Range-Doppler Target Tracking Method Based on Bistatic Range-Doppler Observations

Abstract

This invention provides a range-domain target tracking method based on bistatic range-Doppler observations, comprising: determining the state vector of the target based on its spatial information; calculating the state vector using a Cartesian approximate uniform linear motion model to obtain a state evolution equation; initializing the target's state vector using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance for each search sub-interval; and filtering the measurement vector using a set of unscented Kalman filters based on the initial state vector and initial covariance for each search sub-interval to obtain a joint state vector estimate and a joint covariance estimate for the target. Both the joint state vector estimate and the joint covariance estimate are used for tracking and locating the target. This method can accurately estimate the state vector and obtain the Cartesian position of the target within a quadrant based on the estimation results.

Description

Technical Field

[0001] This invention relates to the field of space target tracking technology, and in particular to a range domain target tracking method based on bistatic range-Doppler observation. Background Technology

[0002] Compared to traditional monostatic radar, bistatic radar, with its flexible transmit / receive configuration, achieves a wider surveillance range and better anti-jamming capabilities. Using passive radar as the receiver further enhances the stealth and adaptability of bistatic radar systems in various situations, especially where active radar cannot be deployed. Due to its technical operability and cost-effectiveness, bistatic radar has attracted considerable interest and has been widely adopted.

[0003] In related technologies, estimation algorithms cannot perform filtering initialization using only data provided by a single receiver when using bistatic distance and Doppler measurements for state estimation.

[0004] Therefore, there is an urgent need for a range-domain target tracking method based on bistatic range-Doppler observations to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a range-domain target tracking method based on bistatic range-Doppler observations, which can use data provided by a receiver for filtering initialization.

[0006] In a first aspect, embodiments of the present invention provide a range-domain target tracking method based on bistatic range-Doppler observations, comprising:

[0007] Based on the spatial information of the target to be tracked, the state vector of the target to be tracked is determined; wherein, the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler of the target relative to the transmitter, the Doppler of the target relative to the receiver, and the square of the velocity of the target to be tracked;

[0008] The state vector is calculated based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked;

[0009] The state vector of the target to be tracked is calculated using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by measurement vectors;

[0010] A set of unscented Kalman filters is used to process the measurement vector based on the initial state vector and initial covariance of each search sub-interval to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein, the joint state vector estimate and the joint covariance estimate are both used to track and locate the target to be tracked.

[0011] Secondly, embodiments of the present invention also provide a range-domain target tracking device based on bistatic range-Doppler observations, comprising:

[0012] A determining unit is configured to determine the state vector of the target to be tracked based on the spatial information of the target; wherein the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler effect of the target relative to the transmitter, the Doppler effect of the target relative to the receiver, and the square of the velocity of the target.

[0013] The calculation unit is used to calculate the state vector based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked.

[0014] The first processing unit is used to initialize the state vector of the target to be tracked using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by the measurement vector;

[0015] The second processing unit is used to filter the measurement vector using a set of unscented Kalman filters based on the initial state vector and initial covariance of each search sub-interval, to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein the joint state vector estimate and the joint covariance estimate are both used to track and locate the target to be tracked.

[0016] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0017] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0018] This invention provides a range-domain target tracking method based on bistatic distance-Doppler observations. First, a state vector containing a minimum number of elements describing the system at any given time is defined in the bistatic distance-Doppler state space. Then, an accurate evolution equation is constructed corresponding to the target state in Cartesian approximate uniform linear motion. Based on this equation and the bistatic distances and Doppler measurements at three time points, Monte Carlo sampling and particle swarm optimization are used to initialize the target motion model. Finally, a set of unscented Kalman filters is used for filtering to obtain the estimated state and covariance of the target, thus yielding the Cartesian position of the target within a quadrant, achieving target tracking. This method solves the problem of traditional estimation algorithms requiring multiple transmitters or receivers and model mismatch when using bistatic distances and Doppler measurements for state estimation, and addresses the issue that existing estimation algorithms cannot use bistatic distances and Doppler measurements provided by a single receiver for filtering and initialization. It achieves accurate estimation of the target's state vector, and the Cartesian position of the target within a quadrant can be obtained from the estimation results. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a range domain target tracking method based on bistatic range-Doppler observations provided by an embodiment of the present invention;

