A UAV target tracking method and device based on distributed nonlinear filter

Abstract

The present invention relates to the field of drone tracking technology, and in particular to a method and device for drone target tracking based on a distributed nonlinear filter. It includes: modeling the communication topology of the distributed nonlinear filter through an undirected graph; predicting and updating the target motion trajectory and position of the drone through a distributed nonlinear filtering algorithm, and mapping the state value; based on the prediction and update results, obtaining the movement law and changes of the target in a complex environment, and tracking and controlling the movement and behavior of the drone. The method and device provided by the present invention can improve tracking accuracy, enhance system robustness, and improve computing efficiency; reduce the computing burden of a single platform, and enhance the real-time response capability of the system. It can improve computing efficiency while ensuring computing accuracy, and meet the real-time requirements of the drone tracking system.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle tracking, and in particular to a method and device for tracking unmanned aerial vehicle targets based on a distributed nonlinear filter. Background Art

[0002] Unmanned aerial vehicle systems have a wide range of applications in military, security, surveillance, search and rescue, and environmental monitoring, and target tracking is one of the key technologies to achieve these applications. UAV target tracking systems need to track targets accurately and in real time in dynamic and complex environments, which may include multi-target scenes, occlusions, noise, and sensor uncertainties. Traditional linear filters, such as Kalman filters, perform poorly when dealing with nonlinear systems, so more advanced nonlinear filtering methods need to be developed.

[0003] Nonlinear filters can better handle nonlinear dynamics and observation models in drone target tracking. These filters can improve tracking accuracy and robustness, especially in the face of high noise and nonlinear systems. The observation data of a single measurement sensor may not be comprehensive enough or be affected by occlusion and noise. Distributed filtering technology can integrate multi-source information and improve the accuracy and reliability of target tracking through information sharing and collaborative calculation between multiple measurement sensors.

[0004] The practical applications of distributed nonlinear filters in UAV target tracking include: air traffic management, ensuring accurate tracking of the position of UAVs in air routes and avoiding conflicts; UAV formation flight, when multiple UAVs collaborate to perform tasks, using distributed filters to ensure the stability of the formation structure and coordination of tasks; and UAV tracking in search and rescue missions, by collaboratively tracking the position of UAVs through multiple platforms to improve search and rescue efficiency and coverage.

[0005] Therefore, it is very necessary to design a distributed nonlinear filter that can be applied to UAV target tracking system. Summary of the invention

[0006] In order to solve the technical difficulty that traditional linear filters, such as Kalman filters, have poor performance in processing nonlinear systems in the prior art, the embodiment of the present invention provides a method and device for tracking UAV targets based on distributed nonlinear filters. The technical solution is as follows:

[0007] On the one hand, a UAV target tracking method based on a distributed nonlinear filter is provided, the method is implemented by a UAV device, and the method includes:

[0008] S1. Model the communication topology of the distributed nonlinear filter through an undirected graph;

[0009] S2, predict and update the target motion trajectory and position of the UAV through the distributed nonlinear filtering algorithm, and map the state value;

[0010] S3. Based on the prediction and update results, the movement patterns and changes of the target in the complex environment are obtained, and the movement and behavior of the UAV are tracked and controlled.

[0011] Optionally, in S1, the communication topology of the distributed nonlinear filter is modeled by an undirected graph, including:

[0012] The communication topology of the distributed nonlinear filter is constructed through an undirected graph Conduct modeling;

[0013] Among them, V = {1, 2, ..., n}, E, A represent the set of measured sensor nodes, the communication link used to measure the sensor nodes, and the weighted adjacency matrix.

[0014] Optionally, S1 further includes:

[0015] For each measuring sensor Construct a UAV target tracking system as shown in the following formula (1):

[0016]

[0017] Among them, x k Represents the real state value of the drone, y i,k represents the measurement value of the real state of the drone by the measuring sensor i; k = 1, 2, ... is the time series, f k () represents the vector value transfer function, h i,k () represents the measurement function, process noise {ω k} and measurement noise {v i,k} are mutually independent zero-mean white noise sequences.

