Target tracking method and device, storage medium and program product

Abstract

The invention provides a target tracking method and device, a storage medium and a program product, and relates to the technical field of network communication. The method comprises the following steps: processing a LoRa signal received by each gateway by using a linear frequency modulation symbol to obtain a multi-channel demodulation symbol; for each gateway, based on multichannel demodulation symbols, symbol-level features corresponding to peak symbols in each channel are extracted, and a signal receiving matrix is formed; according to the signal receiving matrix, obtaining an angle of arrival from the LoRa signal to a gateway receiving the LoRa signal; predicting a target position based on the arrival angles corresponding to the plurality of gateways; and obtaining a target track according to the plurality of target positions in the preset time period.

Description

Technical Field

[0001] The present disclosure relates to the field of network communication technology, and in particular to a target tracking method, device, storage medium and program product. Background Art

[0002] With the development of the Internet of Things, various wireless communication technologies have been introduced in various fields. Based on the characteristics of long distance and low power consumption, low-power wide area network technology has become more and more popular in recent years. As the most representative low-power wide area network technology, LoRa (Long Range Radio) has been widely used in various fields including environmental monitoring, smart industry, smart agriculture and smart home. 25.6 billion Semtech nodes with LoRa and no less than 4.2 million gateways have been deployed worldwide. This dense deployment also brings challenges in managing and maintaining these devices, especially the positioning and tracking of deployed LoRa devices. Summary of the invention

[0003] A technical problem to be solved by the present disclosure is to provide a target tracking method, device, storage medium and program product, which can improve the accuracy of target tracking.

[0004] According to one aspect of the present disclosure, a target tracking method is proposed, including: using linear frequency modulation symbols to process the long-distance radio LoRa signal received by each gateway to obtain multi-channel demodulation symbols; for each gateway, based on the multi-channel demodulation symbols, extracting the symbol-level features corresponding to the peak symbols in each channel to form a signal reception matrix; according to the signal reception matrix, obtaining the arrival angle of the LoRa signal to the gateway receiving the LoRa signal; based on the arrival angles corresponding to multiple gateways, predicting the target position; and obtaining the target trajectory according to multiple target positions within a predetermined time period.

[0005] In some embodiments, using linear frequency modulation symbols to process the long-distance radio LoRa signal received by each gateway to obtain multi-channel demodulation symbols includes: using linear frequency modulation symbols to divide the preamble code of the LoRa signal into multiple adjacent channels to obtain multi-channel demodulation symbols.

[0006] In some embodiments, based on multi-channel demodulation symbols, symbol-level features corresponding to peak symbols in each channel are extracted to form a signal receiving matrix, including: according to the number of antennas of the gateway receiving the LoRa signal and the number of virtual channels corresponding to the linear frequency modulation symbols, phase features and amplitude features corresponding to the peak symbols in each channel in the multi-channel demodulation symbols are extracted to form a signal receiving matrix.

[0007] In some embodiments, obtaining the angle of arrival of the LoRa signal to the gateway receiving the LoRa signal according to the signal receiving matrix includes: spatially smoothing the signal receiving matrix to obtain a singular matrix; based on the singular matrix, using a multi-signal classification algorithm to obtain the angle of arrival of the LoRa signal to the gateway receiving the LoRa signal.

[0008] In some embodiments, predicting the target location based on the arrival angles corresponding to multiple gateways includes: calculating the coordinate information of the intersection point generated by any two gateways based on the arrival angle of the LoRa signal to the gateway receiving the LoRa signal; and fusing multiple coordinate information to predict the target location.

[0009] In some embodiments, multiple coordinate information is integrated to predict the target location, including: calculating the weight of the intersection generated by any two gateways based on the frequency coefficient of each gateway; and performing weighted average calculation on multiple coordinate information based on the weight of the intersection generated by any two gateways to obtain the target location.

[0010] In some embodiments, obtaining the target trajectory according to the multiple target positions within the predetermined time period includes: performing iterative trajectory correction through Kalman filtering based on the continuous characteristics of the multiple target positions within the predetermined time period in the time domain to obtain the target trajectory.