[0021] Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention;

[0022] Figure 3 This is a structural diagram of a range-domain target tracking device based on bistatic range-Doppler observation provided in an embodiment of the present invention;

[0023] Figure 4 The average initial velocity provided in one embodiment of the present invention is The trajectory of the target to be tracked at that time;

[0024] Figure 5 The average initial velocity provided in one embodiment of the present invention is , , hour A schematic diagram of the estimation;

[0025] Figure 6The average initial velocity provided in one embodiment of the present invention is , , hour A schematic diagram of the estimation;

[0026] Figure 7 The average initial velocity provided in one embodiment of the present invention is , , hour A schematic diagram of the estimation;

[0027] Figure 8 The average initial velocity provided in one embodiment of the present invention is , , hour A schematic diagram of the estimation;

[0028] Figure 9 The average initial velocity provided in one embodiment of the present invention is , , hour A schematic diagram of the estimation;

[0029] Figure 10 This is a schematic diagram of the normalized estimation error squared according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0031] Please refer to Figure 1 This invention provides a range-domain target tracking method based on bistatic range-Doppler observations, the method comprising:

[0032] Step 100: Based on the spatial information of the target to be tracked, determine the state vector of the target to be tracked; wherein, the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler of the target relative to the transmitter, the Doppler of the target relative to the receiver, and the square of the velocity of the target to be tracked;

[0033] Step 102: Calculate the state vector based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked;

[0034] Step 104: The measurement vector and state evolution equation of the target to be tracked are calculated using the Monte Carlo sampling strategy and particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by the measurement vector.

[0035] Step 106: A set of unscented Kalman filters is used to process the measurement vector based on the initial state vector and initial covariance of each search sub-interval to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein, the joint state vector estimate and joint covariance estimate are both used to track and locate the target to be tracked.

[0036] In this embodiment of the invention, firstly, a state vector containing the minimum number of elements describing the system at any given time is defined in the bistatic distance-Doppler state space. Then, an accurate evolution equation is constructed corresponding to the target state in Cartesian approximate uniform linear motion. Based on this equation and the bistatic distance and Doppler measurements at three time points, Monte Carlo sampling and particle swarm optimization algorithms are used to initialize the target state vector. Finally, a set of unscented Kalman filters are used for filtering to obtain the estimated state value and covariance of the target to be tracked, thereby obtaining the Cartesian position of the target in one quadrant and achieving target tracking. This method solves the problem of traditional estimation algorithms requiring multiple transmitters or receivers and model mismatch when using bistatic distance and Doppler measurements for state estimation, and addresses the issue that existing estimation algorithms cannot use bistatic distance and Doppler measurements provided by a single receiver for filtering initialization, thus achieving accurate estimation of the target's state vector.

[0037] The following description Figure 1 The execution method of each step is shown.

[0038] First, for step 100, the state vector of the target to be tracked is determined based on the spatial information of the target.

[0039] In existing monostatic scenarios, the state vector constructed in the range-Doppler state space includes the target's position relative to the sensor, the target's Doppler velocity relative to the sensor, and the square of the velocity. However, the construction of state vectors in the range-Doppler space for bistatic scenarios has not yet been studied. This invention aims to find a suitable state space representation for approximately uniform motion in a Cartesian coordinate system within the bistatic range-Doppler plane. Specifically, it defines a state vector containing the minimum number of elements that can fully describe the system at any given time. This state vector is shown in the following formula:

[0040]

[0041] In the formula, It is a state vector; for k The distance between the target and the transmitter must be tracked at all times. for k The distance between the target and the receiver must be tracked at all times; for k The Doppler effect of the target to be tracked relative to the transmitter at all times; for k The Doppler effect of the target to be tracked relative to the receiver at all times; for k The square of the velocity of the target to be tracked at all times; for k The Cartesian position of the target to be tracked at all times; for k The speed of the target to be tracked at all times; S The length of the baseline.