[0018] Optionally, in S2, the target motion trajectory and position of the UAV are predicted and updated through a distributed nonlinear filtering algorithm, and the state value is mapped, including:

[0019] For each measuring sensor The posterior distribution sample at k-1 is expressed as shown in the following formula (2):

[0020]

[0021] Generate a sample of the predicted distribution based on the posterior distribution sample at k-1, and the sample of the predicted distribution is represented by X i,k∣k-1 ; Embed the state value into a positive definite kernel function k: The reproducing kernel Hilbert space of the feature map is defined as In the function, i.e., φ(x) = k(·, x), the estimated value of the predicted distribution of the measurement sensor is given by the following formula (3):

[0022]

[0023] in, express;

[0024] The measurement function is transformed, the measurement equation is linearly transformed, and new update rules are designed.

[0025] Optionally, the UAV state is predicted by a distributed nonlinear filtering algorithm, the measurement function is transformed, the measurement equation is linearly transformed, and a new update rule is designed, including:

[0026] The measurement function is expressed as the inner product in the reproducing kernel Hilbert space RKHS, that is in is a Hilbert–Schmidt linear operator;

[0027] make Y i,k Expressed as , to obtain Λ i,k An optimal solution for in, K means;

[0028] At this time, the estimated value μ of the predicted distribution of the measuring sensor is i,k|k The update rule of is shown in the following formula (4):

[0029]

[0030] in,

[0031] Set the minimum polynomial degree of the weighted adjacency matrix for the process matrix Γ i,k and i,k Perform initialization operations;

[0032] Update process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k .

[0033] Optionally, set the minimum polynomial degree of the weighted adjacency matrix, for the process matrix Γ i,k and i,k Perform initialization operations, including:

[0034] The minimum polynomial degree of the weighted adjacency matrix A is d+1 (d+1≤n), where α 0 ,α 1 ,…α d is a constant, and let

[0035] For the process matrix Γ i,k and i,k Perform the initialization operation as shown in the following formula (5) and formula (6):

[0036]

[0037] in, Represents the process matrix Γ i,k The initial value of Process Matrixξ i,k The initial value of; I represents the identity matrix, express The adjoint matrix of .

[0038] Optionally, update the process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k ,include:

[0039] Set r = 0, Repeat the following formula (7) to formula (10) until r = d:

[0040]

[0041] r=r+1 (10)

[0042] calculate

[0043] The weight vector is calculated according to the following formula (11):

[0044]

[0045] The normalized weight vector w is calculated according to the following formula (12): i,k :

[0046]

[0047] The optimal state estimate x is calculated according to the following formula (13) and formula (14): i,k∣k and its error covariance matrix P i,k∣k :

[0048]

[0049] On the other hand, a UAV target tracking device based on a distributed nonlinear filter is provided, and the device is applied to a UAV method, and the device comprises:

[0050] A modeling module for modeling the communication topology of the distributed nonlinear filter through an undirected graph;

[0051] The trajectory prediction and update module is used to predict and update the target motion trajectory and position of the drone through a distributed nonlinear filtering algorithm, and to map the state value;

[0052] The UAV tracking and control module is used to obtain the movement patterns and changes of the target in a complex environment based on the prediction and update results, and to track and control the movement and behavior of the UAV.

[0053] On the other hand, a UAV target tracking device is provided, and the UAV target tracking device based on a distributed nonlinear filter includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned UAV target tracking methods is implemented.

[0054] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned drone methods.

[0055] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0056] The method and device provided by the present invention can improve tracking accuracy: the designed distributed nonlinear filter can integrate data from multiple measurement sensors and reduce the uncertainty and error of a single measurement sensor through information fusion; compared with traditional linear filters, nonlinear filters can better handle the nonlinear characteristics of UAV movement and observation processes and improve tracking accuracy.

[0057] Enhanced system robustness: The designed distributed nonlinear filter has good fault tolerance. Even if a measurement sensor fails, the system can still maintain stable tracking performance through the data of other measurement sensors, avoiding the impact of single point failure on the entire system.