[0011] According to another aspect of the present disclosure, a target tracking device is also proposed, including: a channel division unit, configured to use linear frequency modulation symbols to process the long-distance radio LoRa signal received by each gateway to obtain multi-channel demodulation symbols, and for each gateway, based on the multi-channel demodulation symbols, extract the symbol-level features corresponding to the peak symbols in each channel to form a signal reception matrix; an arrival angle estimation unit, configured to obtain the arrival angle of the LoRa signal to the gateway receiving the LoRa signal according to the signal reception matrix; a multi-gateway information fusion unit, configured to predict the target position based on the arrival angles corresponding to multiple gateways; a trajectory tracking unit, configured to obtain the target trajectory according to multiple target positions within a predetermined time period.

[0012] According to another aspect of the present disclosure, a target tracking device is also proposed, including: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the target tracking method as described above based on instructions stored in the memory.

[0013] According to another aspect of the present disclosure, a computer-readable storage medium is further provided, on which computer program instructions are stored, and when the instructions are executed by a processor, the target tracking method as described above is implemented.

[0014] According to another aspect of the present disclosure, a computer program product is also proposed, including a computer program or instructions, and the computer program or instructions implement the above-mentioned target tracking method when executed by a processor.

[0015] In the disclosed embodiment, strict synchronization constraints between multiple gateways are not required. By utilizing the characteristics of the linear spread spectrum modulation method of the LoRa technology itself, without the need to modify the hardware and software of the transmitter, only the normally transmitted signal is processed at the receiving end, and the channel is divided using a specially designed linear modulation symbol. This can achieve target positioning, improve the accuracy of target trajectory prediction, and enhance the possibility of actual deployment of the system.

[0016] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0018] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0019] Figure 1 Schematic diagram of the flow of some embodiments of the target tracking method disclosed in the present invention;

[0020] Figure 2 Schematic diagram of the flow chart of other embodiments of the target tracking method disclosed in the present invention;

[0021] Figure 3A Schematic diagram of some embodiments of the preamble code of the LoRa signal disclosed in the present invention;

[0022] Figure 3B Schematic diagram of some embodiments of the linear frequency modulation symbol disclosed in the present invention;

[0023] Figure 3C Schematic diagram of some embodiments of multi-channel demodulation symbols disclosed in the present invention;

[0024] Figure 4 It is a schematic diagram of some embodiments of multi-gateway information fusion disclosed in the present invention;

[0025] Figure 5 Schematic diagrams of some embodiments of target trajectories disclosed herein;

[0026] Figure 6 Schematic diagram of the flow chart of other embodiments of the target tracking method disclosed in the present invention;

[0027] Figure 7It is a schematic diagram of the structure of some embodiments of the target tracking device disclosed in the present invention;

[0028] Figure 8 Schematic diagrams of the structures of other embodiments of the target tracking device disclosed in the present invention. DETAILED DESCRIPTION

[0029] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0030] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0031] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0032] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0033] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0034] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0035] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0036] LoRa is a low-power local area network wireless standard. Its biggest feature is that it can transmit farther than other wireless methods under the same power consumption conditions, achieving the unity of low power consumption and long distance. It can extend the communication distance 3-5 times that of traditional wireless RF under the same power consumption.

[0037] There are three main types of wireless target positioning technologies: those based on received signal strength information, those based on arrival time difference, and those based on arrival angle. The related technologies can generally only provide very rough location information. For example, the methods based on signal reception strength and triangulation positioning can only provide location information services with an error accuracy of about 200m, which is difficult to meet the positioning requirements of upper-layer applications.

[0038] In the long-distance scenario of low-power wide area network, how to achieve high-precision positioning of the target will face more complex tasks and challenges: on the one hand, LoRa signals have the characteristics of long-distance transmission, and the signals at the receiving end usually have a low signal-to-noise ratio. In the general arrival angle estimation method, the signal-to-noise ratio will affect the estimation of the signal subspace, resulting in obvious errors in the arrival angle estimation results; on the other hand, in outdoor long-distance communication scenarios, multipath effects are prevalent, which leads to an increase in reflection paths and significant differences in distances between different paths, resulting in an inability to accurately estimate the arrival angle; finally, each gateway generates an estimated value of the target arrival angle instead of the final positioning coordinate information. Different gateway incident angles will produce multiple predicted intersections. How to fuse multiple signal features from the physical layer and match the acquired signal features with the intersection information to accurately predict the target position is a challenging task.

[0039] The present disclosure provides a target tracking method, which is applicable to long-distance target tracking in a low-power wide area network and improves the accuracy of target tracking. The solution of the present disclosure will be introduced below by taking a specific embodiment as an example.