[0042] Then, for step 102, the state vector is calculated based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked.

[0043] In this embodiment of the invention, the Cartesian approximate uniform linear motion model is shown in the following formula:

[0044]

[0045] In the formula, The sampling interval is... It is a two-dimensional identity matrix. for x The variance of the independent distributions along the axial direction is White Gaussian process noise; for y The variance of the independently distributed axial directions is White Gaussian process noise.

[0046] Based on the above formula, we can derive The evolution equation is:

[0047]

[0048] in,

[0049]

[0050]

[0051]

[0052] Right now,

[0053]

[0054] Similarly, we can conclude that:

[0055]

[0056]

[0057]

[0058]

[0059] in,

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] In the formula, subscripts a Data independent of process noise; subscript b This is data related to process noise.

[0068] In other words, , , , These represent the time intervals from time 1 to 2, assuming no process noise interference. The state evolution yields the transmit distance, receive distance, transmit Doppler, and receive Doppler.

[0069] Combining the above formulas, we can obtain the state evolution equation of the target to be tracked as follows:

[0070]

[0071] in,

[0072]

[0073]

[0074]

[0075] In the formula, The expected value of the process noise in the range-Doppler coordinate system; This represents the process noise term, with a mean of 0.

[0076] For step 104, the state vector of the target to be tracked is initialized using the Monte Carlo sampling strategy and the particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval.

[0077] In this embodiment of the invention, the initialization process specifically includes: obtaining a first matrix composed of measurement vectors at three time points and a covariance matrix corresponding to the first matrix based on the measurement vectors; calculating the first matrix and covariance matrix using a Monte Carlo algorithm to obtain a first sampling point and a first weight of the first sampling point; and obtaining the initial state vector and initial covariance of each search sub-interval using a particle swarm optimization algorithm based on the first sampling point and the first weight.

[0078] In other words, in this embodiment of the invention, only the distance sum and the corresponding Doppler sum at three time points are needed to calculate the solution of the above state vector. However, due to the randomness of measurement noise, the discriminant in the correlation equation may have negative values. At the same time, due to the strong nonlinearity of the solution, the derivation of the initial covariance of the state vector is also very difficult. Therefore, this embodiment of the invention uses the Monte Carlo sampling strategy and the particle swarm optimization algorithm to calculate the initial state vector and its corresponding covariance.

[0079] Specifically, first obtain , and Measurement vector at time , and Based on the measurements taken at these three times, a measurement vector matrix and the corresponding covariance matrix are constructed, as shown in the following formula:

[0080]

[0081]

[0082] In the formula, This is the first matrix; for The measurement vector at time; for Transpose of; This is the covariance matrix corresponding to the first matrix; for The covariance matrix of time measurements; It is an empty matrix of size 2×2.

[0083] Subsequently, a Monte Carlo sampling strategy was used to generate sampling points to approximate the results. The probability distribution is used to obtain the first sampling point and its corresponding first weight, which is specifically calculated using the following formula:

[0084]

[0085]

[0086] In the formula, This is the first sampling point; The first weight is the value corresponding to the first sampling point; g A function to generate Gaussian-distributed sampling points. N The number of sampling points

[0087] Furthermore, before performing calculations using the particle swarm optimization algorithm, it is necessary to... Derivation The possible search range. Consider the target is located in On a bibasic ellipse, in this case, The search scope is , The search scope is . The search scope is , The observed values ​​of Doppler summation, where It is the maximum value of the square of the known target velocity. and The search scope is .

