A method and device for determining the position of a UWB device in a LOS / NLOS environment, and an electronic device

Abstract

The present invention discloses a method and apparatus for determining the position of a UWB device in a LOS / NLOS environment, and an electronic device, and belongs to the field of computer technology. The method comprises: in response to detecting that a target device has established a connection with a reference base station, obtaining the TOF and RSS information of the target device and the reference base station; in the sub-filter of the first-level filtering channel, calculating the local optimal estimate of the position state of each sub-filter in all channels based on the TOF and RSS information; and determining the position of the target device in the second-level filtering part based on the local optimal estimation results of all sub-filters. The present invention can adapt to changes in the LOS / NLOS environment without the need for NLOS identification 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, and the calculation time is comparable to that of simple TOF measurement and tracking.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a method and apparatus 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] Due to their strong penetration and anti-interference properties, UWB signals are gradually becoming the mainstream technology for high-precision indoor positioning. However, the positioning performance of UWB wireless ad hoc networks varies significantly in different signal propagation environments. For example, in indoor non-line-of-sight (NLOS) environments, the positioning accuracy of UWB target devices based on parameters such as TOF (time of flight) or RSS (received signal strength) is reduced due to the introduction of NLOS errors. This significantly limits the implementation of high-precision positioning and target tracking tasks in complex LOS / NLOS environments.

[0003] With the currently known technologies, conventional positioning algorithms based on a single parameter are difficult to adapt to various environments. For example, 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 environments [D]. Jiangsu Province: Nanjing University of Posts and Telecommunications, 2023.) proposes a suppression algorithm that improves the traditional quadratic programming algorithm to address the problem of non-line-of-sight errors in indoor positioning. The algorithm identifies and classifies line-of-sight and non-line-of-sight data, and uses the classified data to perform positioning using the proposed residual weighting 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. Two main algorithms are proposed: 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, given the ever-changing LOS and NLOS environments in real-world environments, real-time NLOS identification and error correction are extremely difficult, limiting the practical application of mainstream NLOS mitigation solutions. Therefore, robust target tracking and localization in complex LOS / NLOS scenarios remains an unresolved technical challenge. Summary of the Invention

[0004] In response to the current technical problem of requiring high robustness in target tracking and positioning 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 identification 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 and the number of EHF sub-filters is JN. 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; suppose that the optimal state estimates of J k moments are 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 federal filter based on the output of each sub-filter in the primary filtering. and Perform overall data fusion to obtain the target global optimal positioning estimation result; 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 based on 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, including: 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, and 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 based on 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, and the determination unit is configured to determine the global optimal estimated position result of the target device based on 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; and when the one or more programs are executed by 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 implements the method described in the first aspect when executed by a processor.

[0012] Compared with the existing technology, the present invention has the following advantages: after the target device and the reference base station establish communication, the method, device, and electronic device of the present invention can achieve high positioning accuracy and good robustness in the LOS / NLOS environment without the need for NLOS identification. The present invention fuses the TOF and RSS measurements and uses two filters to work in parallel, namely, the extended Kalman filter (EKF) and the extended H ∞ The filtering (EHF) approximates the LOS and NLOS propagation environments, and then adopts a two-stage filtering structure to achieve adaptation to complex LOS / NLOS scenarios without the need for NLOS identification and error correction. In view of the shortcomings of the EKF effect relying on the refinement of the model and the EHF effect relying on the adjustment of the damping factor parameters, the present invention adopts a multi-channel method of multi-domain projection, calculates the local optimal estimation results in multiple spaces, and then uses two-stage filtering to perform a global optimal estimation solution, effectively alleviating 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 no new hardware equipment needs to be added, 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 This is a schematic diagram showing 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 target moving at a uniform speed along a straight line;

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

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

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

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

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

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

[0021] Figure 9 This is an empirical CDF comparison chart 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] Figure 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 alone in five tests in a mixed LOS / NLOS environment;

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

[0024] Figure 12 2 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] Figure 13 It is a schematic structural diagram 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 target's velocity in the x, y, and z directions. The superscript T indicates transposition. The target's state equation is as follows:

[0030]

[0031] Where, 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 transition 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 ∞ A two-stage weighted robust tracking algorithm based on Kalman filter and extended Kalman filter is proposed. The overall principle is as follows: In this example, J different sub-filters are set, including N EKF sub-filters and JN EHF sub-filters. The global optimal estimated state result and covariance matrix 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 estimated state result and covariance matrix; the local optimal estimated state results and covariance matrices calculated by all sub-filters are then input into the second-stage federated filter for information fusion. A weighted calculation of the federated filter is performed based on the observability to obtain the global optimal estimated state result at time k.