[0058] Improve computing efficiency: The designed distributed nonlinear filter distributes computing tasks to multiple platforms for parallel processing, reducing the computing burden of a single platform and improving the real-time response capability of the system. It can improve computing efficiency while ensuring computing accuracy and meet the real-time requirements of the UAV tracking system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 It is a flow chart of UAV target tracking based on distributed nonlinear filter provided by an embodiment of the present invention;

[0061] Figure 2 is a distributed nonlinear filter communication topology diagram provided by an embodiment of the present invention;

[0062] Figure 3 is a diagram (simulation A) of tracking the real motion trajectory of a drone provided by an embodiment of the present invention;

[0063] Figure 4 is a distributed nonlinear filter communication topology diagram (simulation B) provided by an embodiment of the present invention;

[0064] Figure 5 is a diagram of tracking the actual motion trajectory of a drone provided by an embodiment of the present invention (simulation B);

[0065] Figure 6 It is a block diagram of a UAV target tracking device based on a distributed nonlinear filter provided by an embodiment of the present invention;

[0066] Figure 7 It is a structural schematic diagram of a UAV target tracking device based on a distributed nonlinear filter provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0068] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0069] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0070] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be expressed in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0071] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0072] The embodiment of the present invention provides an unmanned target tracking method, which can be implemented by an unmanned target tracking device based on a distributed nonlinear filter, and the unmanned target tracking device based on a distributed nonlinear filter can be a terminal or a server. Figure 1 The flowchart of the UAV target tracking method based on distributed nonlinear filter is shown in the figure. The processing flow of the method may include the following steps:

[0073] S1. Model the communication topology of the distributed nonlinear filter through an undirected graph;

[0074] In a feasible implementation, in S1, the communication topology of the distributed nonlinear filter is modeled by an undirected graph, including:

[0075] The communication topology of the distributed nonlinear filter is constructed through an undirected graph Conduct modeling;

[0076] Where V = {1, 2, ..., n}, E, A represent the set of sensor nodes used to measure the communication links between sensor nodes, and the weighted adjacency matrix. ij ] is a doubly random matrix and satisfies a ii >0,a ij ≥0,∑ j∈ V a ij =1,∑ i∈V a ij =1.

[0077] In a feasible implementation manner, S1 further includes:

[0078] For each measuring sensor Construct a UAV target tracking system as shown in the following formula (1):

[0079]

[0080] Among them, x k Represents the real state value of the drone, y i,k represents the measurement value of the real state of the drone by the measuring sensor i; k = 1, 2, ... is the time series, f k () represents the vector value transfer function, h i,k () represents the measurement function, process noise {ω k} and measurement noise {v i,k} are mutually independent zero-mean white noise sequences, and their joint covariance matrix is:

[0081]

[0082] in, and δ i,j is the Kroneckerdelta function.

[0083] S2, predict and update the target motion trajectory and position of the UAV through the distributed nonlinear filtering algorithm, and map the state value;

[0084] In a feasible implementation manner, in a UAV target tracking system, multiple measurement sensors in the distributed nonlinear filter designed by the present invention achieve tracking and positioning of the UAV through communication and collaboration.

[0085] Before executing the specific algorithm in the distributed nonlinear filter, the following preparations need to be done:

[0086] For each measuring sensor The posterior distribution sample at k-1 is expressed as shown in the following formula (2):

[0087]

[0088] Generate a sample of the predicted distribution based on the posterior distribution sample at k-1, and the sample of the predicted distribution is represented by X i,k∣k-1 ; Embed the state value into a positive definite kernel function k: The reproducing kernel Hilbert space of the feature map is defined as In the function, i.e., φ(x) = k(·, x), the estimated value of the predicted distribution of the measurement sensor is given by the following formula (3):

[0089]

[0090] in, express;

[0091] The measurement function is transformed, the measurement equation is linearly transformed, and new update rules are designed.