[0088] The particle swarm optimization solution is then calculated using the following formula:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] In the formula, For particle optimization algorithms; The standard deviation of distance measurement noise; The standard deviation of noise is measured using Doppler. These are the initial values ​​for the state vector; l =0, 1, 2; This is the initial value of the distance from the target to the transmitter; The initial value of the Doppler amplitude of the target to be tracked relative to the transmitter; Let be the initial value of the square of the velocity of the target to be tracked; This is the initial value of the distance from the target to the receiver; The initial value is the Doppler value of the target to be tracked relative to the receiver.

[0095] In this embodiment of the invention, to address the potential for the particle swarm optimization algorithm to get trapped in a local optimum, the search intervals for distance and Doppler are divided into equal intervals. and The search sub-intervals are defined as follows: For each search sub-interval, the corresponding posterior sampling points are estimated using the sampling points obtained from Monte Carlo sampling through particle swarm optimization. For the nth sub-interval... The number of sub-intervals, i.e., the number of... For each model, the posterior sampling points of the state are:

[0096]

[0097] In the formula, For the first Posterior sampling points of each search sub-interval; This is the result after iteration of the particle swarm optimization algorithm.

[0098] Combining the above formulas, we can obtain the first... The initial state vectors for the search sub-intervals are:

[0099]

[0100] No. The initial covariance of each search sub-interval is:

[0101]

[0102] In the formula, .

[0103] For step 106, the initial state vector and initial covariance of each search sub-interval are filtered based on a set of unscented Kalman filters to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked.

[0104] In this embodiment of the invention, the filtering process specifically includes: calculating the second sampling point and the second weight corresponding to the second sampling point based on the initial state vector and the initial covariance; calculating the second sampling point using the state evolution equation, and calculating the probability of the search sub-interval based on the calculation result and the second weight; and calculating the joint state vector estimate and the joint covariance estimate based on the probability.

[0105] Specifically, the initial value of the state vector and the corresponding initial covariance can be calculated through the above initialization process. The second sampling point and its corresponding second weight can be calculated using the following formula:

[0106]

[0107] In the formula, This is the second sampling point; This is the second weight corresponding to the second sampling point; State vector The dimension; To meet Scalar parameters.

[0108] The updated or predicted values ​​for each data point are calculated using the following formula:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, The predicted value of the state vector; The covariance of the predicted values ​​of the state vector; It is the covariance matrix of the process noise; The predicted value is the measurement result; The covariance of the measured predicted values; This is the transpose of matrix H; The covariance between the state vector and the measurement; This is the filter gain; It is the difference between the predicted value and the observed value. for Time of the first The estimated values ​​of the model state vectors; for Time of the first The covariance of the estimated values ​​of the model state vectors.

[0121] Next, the likelihood value for each search sub-interval is calculated using the following formula:

[0122]

[0123] In the formula, L The likelihood value; To reach the first k The set of all measurements up to a given moment.

[0124] Furthermore, based on the Bayesian criterion, the probability of the filter is recursively updated using the likelihood value of each search subinterval:

[0125]

[0126]

[0127] In the formula, for The probability of the filter at time step.

[0128] Finally, the joint state vector estimate and joint covariance estimate of the target to be tracked are calculated according to the following formulas:

[0129]

[0130]

[0131]

[0132] In the formula, For joint state vector estimation; This is for joint covariance estimation.

[0133] In summary, the above formulas can be used to initialize and filter the state vector using bistatic distance and Doppler measurements provided by a single receiver, and to accurately estimate the state vector. The estimation results are as follows: Figures 4 to 10 As shown.

[0134] like Figure 2 , Figure 3 As shown, this embodiment of the invention provides a range-domain target tracking device based on bistatic range-Doppler observations. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 2The diagram shown is a hardware architecture diagram of an electronic device for a range-domain target tracking device based on bistatic range-Doppler observation, provided by an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of its electronic device reading the corresponding computer program from the non-volatile memory into memory and running it. This embodiment provides a range-domain target tracking device based on bistatic range-Doppler observation, comprising:

[0135] The determining unit 300 is used to determine the state vector of the target to be tracked based on the spatial information of the target; wherein the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler of the target relative to the transmitter, the Doppler of the target relative to the receiver, and the square of the velocity of the target;

[0136] The calculation unit 302 is used to calculate the state vector based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked.

