimal positioning estimate of the target at the current k moment as follows: Among them, P g,k-1 is the system overall error covariance matrix at time k-1, P i,k is the error covariance matrix of the i-th filter at time k, is the optimal state estimate of the i-th filter at time k; Update the overall system error covariance matrix P at time k g,k as follows: Update the error covariance matrix of the i-th filter at time k 4. A UWB positioning device in a LOS / NLOS environment, characterized in that: The device includes: an acquisition unit, configured to acquire TOF distance information and RSS information between the target device and the reference base station in response to detecting that the target device establishes a connection with the reference base station; A first-level multi-channel positioning unit, configured to perform the first-level filtering calculation in the method according to claim 1, and perform a local optimal estimation calculation of the position state of the target device in the sub-filter according to the TOF distance information and the RSS information; The determination unit is configured to perform the secondary filtering calculation in the method as claimed in claim 1, and determine the global optimal estimated position result of the target device according to the local optimal estimation result output by the primary multi-channel positioning unit and the secondary filter.

5. An electronic device, comprising: one or more processors; A storage device for storing one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 3.

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