[0092] In a feasible implementation, a distributed nonlinear filtering algorithm is used to predict the state of the UAV, transform the measurement function, transform the measurement equation linearly, and design a new update rule, including:

[0093] The measurement function is expressed as the inner product in the reproducing kernel Hilbert space RKHS, that is in is a Hilbert–Schmidt linear operator;

[0094] make Y i,k Expressed as , to obtain Λ i,k An optimal solution for in, K means;

[0095] Therefore, the measurement equation can be transformed linearly into:

[0096]

[0097] At this time, the estimated value μ of the predicted distribution of the measuring sensor is i,k|k The update rule of is shown in the following formula (4):

[0098]

[0099] in,

[0100] Set the minimum polynomial degree of the weighted adjacency matrix for the process matrix Γ i,k and i,k Perform initialization operations;

[0101] Update process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k .

[0102] In one feasible implementation, the minimum polynomial degree of the weighted adjacency matrix is ​​set, and the process matrix Γ i,k and i,k Perform initialization operations, including:

[0103] The minimum polynomial degree of the weighted adjacency matrix A is d+1 (d+1≤n), where α 0 ,α1 ,…α d is a constant, and let where α 0 ,α 1 ,…α d is a constant.

[0104] For the process matrix Γ i,k and i,k Perform the initialization operation as shown in the following formula (5) and formula (6):

[0105]

[0106] in, Represents the process matrix Γ i,k The initial value of Process Matrixξ i,k The initial value of; I represents the identity matrix, express The adjoint matrix of .

[0107] In a feasible implementation manner, after the preparation work is completed, Algorithm 1 in Table 1 and Algorithm 2 in Table 2 are executed in sequence.

[0108] Table 1 Γ i,k and i,k Distributed consensus algorithm

[0109]

[0110] Table 2 Distributed nonlinear filtering algorithm

[0111]

[0112] In a feasible implementation manner, the distributed nonlinear filter designed by the present invention is applied to a UAV target tracking system, and the calculated optimal state estimate is substantially the same as the actual state value of the UAV, which proves that the distributed nonlinear filter of the present invention can achieve good tracking effect in the UAV target tracking system.

[0113] S3. Based on the prediction and update results, the movement patterns and changes of the target in the complex environment are obtained, and the movement and behavior of the UAV are tracked and controlled.

[0114] In a feasible implementation manner, the present invention illustrates the accuracy of the designed distributed nonlinear filter through the following two simulation cases:

[0115] Simulation A. The UAV speed in the UAV target tracking system is approximately constant

[0116] The present invention considers a drone target tracking system in which a drone moves at a fixed altitude at an approximately constant speed. The drone state update equation is given by the following formula:

[0117] x k =F k-1 x k-1 +G k-1 ω k-1 ,

[0118] Among them, [x k ,y k ] T and They are the position and speed of the drone respectively;

[0119] F k-1 =block-diag(F,F),G k-1 =block-diag(G,G), Sampling period Δt = 1s, process noise ω k-1 Obey Gaussian distribution.

[0120] The distributed nonlinear filter designed by the present invention is composed of ten measurement sensors, and its communication topology is shown in the following figure: Figure 2 As shown, the red dots represent measurement sensors and the blue lines are the communication links between the measurement sensors.

[0121] The tracking of the real motion trajectory of the UAV by the designed distributed nonlinear filter is shown in the figure. Figure 3 As shown in the figure, the blue curve represents the real motion trajectory of the UAV, and the red curve represents the estimation of the real motion trajectory of the UAV by the distributed nonlinear filter. It can be seen that the designed distributed nonlinear filter can be successfully applied to the UAV target tracking system with constant speed and has good tracking performance.

[0122] Simulation B. Coordinated turning of drones in drone target tracking system

[0123] The present invention considers a drone target tracking system in which the drone performs coordinated turns. The drone state update equation is given by the following formula:

[0124]

[0125] Among them, is the unknown turning rate at time, 0.2 is the sampling period, and is a Gaussian white noise sequence with zero mean and zero covariance matrix.

[0126] The distributed nonlinear filter designed by the present invention is composed of eight measurement sensors, and its communication topology is shown in the following figure: Figure 4As shown, the red dots represent measurement sensors and the blue lines are the communication links between the measurement sensors.