[0137] The first processing unit 304 is used to initialize the state vector of the target to be tracked using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by the measurement vector;

[0138] The second processing unit 306 is used to filter the initial state vector and initial covariance of each search sub-interval based on a set of unscented Kalman filters to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein the joint state vector estimate and the joint covariance estimate are both used to track and locate the target to be tracked.

[0139] In this embodiment of the invention, the state vector is established using the following formula:

[0140]

[0141] In the formula, It is a state vector; for k The distance between the target and the transmitter must be tracked at all times. for k The distance between the target and the receiver must be tracked at all times; for k The Doppler effect of the target to be tracked relative to the transmitter at all times; for k The Doppler effect of the target to be tracked relative to the receiver at all times; for k The square of the velocity of the target to be tracked at all times; for k The Cartesian position of the target to be tracked at all times; for k The speed of the target to be tracked at all times; S The length of the baseline.

[0142] In this embodiment of the invention, the state evolution equation is calculated using the following formula:

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] In the formula, The sampling interval is... It is a two-dimensional identity matrix. for x The variance of the independent distributions along the axial direction is White Gaussian process noise; for y The variance of the independently distributed axial directions is White Gaussian process noise; a Data that is independent of process noise; b This is data related to process noise.

[0149] In this embodiment of the invention, when the first processing unit 304 performs initialization processing on the state vector of the target to be tracked using the Monte Carlo sampling strategy and the particle swarm optimization algorithm to obtain the initial state vector and initial covariance of the search sub-interval of the state vector, it specifically performs the following operations: based on the measurement vector, it obtains a first matrix composed of the measurement vectors at three time points and the covariance matrix corresponding to the first matrix; it calculates the first matrix and the covariance matrix using the Monte Carlo sampling strategy to obtain the first sampling point and the first weight of the first sampling point; based on the first sampling point and the first weight, it calculates the state evolution equation using the particle swarm optimization algorithm to obtain the initial value of the state vector and the initial covariance of each search sub-interval.

[0150] In this embodiment of the invention, the initial state vector and initial covariance of the search sub-interval are calculated using the following formulas:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] In the formula, This is the first matrix; for k The measurement vector at time; for Transpose of; It is the covariance matrix; for k The covariance matrix of the measurements at each time point; It is an empty matrix of size 2×2; This is the first sampling point; The first weight is the value corresponding to the first sampling point; l =0, 1, 2; g A function to generate Gaussian-distributed sampling points. N It is the number of sampling points; Optimization problems to be solved by the particle optimization algorithm; The standard deviation of distance measurement noise; The standard deviation of noise is measured using Doppler. These are the initial values ​​for the state vector; For the first Posterior sampling points of each search sub-interval; This is the result after iteration of the particle swarm optimization algorithm; For the first The initial state vector of each search sub-interval; For the first The initial covariance of each search sub-interval; , N r The number of sub-intervals for the distance value. N ddenoted as the number of Doppler search subintervals.

[0160] In this embodiment of the invention, when the second processing module 306 performs filtering processing on the measurement vector using a set of unscented Kalman filters based on the initial state vector and initial covariance of each search sub-interval to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked, it specifically performs the following operations: calculating the second sampling point and the second weight corresponding to the second sampling point based on the initial state vector and initial covariance; calculating the second sampling point using the state evolution equation, and calculating the probability of the search sub-interval based on the calculation result and the second weight; and calculating the joint state vector estimate and joint covariance estimate based on the probability of the sub-interval.