[0033] A method for determining the location 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] Where y k,m is the mth base station BS m The TOF observation distance between the tracking 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, obeying 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; d0 is the reference distance; PL(d0) is the signal strength at the transmitting node, which is generally obtained from the hardware specification 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 by expanding the measurement dimension, 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 equations (4) and (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 in Figure 1, 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 set to N, and the number of EHF sub-filters is set to 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 sub-model under the action of EKF

[0054] F of multiple EKF sub-filters k-1 The matrix is ​​substituted using models of 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] The input of this part is multiple calculated by J sub-filters of the first-level filtering and This part performs overall data fusion based on the error covariance matrix of each sub-filter and the optimal state estimate in the federated filter 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 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 dimensionality 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, let the eigenvalue of the P"(k) matrix corresponding to the j-th sub-filter be The weight factors of the sub-filters in the secondary filtering based on the eigenvalue calculation are 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 distribution coefficient matrix of the jth sub-filter. All J sub-filters meet the following conditions:

[0067]

[0068] Then, the distributed preliminary fusion results are read to construct the error covariance matrix of the federated filter and the entire system, and the coefficients are allocated in real time based on the calculation results of the error allocation coefficient matrix. 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, namely the extended Kalman filter and the extended H ∞ The filtering approximates the LOS and NLOS propagation environments, and then performs federated filtering based on the observability of the approximation results of each sub-filter to obtain the global optimal estimate. 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 parameters hybrid depends on the accuracy requirements and complexity constraints.

[0075] The degree of refinement and accuracy of the state equation transfer model and 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, Wi-Fi, 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 a target moving at a constant speed along a straight line, the measurement noise is assumed to be white Gaussian noise in the LOS scenario. For the NLOS scenario, the ranging error noise is set to exponential and non-zero mean Gaussian distributions, respectively. The proposed method (TOF-RSS) was compared with tracking algorithms based on EHF and EKF under TOA measurement alone. After five Monte Carlo simulations, the average standard deviation (STD) was used as the accuracy evaluation metric.

[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 This is a comparison chart of the mean deviations of the horizontal and vertical axes of five experiments. Five groups of experiments were conducted on the same trajectory. Compared with the pure TOF positioning algorithm, the TOF-RSS joint tracking algorithm has more stable deviations in the X and Y directions, and therefore has better noise resistance.

[0084] Figure 6 The figure shows a comparison of the standard deviations of the horizontal and vertical axes of five experiments. Five groups of experiments were conducted on the same trajectory. Compared with the pure TOF positioning algorithm, the TOF-RSS joint tracking algorithm has smaller standard deviations in the X and Y directions and is relatively stable, so it has better noise resistance.

[0085] In mixed LOS / NLOS environments, measurement errors include not only standard measurement errors but also random errors caused by NLOS propagation. Due to obstructions, the transmission channel between the base station and the target may be in LOS, NLOS, or mixed LOS / NLOS environments for some periods of time. The distribution of target motion relative to each base station's environmental information is random.

[0086] Figure 7 Tracking trajectories for three algorithms in mixed LOS / NLOS environments. Figure 8 This figure compares the horizontal and vertical coordinate errors of the proposed method and TOF positioning alone in a mixed LOS / NLOS environment, when the NLOS error follows an exponential distribution with a mean of 1. When the NLOS error follows an exponential distribution, the TOF-RSS combined tracking algorithm tracks a trajectory that more closely matches the true trajectory, achieving significantly higher positioning accuracy than the TOF tracking algorithm alone, while also converging faster and providing more stable positioning and tracking performance.

[0087] Figure 9 This is an empirical CDF comparison diagram of the horizontal and vertical coordinate positioning deviations of the method of the present invention and simple 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 simple TOF positioning algorithm.

[0088] Figure 10 This figure compares the mean deviations of the horizontal and vertical coordinates of the proposed method and TOF positioning alone, from five experiments in a mixed LOS / NLOS environment. If the NLOS error is Gaussian noise with a non-zero mean and follows an N(3,4) distribution, five measurement experiments conducted on the same trajectory show that the TOF-RSS joint tracking algorithm has more stable deviations in both the X and Y directions compared to the TOF positioning algorithm alone, resulting in better noise immunity, greater robustness, and higher tracking accuracy.