[0127] The distributed nonlinear filter designed in this invention tracks the real motion trajectory of the UAV. Figure 5 As shown in the figure, the blue curve represents the real motion trajectory of the UAV, and the red curve represents the estimation of the real motion trajectory of the UAV by the distributed nonlinear filter. It can be seen that the designed distributed nonlinear filter can be successfully applied to the target tracking system of the UAV in the case of turning, and has good tracking performance.

[0128] The method provided by the present invention can improve tracking accuracy: the designed distributed nonlinear filter can integrate data from multiple measurement sensors and reduce the uncertainty and error of a single measurement sensor through information fusion; compared with traditional linear filters, nonlinear filters can better handle the nonlinear characteristics of UAV movement and observation processes and improve tracking accuracy.

[0129] Enhanced system robustness: The designed distributed nonlinear filter has good fault tolerance. Even if a measurement sensor fails, the system can still maintain stable tracking performance through the data of other measurement sensors, avoiding the impact of single point failure on the entire system.

[0130] Improve computing efficiency: The designed distributed nonlinear filter distributes computing tasks to multiple platforms for parallel processing, reducing the computing burden of a single platform and improving the real-time response capability of the system. It can improve computing efficiency while ensuring computing accuracy and meet the real-time requirements of the UAV tracking system.

[0131] Figure 6 1 is a block diagram of a UAV target tracking device based on a distributed nonlinear filter according to an exemplary embodiment, wherein the device is used in a UAV method. Figure 6 The device includes a modeling module 310, a trajectory prediction and updating module 320, and a drone tracking control module 330. Among them:

[0132] A modeling module 310, for modeling the communication topology of the distributed nonlinear filter through an undirected graph;

[0133] The trajectory prediction and update module 320 is used to predict and update the target motion trajectory and position of the UAV through a distributed nonlinear filtering algorithm, and perform mapping operations on the state value;

[0134] The UAV tracking and control module 330 is used to obtain the movement patterns and changes of the target in a complex environment based on the prediction and update results, and to track and control the movement and behavior of the UAV.

[0135] Modeling module 310, for the communication topology of the distributed nonlinear filter, through an undirected graph Conduct modeling;

[0136] Among them, V = {1, 2, ..., n}, E, A represent the set of measured sensor nodes, the communication link used to measure the sensor nodes, and the weighted adjacency matrix.

[0137] Modeling module 310 is used for each measurement sensor Construct a UAV target tracking system as shown in the following formula (1):

[0138]

[0139] Among them, x k Represents the real state value of the drone, y i,k represents the measurement value of the real state of the drone by the measuring sensor i; k = 1, 2, ... is the time series, f k () represents the vector value transfer function, h i,k () represents the measurement function, process noise {ω k} and measurement noise {v i,k} are mutually independent zero-mean white noise sequences.

[0140] The trajectory prediction and updating module 320 is used for each measurement sensor The posterior distribution sample at k-1 is expressed as shown in the following formula (2):

[0141]

[0142] Generate a sample of the predicted distribution based on the posterior distribution sample at k-1, and the sample of the predicted distribution is represented by X i,k∣k-1 ; Embed the state value into a positive definite kernel function k: The reproducing kernel Hilbert space of the feature map is defined as In the function, i.e., φ(x) = k(·, x), the estimated value of the predicted distribution of the measurement sensor is given by the following formula (3):

[0143]

[0144] in, express;

[0145] The measurement function is transformed, the measurement equation is linearly transformed, and new update rules are designed.

[0146] Optionally, the UAV state is predicted by a distributed nonlinear filtering algorithm, the measurement function is transformed, the measurement equation is linearly transformed, and a new update rule is designed, including:

[0147] The measurement function is expressed as the inner product in the reproducing kernel Hilbert space RKHS, that is in is a Hilbert–Schmidt linear operator;

[0148] make Y i,k Expressed as , to obtain Λ i,k An optimal solution for in, K means;

[0149] At this time, the estimated value μ of the predicted distribution of the measuring sensor is i,k|k The update rule of is shown in the following formula (4):

[0150]

[0151] in,

[0152] Set the minimum polynomial degree of the weighted adjacency matrix for the process matrix Γ i,k and i,k Perform initialization operations;

[0153] Update process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k .