[0040] Figure 1 1 is a flowchart of some embodiments of the target tracking method disclosed in the present invention, and the embodiment includes steps S11-S15.

[0041] In step S11, the LoRa signal received by each gateway is processed using linear frequency modulation symbols to obtain multi-channel demodulation symbols.

[0042] In some embodiments, the preamble code of the LoRa signal is divided into multiple adjacent channels using linear frequency modulation symbols to obtain multi-channel demodulation symbols.

[0043] For example, for physical layer LoRa signals received by different gateways, a specially constructed linear frequency modulation symbol is used to divide a single preamble code into multiple adjacent channels to obtain multi-channel demodulation symbols.

[0044] Since the signal is distorted after long-distance transmission, its phase characteristics at a single chip level are unreliable. In this step, by taking advantage of the characteristics of LoRa long-distance transmission, additional processing gain is obtained by multiplying with the local linear frequency modulation symbol, thereby improving the signal-to-noise ratio and thus improving the reliability of the LoRa signal in the phase domain. In addition, using the linear frequency modulation symbol to process the LoRa signal can avoid introducing additional phase differences, ensuring the accuracy of the original phase difference while generating a virtual equidistant uniform antenna array, achieving a more accurate estimation of the signal flight time.

[0045] In step S12, for each gateway, based on the multi-channel demodulation symbols, the symbol-level features corresponding to the peak symbols in each channel are extracted to form a signal reception matrix.

[0046] In this step, the multi-band symbol-level phase features within the multi-channel demodulation symbols are extracted to construct a preliminary signal reception matrix.

[0047] In step S13, the arrival angle of the LoRa signal to the gateway receiving the LoRa signal is obtained according to the signal reception matrix.

[0048] For example, the signal receiving matrix is ​​reconstructed by reducing row vectors and increasing column vectors to make it tend to a square matrix, constructing the signal subspace matrix and the noise subspace matrix, and performing independent operations on the data received by each gateway to obtain the arrival angle of the target and different gateways.

[0049] In step S14, the target location is predicted based on the arrival angles corresponding to the multiple gateways.

[0050] For example, the arrival angle information of different gateways is fused, and different weights are given according to the differences in spectral coefficients caused by the differences in the distance from the target to the gateway. Different weights are assigned to the corresponding intersections, and the final positioning result is obtained through comprehensive calculation.

[0051] In step S15, a target trajectory is obtained according to a plurality of target positions within a predetermined time period.

[0052] For example, based on the continuous characteristics of multiple target positions in the time domain within a predetermined time period, iterative trajectory correction is performed through Kalman filtering to obtain the target trajectory.

[0053] For another example, multiple target positions within a period of time are connected to obtain a target trajectory.

[0054] In the above embodiment, strict synchronization constraints between multiple gateways are not required. By utilizing the characteristics of the linear spread spectrum modulation method of the LoRa technology itself, without the need to modify the hardware and software of the transmitter, only the normally transmitted signal is processed at the receiving end, and the channel is divided using a specially designed linear modulation symbol. This can achieve target positioning, improve the accuracy of target trajectory prediction, and enhance the possibility of actual deployment of the system.

[0055] Figure 2 1 is a flowchart of some other embodiments of the target tracking method disclosed in the present invention, and the embodiment includes steps S111-S151.

[0056] In step S111, the preamble code of the LoRa signal is divided into multiple adjacent channels using linear frequency modulation symbols to obtain multi-channel demodulation symbols.

[0057] For example, for a single gateway, the LoRa signal received by the gateway is in the form of ,in, is the slope of the linear frequency modulation signal, is the bandwidth of the LoRa signal, is the carrier frequency, is the sampling point number.

[0058] The present disclosure designs a special linear frequency modulation symbol, which can also be called a spread spectrum symbol. The linear frequency modulation symbol is, for example, ,in, is the number of virtual channels to be generated.

[0059] In some embodiments, the preamble code of the LoRa signal is multiplied by the linear frequency modulation symbol to obtain a multi-channel demodulation symbol. Figures 3A-3C As shown, Figure 3A is the preamble code of the LoRa signal, Figure 3B is the linear frequency modulation symbol, Figure 3C is a multi-channel demodulation symbol.

[0060] In this step, the preamble is divided into different channels using linear frequency modulation symbols. By splitting the preamble of a single channel, the number of elements in the multi-signal classification algorithm can be effectively increased, thereby improving the system's ability to resist multipath effects.