[0089] Figure 11 The STD values ​​for the deviations in each direction between the proposed method and TOF positioning alone, when the NLOS error follows an N(3,4) distribution in a mixed LOS / NLOS environment. Five measurement experiments were conducted on the same trajectory. The mean STD values ​​for the TOF-RSS combined tracking algorithm are smaller than those for the TOF positioning algorithm alone at each epoch. A comparison of the mean STD values ​​is shown in Table 2. Compared to the TOF positioning algorithm alone, the TOF-RSS combined tracking algorithm achieves smaller mean STD values ​​in both the X and Y directions, is more stable, and exhibits better noise immunity.

[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 Figure 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 Figure 12 As shown, the UWB positioning device 300 of some embodiments includes: an acquisition unit 301, a first-level multi-channel positioning unit 302, and a determination unit 303. The acquisition unit 301 is configured to, in response to detecting that the target device has established a connection with the reference base station, acquire TOF distance information and RSS information between the target device and the reference base station; the first-level multi-channel positioning unit 302 is configured to perform a local optimal estimation calculation of the position state of the target device in the sub-filter based on the TOF distance information and RSS information; and the determination unit 303 is configured to determine the global optimal estimated position result of the target device based on the local optimal estimation result and the second-level filter.

[0094] It is understood that the units described in the device 300 are similar to those 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 included therein, and will not be repeated here.

[0095] Reference below Figure 13 , which shows a structural diagram of an electronic device 400 suitable for implementing some embodiments of the present disclosure. Figure 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 Figure 13 As shown, the electronic device 400 may include a processing device (such as a central processing unit, a graphics processing unit, 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. Various programs and data required for the operation of the electronic device 400 are also stored in the RAM 403. The processing device 401, ROM 402, and RAM 403 are connected to each other via 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 touchpad, 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 of 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 needed.

[0099] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via 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 performed.

[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 component, 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 can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0102] A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport 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 thereof.

[0103] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can 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 may exist independently and not incorporated into the electronic device. The computer-readable medium carries one or more programs. When executed by the electronic device, the electronic device: in response to detecting that the signal tag has established a connection with the target base station, obtains distance information between the signal tag and the target base station; performs coarse positioning of the target base station and the signal tag based on the distance information to obtain a coarse position set; and determines the target base station position and the signal tag position based on 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 stand-alone 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 through 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., through the Internet using an Internet service provider).

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the 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 in software or in hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising an acquisition unit, a positioning unit, and a determination unit. The names of these units do not, in some cases, constitute a limitation on the units themselves. 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, and 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 chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0109] The above description is only an illustration of some preferred embodiments of the present disclosure and 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 the 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 (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for determining the location of a UWB device in a LOS / NLOS environment, characterized in that: When a target moves within the monitoring area of ​​m base stations, the base station determines the state vector of the target motion, 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 fusion 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 and the number of EHF filters is JN. 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 using 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; let the optimal state estimate of J k moments output by J sub-filters be and the posterior estimated covariance matrix EKF stands for Extended Kalman Filter, EHF stands for Extended H ∞ filtering; The second step includes: setting the measurement vector at time k 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 Substitute into the 1st to Nth EKF filters respectively, and the EKF filter calculation is as follows: in, is the prior estimated covariance matrix at time k, F k-1 is the state transition 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 identity matrix, and the superscript -1 indicates the inverse matrix; the above calculation yields the LOS sub-model under the action of EKF and (22) Let the process noise matrix W k and measurement noise v k The energy is bounded. When the damping factor γ is given, the energy from the N+1th to the Jth EHF filter is calculated 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 results show that the NLOS sub-model under the action of EHF is and Step 3: Perform secondary filtering calculation on the result of the primary filtering output; the secondary filtering is performed in the federal filter based on the output of each sub-filter in the primary filtering. and Perform overall data fusion to obtain the target global optimal positioning estimation result; 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 based on 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 three includes: (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 estimation result 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 system overall error covariance matrix P at time k g,k as follows: Update the error covariance matrix of the i-th filter at time k 3. A UWB positioning device in LOS / NLOS environment, characterized in that: The device includes: an acquiring 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 according to 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.

4. 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 2.

5. A computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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