[0154] Optionally, set the minimum polynomial degree of the weighted adjacency matrix, for the process matrix Γ i,k and i,k Perform initialization operations, including:

[0155] The minimum polynomial degree of the weighted adjacency matrix A is d+1 (d+1≤n), where α 0 ,α 1 ,…α d is a constant, and let

[0156] For the process matrix Γ i,k and i,k Perform the initialization operation as shown in the following formula (5) and formula (6):

[0157]

[0158] in, Represents the process matrix Γ i,k The initial value of Process Matrixξ i,k The initial value of; I represents the identity matrix, express The adjoint matrix of .

[0159] Optionally, update the process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k ,include:

[0160] Set r = 0, Repeat the following formula (7) to formula (10) until r = d:

[0161]

[0162] r=r+1 (10)

[0163] calculate

[0164] The weight vector is calculated according to the following formula (11):

[0165]

[0166] The normalized weight vector w is calculated according to the following formula (12): i,k :

[0167]

[0168] The optimal state estimate x is calculated according to the following formula (13) and formula (14): i,k∣k and its error covariance matrix P i,k∣k :

[0169]

[0170] The device provided by the present invention can improve tracking accuracy: the designed distributed nonlinear filter can integrate data from multiple measurement sensors and reduce the uncertainty and error of a single measurement sensor through information fusion; compared with traditional linear filters, nonlinear filters can better handle the nonlinear characteristics of drone movement and observation processes and improve tracking accuracy.

[0171] Enhanced system robustness: The designed distributed nonlinear filter has good fault tolerance. Even if a measurement sensor fails, the system can still maintain stable tracking performance through the data of other measurement sensors, avoiding the impact of single point failure on the entire system.

[0172] Improve computing efficiency: The designed distributed nonlinear filter distributes computing tasks to multiple platforms for parallel processing, reducing the computing burden of a single platform and improving the real-time response capability of the system. It can improve computing efficiency while ensuring computing accuracy and meet the real-time requirements of the UAV tracking system.

[0173] Figure 7 is a schematic diagram of the structure of a UAV target tracking device based on a distributed nonlinear filter provided by an embodiment of the present invention. Figure 7 As shown, the UAV target tracking device based on distributed nonlinear filter may include the above Figure 6 Optionally, the UAV target tracking device 410 based on distributed nonlinear filter may include a first processor 2001 .

[0174] Optionally, the UAV target tracking device 410 based on a distributed nonlinear filter may further include a memory 2002 and a transceiver 2003 .

[0175] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0176] Combine the following Figure 7 The components of the UAV target tracking device 410 based on the distributed nonlinear filter are specifically introduced as follows:

[0177] The first processor 2001 is the control center of the UAV target tracking device 410 based on the distributed nonlinear filter, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0178] Optionally, the first processor 2001 can perform various functions of the UAV target tracking device 410 based on the distributed nonlinear filter by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0179] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 7 CPU0 and CPU1 are shown in FIG.

[0180] In a specific implementation, as an embodiment, the UAV target tracking device 410 based on the distributed nonlinear filter may also include multiple processors, such as Figure 7 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0181] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0182] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be accessed through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0183] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0184] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0185] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and communicate with the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0186] It should be noted that Figure 7 The structure of the UAV target tracking device 410 based on distributed nonlinear filter shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0187] In addition, the technical effects of the UAV target tracking device 410 based on the distributed nonlinear filter can refer to the technical effects of the UAV method described in the above method embodiment, which will not be repeated here.