[0061] The above step S111 is an implementation method of step S11.

[0062] In step S121, according to the number of antennas of the gateway receiving the LoRa signal and the number of virtual channels corresponding to the linear frequency modulation symbols, the phase characteristics and amplitude characteristics corresponding to the peak symbols in each channel of the multi-channel demodulation symbols are extracted to form a signal receiving matrix.

[0063] For example, a gateway has two antennas, and the number of virtual channels corresponding to the linear frequency modulation symbol is 8, then the signal receiving matrix is , wherein the horizontal axis is the number of virtual channels and the vertical axis is the number of actual receiving antennas. Those skilled in the art should understand that the number of virtual channels and the number of antennas here are only used as examples and can be set according to actual conditions.

[0064] In this step, by extracting the phase and amplitude features at the symbol level, the reliability of the phase in the case of low signal-to-noise ratio can be improved.

[0065] The above step S121 is an implementation method of step S12.

[0066] In step S131, spatial smoothing reconstruction is performed on the signal receiving matrix to obtain a singular matrix.

[0067] For example, the original signal receiving matrix is ​​a 2*8 matrix. After spatial smoothing reconstruction of the signal receiving matrix, the singular matrix is ​​obtained as .

[0068] The singular matrix is ​​a 6*8 matrix. The 6*6 matrix is ​​to achieve the full rank of the receiving matrix. Currently, it can also be constructed as a square matrix with the number of rows and columns.

[0069] In this step, the inevitable multipath problem in actual communication can be solved by reconstructing the signal receiving matrix. The same signal is reflected through different paths and produces a delay effect at the receiving end. However, the signals in different time domains of the original signal receiving matrix are coherent and cannot be effectively decomposed in space. The spatial smoothing operation sacrifices the effective aperture and converts the signal receiving matrix into a singular matrix to solve the multipath problem.

[0070] In step S132, based on the singular matrix, a multi-signal classification algorithm is used to obtain the arrival angle of the LoRa signal to the gateway receiving the LoRa signal.

[0071] In some embodiments, the relative position of the target and the current gateway is estimated by decomposing the singular matrix into different subspaces. For example, the singular matrix is ​​eigen-decomposed into a signal subspace and a noise subspace, and the orthogonal characteristics of the two are used to traverse all angles and calculate the arrival angle when the correlation is the smallest.

[0072] For example, the multi-signal classification algorithm formula is ,in, is the traversal angle, and for The corresponding signal subspace and the transpose of the signal subspace, and for The corresponding noise subspace and the transpose of the noise subspace. Both the signal subspace and the noise subspace are obtained based on the singular matrix. is the calculated arrival angle.

[0073] The above steps S131 and S132 are one implementation of step S13.

[0074] In step S141, based on the arrival angle of the LoRa signal to the gateway receiving the LoRa signal, the coordinate information of the intersection point generated by any two gateways is calculated, and multiple coordinate information is fused to predict the target position.

[0075] After each receiving end obtains the arrival angle information, the intersection of the arrival angles of multiple gateways will generate multiple target prediction positioning points. Then, a weight-based positioning algorithm is used to fuse the information received by multiple gateways to improve the accuracy of the target location.

[0076] In some embodiments, the weight of the intersection point generated by any two gateways is calculated according to the frequency coefficient of each gateway; and based on the weight of the intersection point generated by any two gateways, a weighted average calculation is performed on multiple coordinate information to obtain the target position.

[0077] For example, the entire received preamble is multiplied by the locally generated standard frequency modulation symbol, and the spectrum coefficient is obtained by Fourier transform calculation as a weight, and multiple potential predicted positioning points are fused and calculated to obtain the final positioning result.

[0078] The preamble demodulation formula is: , the frequency coefficient of the demodulated signal is ,in, is the maximum symbol value that can be modulated, and its value is , is the spreading factor.

[0079] An intersection is generated by calculating the arrival angle of two gateways, and the weight corresponding to the intersection is generated by the two gateways. Therefore, the weight of the intersection generated by the two gateways is ,in, and Represents two gateways, is the spectral coefficient of the gateway.

[0080] The weight fusion is used to generate the tracking positioning result. ,in, for and The two gateways generate the coordinate information of the intersection. Generates the weight of the intersection between two gateways.

[0081] like Figure 4 As shown, 41 is a gateway, 42 represents an estimated arrival angle, 43 is a target position, 44 is a cross point, and 45 is a predicted target position.

[0082] In this step, the positioning method based on the arrival angle eliminates the need for additional synchronization source equipment based on the arrival time difference. When cooperating with other gateways to improve positioning accuracy, strict time synchronization is not required, thereby saving the deployment overhead of synchronization equipment.

[0083] The above step S141 is an implementation method of step S14.

[0084] In step S151, based on the continuous characteristics of multiple target positions in the time domain within a predetermined time period, iterative trajectory correction is performed through Kalman filtering to obtain the target trajectory.

[0085] For example, a discrete positioning result obtained after multi-gateway information fusion is combined with the characteristics of the target trajectory and time domain, and the trajectory is iteratively corrected through Kalman filtering to minimize the total loss function, thereby further improving the final trajectory tracking accuracy based on target positioning.

[0086] like Figure 5 As shown, Figure 5 Schematic diagram of some embodiments of the target trajectory of the present invention, 51 and 52 are key points, for example, 51 is the starting point, 52 is the end point, 53 represented by a triangle is the predicted position, 54 represented by a square is the position after Kalman filtering, the continuous predicted positions are the predicted trajectory, and the line connecting the positions after Kalman filtering is the target trajectory.

[0087] In this step, according to the predicted position information of the existing target at different times, and taking advantage of the relatively stable target movement speed and continuous movement trajectory, the time domain features and discrete target position information are iterated to correct the target movement trajectory. That is, the multi-dimensional information is fully utilized to correct the existing results, reduce the overall error of the system trajectory tracking, achieve more accurate long-distance target tracking, and improve the overall positioning accuracy.

[0088] This step S151 is an implementation method of step S15.

[0089] The description of each of the above steps can be as follows: Figure 6 As shown in the figure, the preamble code of the LoRa signal collected by each LoRa gateway is divided into different channels, and the symbol-level phase characteristics are extracted. The arrival angle of the signal is estimated using spatial smoothing and multi-signal classification algorithms. The signal information of multiple gateways is fused to predict the target position. Finally, Kalman filtering is used to improve the accuracy of the target movement trajectory and achieve low-power long-distance target tracking.

[0090] The above embodiments are applicable to long-distance scenarios of various low-power wide area network devices, and reasonably utilize signal characteristics to achieve accurate target positioning and tracking, which helps to achieve fast and timely positioning of required nodes in environmental monitoring, as well as tracking of the trajectory of concerned targets in the fields of animal protection and smart industry. This is conducive to low-power wide area networks providing more practical and accurate long-distance positioning.

[0091] Figure 7 It is a schematic diagram of the structure of some embodiments of the target tracking device disclosed in the present invention, and the target tracking device includes a channel division unit 710, an arrival angle estimation unit 720, a multi-gateway information fusion unit 730 and a trajectory tracking unit 740.

[0092] The channel division unit 710 is configured to process the LoRa signal received by each gateway using linear frequency modulation symbols to obtain multi-channel demodulation symbols. For each gateway, based on the multi-channel demodulation symbols, the symbol-level features corresponding to the peak symbols in each channel are extracted to form a signal reception matrix.

[0093] The channel division unit 710 can be divided into different sub-channel phase information modules and symbol-level phase information modules.

[0094] Different sub-channel phase information modules are configured to provide a linear frequency modulation symbol. Using the linear frequency modulation symbol to process the LoRa signal can not introduce additional phase difference, thereby ensuring the accuracy of the original phase difference while generating a virtual equidistant uniform antenna array, achieving more accurate signal flight time estimation.

[0095] The symbol-level phase information module is configured to use linear frequency modulation symbols to divide the preamble code of the LoRa signal into multiple adjacent channels to obtain multi-channel demodulation symbols. The symbol-level phase information module is also configured to extract the phase characteristics and amplitude characteristics corresponding to the peak symbols in each channel of the multi-channel demodulation symbols according to the number of antennas of the gateway receiving the LoRa signal and the number of virtual channels corresponding to the linear frequency modulation symbols to form a signal receiving matrix.

[0096] The symbol-level phase information module utilizes the characteristics of LoRa long-distance transmission to obtain additional processing gain by performing dot multiplication with the local linear frequency modulation sequence, thereby improving the signal-to-noise ratio and thus improving the reliability of the LoRa signal in the phase domain.