[0188] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0189] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0190] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0191] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0192] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0193] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0194] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0195] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0196] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0199] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0200] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A UAV target tracking method based on distributed nonlinear filter, characterized in that: The method comprises: S1. Model the communication topology of the distributed nonlinear filter through an undirected graph; S2, predict and update the target motion trajectory and position of the drone through a distributed nonlinear filtering algorithm, and perform mapping operations on the updated drone state value; In S2, the target motion trajectory and position of the UAV are predicted and updated through a distributed nonlinear filtering algorithm, and the state value is mapped, including: For each measurement sensor i∈V, the posterior distribution sample at time k-1 is expressed as shown in the following formula (2): Generate a sample of the predicted distribution based on the posterior distribution sample at k-1, and the sample of the predicted distribution is represented by X i,k∣k-1 ; Embed the state value into a positive definite kernel function In the reproducing kernel Hilbert space of the defined feature map, i.e., φ(x) = k(·, x), the estimated value of the predicted distribution of the measurement sensor is given by the following formula (3): in, Based on the estimated values ​​of the predicted distribution of the measurement sensor, the measurement function is transformed, the measurement equation is linearly transformed, and a new update rule is designed; The method transforms the measurement function based on the estimated value of the predicted distribution of the measurement sensor, transforms the measurement equation linearly, and designs a new update rule, including: The measurement function is expressed as the inner product in the reproducing kernel Hilbert space, that is in is a Hilbert–Schmidt linear operator; make Find Λ i,k An optimal solution for in, K represents a matrix; At this time, the estimated value μ of the predicted distribution of the measuring sensor is i,k|k The update rule of is shown in the following formula (4): in, Set the minimum polynomial degree of the weighted adjacency matrix for the process matrix Γ i,k and i,k Perform initialization operations; Update process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k ; S3. Based on the prediction and update results, the movement patterns and changes of the target in the complex environment are obtained, and the movement and behavior of the UAV are tracked and controlled.

2. The method according to claim 1, characterized in that In S1, the communication topology of the distributed nonlinear filter is modeled by an undirected graph, including: The communication topology of the distributed nonlinear filter is constructed through an undirected graph Conduct modeling; Among them, V = {1, 2, ..., n}, E, A represent the set of measured sensor nodes, the communication links between the sensor nodes, and the weighted adjacency matrix.

3. The method according to claim 2, characterized in that The S1 further comprises: For each measuring sensor Construct a UAV target tracking system as shown in the following formula (1): Among them, x k Represents the real state value of the drone, y i,k represents the measurement value of the real state of the drone by the measuring sensor i; k = 1, 2, ... is the time series, f k () represents the vector value transfer function, h i,k () represents the measurement function, process noise {ω k } and measurement noise {v i,k } are mutually independent zero-mean white noise sequences.

4. The method according to claim 3, characterized in that The minimum polynomial degree of the weighted adjacency matrix is ​​set to the process matrix Γ i,k and i,k Perform initialization operations, including: The minimum polynomial degree of the weighted adjacency matrix A is d+1 (d+1≤n), where α0, α1, …α d is a constant, and let For the process matrix Γ i,k and i,k Perform the initialization operation as shown in the following formula (5) and formula (6): in, Represents the process matrix Γ i,k The initial value of Process Matrixξ i,k The initial value of; I represents the identity matrix, express The adjoint matrix of .

5. The method according to claim 4, characterized in that Update process matrix Γ i,k and i,k , get the optimal state estimate x i,k∣k and its error covariance matrix P i,k∣k ,include: Set r = 0, Repeat the following formula (7) to formula (10) until r = d: r＝r+1 (10) calculate The weight vector is calculated according to the following formula (11): The normalized weight vector W is calculated according to the following formula (12): i,k : The optimal state estimate x is calculated according to the following formula (13) and formula (14): i,k∣k and its error covariance matrix P i,k∣k :

6. A UAV target tracking device based on a distributed nonlinear filter, wherein the UAV target tracking device based on a distributed nonlinear filter is used to implement the UAV target tracking method based on a distributed nonlinear filter as claimed in any one of claims 1 to 5, characterized in that: The device comprises: A modeling module for modeling the communication topology of the distributed nonlinear filter through an undirected graph; The trajectory prediction and update module is used to predict and update the target motion trajectory and position of the drone through a distributed nonlinear filtering algorithm, and to map the state value; The UAV tracking and control module is used to obtain the movement patterns and changes of the target in a complex environment based on the prediction and update results, and to track and control the movement and behavior of the UAV.

7. A UAV target tracking device based on a distributed nonlinear filter, characterized in that: The UAV target tracking device based on distributed nonlinear filter includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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