[0097] The arrival angle estimation unit 720 is configured to obtain the arrival angle of the LoRa signal to the gateway receiving the LoRa signal according to the signal reception matrix.

[0098] The arrival angle estimation unit 720 can be divided into a spatial smoothing module and a multi-signal classification algorithm module. The spatial smoothing module is configured to perform spatial smoothing reconstruction on the signal receiving matrix to obtain a singular matrix. The multi-signal classification algorithm module is configured to obtain the arrival angle of the LoRa signal to the gateway receiving the LoRa signal based on the singular matrix and using the multi-signal classification algorithm.

[0099] The spatial smoothing module reconstructs the signal receiving matrix to solve the inevitable multipath problem in actual communication. The same signal is reflected through different paths and produces a time delay effect at the receiving end. The signals in different time domains of the original signal receiving matrix are coherent and cannot be effectively decomposed in space. The spatial smoothing module sacrifices the effective aperture to convert the receiving matrix into a singular matrix to solve the multipath problem; the multi-signal classification algorithm module performs feature decomposition on the singular matrix processed by the spatial smoothing module, specifically dividing it into signal subspace and noise subspace. Using the orthogonal characteristics of the two, all angles are traversed and the angle of arrival is calculated to obtain the minimum correlation.

[0100] The multi-gateway information fusion unit 730 is configured to predict the target location based on the arrival angles corresponding to the multiple gateways.

[0101] The multi-gateway information fusion unit 730 is configured to calculate the coordinate information of the intersection point generated by any two gateways according to the arrival angle of the LoRa signal to the gateway receiving the LoRa signal; and fuse multiple coordinate information to predict the target position.

[0102] For example, the weight of the intersection point generated by any two gateways is calculated according to the frequency coefficient of each gateway; and the weighted average calculation of multiple coordinate information is performed according to the weight of the intersection point generated by any two gateways to obtain the target position.

[0103] After the multi-gateway information fusion unit obtains the arrival angle information at each receiving end, the intersection of the arrival angles of multiple gateways will generate multiple target predicted positioning points. The entire received preamble code is multiplied with the locally generated standard frequency modulation symbol, and the spectrum coefficient is obtained by Fourier transform calculation as the weight. Multiple potential predicted positioning points are fused and calculated to obtain the final positioning result.

[0104] The trajectory tracking unit 740 is configured to obtain a target trajectory according to a plurality of target positions within a predetermined time period.

[0105] The trajectory tracking unit 740 is configured to perform iterative trajectory correction through Kalman filtering based on the continuous characteristics of multiple target positions in the time domain within a predetermined time period to obtain the target trajectory.

[0106] The trajectory tracking unit 740 iterates the time domain features and discrete target position information based on the predicted position information of the existing target at different times, taking advantage of the relatively stable target movement speed and continuous movement trajectory, and corrects the target movement trajectory, thereby reducing the overall error of the system trajectory tracking and achieving more accurate long-distance target tracking.

[0107] In the above embodiment, the channel division unit uses a special linear frequency modulation symbol to divide the preamble code into different channels. By extracting the phase characteristics at the symbol level, the reliability of the phase in the low signal-to-noise ratio situation is improved. By splitting the preamble code of a single channel, the number of elements in the multi-signal classification algorithm can be effectively increased, thereby improving the system's ability to resist multipath effects; the arrival angle estimation unit introduces spatial smoothing and multi-signal classification algorithms, and estimates the relative position of the target and the current gateway by decomposing the signal matrix into different subspaces; the multi-gateway information fusion unit proposes a weight-based positioning algorithm to fuse the information received by multiple gateways to improve the accuracy of the target position; the trajectory tracking unit uses the Kalman filter method to correct the trajectory based on the target position and its continuous characteristics in the time domain, thereby further improving the accuracy of target trajectory tracking.

[0108] Figure 8 Schematic diagram of the structure of some other embodiments of the target tracking device of the present disclosure. The target tracking device 800 includes a memory 810 and a processor 820. Among them: the memory 810 can be a disk, a flash memory or any other non-volatile storage medium. The memory is used to store the instructions in the above embodiments. The processor 820 is coupled to the memory 810 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 820 is used to execute the instructions stored in the memory.

[0109] In some embodiments, the processor 820 is coupled to the memory 810 via a BUS bus 830. The target tracking device 800 can also be connected to an external storage system 880 via a storage interface 840 to call external data, and can also be connected to a network or another computer system (not shown) via a network interface 860. No further detailed description will be given here.

[0110] In this embodiment, the accuracy of target tracking can be improved by storing data instructions in a memory and processing the instructions through a processor.

[0111] In other embodiments, a computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the steps of the method in the above-mentioned embodiment. It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] In some embodiments, a computer program product is protected, including a computer program or an instruction, which implements the above method when the computer program or instruction is executed by a processor. The computer program product includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by the CPU, the above functions defined in the method of the embodiment of the present disclosure are executed.

[0113] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0117] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0118] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A target tracking method, comprising: Using linear frequency modulation symbols, the long-distance radio LoRa signal received by each gateway is processed to obtain multi-channel demodulation symbols; For each of the gateways, based on the multi-channel demodulation symbols, symbol-level features corresponding to peak symbols in each channel are extracted to form a signal receiving matrix; According to the signal reception matrix, obtaining the arrival angle of the LoRa signal to the gateway receiving the LoRa signal; Predict the target location based on the arrival angles corresponding to multiple gateways; A target trajectory is obtained according to the multiple target positions within a predetermined time period.

2. The target tracking method according to claim 1, wherein: The method of using linear frequency modulation symbols to process the long-distance radio LoRa signal received by each gateway to obtain multi-channel demodulation symbols includes: The preamble code of the LoRa signal is divided into a plurality of adjacent channels by using the linear frequency modulation symbol to obtain the multi-channel demodulation symbol.

3. The target tracking method according to claim 2, wherein: The extracting, based on the multi-channel demodulation symbols, symbol-level features corresponding to the peak symbols in each channel to form a signal receiving matrix comprises: According to the number of antennas of the gateway receiving the LoRa signal and the number of virtual channels corresponding to the linear frequency modulation symbol, the phase characteristics and amplitude characteristics corresponding to the peak symbols in each channel of the multi-channel demodulation symbols are extracted to form the signal receiving matrix.

4. The target tracking method according to claim 1, wherein: The obtaining, according to the signal reception matrix, the angle of arrival of the LoRa signal to a gateway receiving the LoRa signal comprises: Performing spatial smoothing reconstruction on the signal receiving matrix to obtain a singular matrix; Based on the singular matrix, a multi-signal classification algorithm is used to obtain the arrival angle of the LoRa signal to the gateway receiving the LoRa signal.

5. The target tracking method according to claim 1, wherein: The predicting of the target position based on the arrival angles corresponding to the multiple gateways includes: Calculate the coordinate information of the intersection point between any two gateways according to the arrival angle of the LoRa signal to the gateway receiving the LoRa signal; A plurality of coordinate information is fused to predict the target position.

6. The target tracking method according to claim 5, wherein: The fusing of the plurality of coordinate information to predict the target position comprises: According to the frequency coefficient of each gateway, the weight of the intersection generated by any two gateways is calculated; According to the weight of the intersection point generated by any two gateways, a weighted average calculation is performed on multiple coordinate information to obtain the target position.

7. The target tracking method according to any one of claims 1 to 6, wherein: The obtaining of the target trajectory according to the plurality of target positions within a predetermined time period comprises: Based on the continuous characteristics of the multiple target positions in the time domain within the predetermined time period, iterative trajectory correction is performed through Kalman filtering to obtain the target trajectory.

8. A target tracking device, comprising: The channel division unit is configured to process the long-distance radio LoRa signal received by each gateway using the linear frequency modulation symbol to obtain a multi-channel demodulation symbol, and for each gateway, extract the symbol-level features corresponding to the peak symbol in each channel based on the multi-channel demodulation symbol to form a signal reception matrix; an angle of arrival estimation unit, configured to obtain an angle of arrival of the LoRa signal to a gateway receiving the LoRa signal according to the signal reception matrix; a multi-gateway information fusion unit configured to predict a target location based on arrival angles corresponding to the plurality of gateways; The trajectory tracking unit is configured to obtain a target trajectory according to a plurality of target positions within a predetermined time period.

9. A target tracking device, comprising: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the target tracking method according to any one of claims 1 to 7 based on instructions stored in the memory.

10. A computer-readable storage medium having computer program instructions stored thereon, wherein the instructions, when executed by a processor, implement the target tracking method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the target tracking method according to any one of claims 1 to 7 is implemented.

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