[0161] In this embodiment of the invention, the joint state vector estimate and joint covariance estimate of the target to be tracked are calculated using the following formulas:

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179] In the formula, This is the second sampling point; This is the second weight corresponding to the second sampling point; State vector The dimension; To meet scalar parameters; The predicted value of the state vector; The covariance of the predicted values ​​of the state vector; It is the covariance matrix of the process noise; The predicted value is the measurement result; The covariance of the measured predicted values; For matrix Transpose of; The covariance between the state vector and the measurement; This is the filter gain; It is the difference between the predicted value and the observed value. for Time of the first The estimated values ​​of the model state vectors; for Time of the first The covariance of the estimated values ​​of the model state vectors; L The likelihood value; For the filter at time k; To reach the first k The set of all measurements up to a given moment; For joint state vector estimation; This is for joint covariance estimation.

[0180] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a range-domain target tracking device based on bistatic range-Doppler observations. In other embodiments of the present invention, a range-domain target tracking device based on bistatic range-Doppler observations may include more or fewer components than illustrated, or combine some components, split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0181] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0182] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a range domain target tracking method based on bistatic range-Doppler observations according to any embodiment of this invention.

[0183] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a range-domain target tracking method based on bistatic range-Doppler observations according to any embodiment of this invention.

[0184] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0185] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0186] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0187] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0188] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0189] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0190] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A range-domain target tracking method based on bistatic range-Doppler observations, characterized in that, include: Based on the spatial information of the target to be tracked, a state vector of the target is determined; wherein, the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler effect of the target relative to the transmitter, the Doppler effect of the target relative to the receiver, and the square of the target's velocity; the state vector is established by the following formula: In the formula, It is a state vector; for k The distance between the target and the transmitter must be tracked at all times. for k The distance between the target and the receiver must be tracked at all times; for k The Doppler effect of the target to be tracked relative to the transmitter at all times; for k The Doppler effect of the target to be tracked relative to the receiver at all times; for k The square of the velocity of the target to be tracked at all times; for k The Cartesian position of the target to be tracked at all times; for k The speed of the target to be tracked at all times; S The length of the baseline; The state vector is calculated based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked; the state evolution equation is calculated using the following formula: The evolution equation is: in, Similarly, we can conclude that: in, Combining the above equations, we obtain the state evolution equation of the target to be tracked. for: In the formula, The sampling interval is... It is a two-dimensional identity matrix. for x The variance of the independent distributions along the axial direction is White Gaussian process noise; for y The variance of the independently distributed axial directions is White Gaussian process noise; a Data that is independent of process noise; b For data related to process noise; The state vector of the target to be tracked is initialized using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by measurement vectors; A set of unscented Kalman filters is used to filter the measurement vector based on the initial state vector and initial covariance of each search sub-interval to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein, the joint state vector estimate and the joint covariance estimate are both used to track and locate the target to be tracked.

2. The method according to claim 1, characterized in that, The initialization process of the state vector of the target to be tracked using the Monte Carlo sampling strategy and particle swarm optimization algorithm yields the initial state vector and initial covariance for each search sub-interval, including: Based on the measurement vectors, a first matrix composed of the measurement vectors at three time points and the covariance matrix corresponding to the first matrix are obtained. The first sampling point and the first weight of the first sampling point are obtained by calculating the first matrix and the covariance matrix using the Monte Carlo sampling strategy; Based on the first sampling point and the first weight, the state evolution equation is calculated using the particle swarm optimization algorithm in each search sub-interval to obtain the initial state vector and initial covariance of each search sub-interval.

3. The method according to claim 2, characterized in that, The initial state vector and initial covariance of the search sub-interval are calculated using the following formula: In the formula, This is the first matrix; for k The measurement vector at time; for transpose; This is the covariance matrix corresponding to the first matrix; for k The covariance matrix of time measurements; It is an empty matrix of size 2×2; This is the first sampling point; The first weight is the value corresponding to the first sampling point; l =0, 1, 2; g A function to generate Gaussian-distributed sampling points. It is the number of sampling points; Optimization problems to be solved by the particle optimization algorithm; The standard deviation of the distance measurement noise; The standard deviation of noise is measured using Doppler. These are the initial values ​​for the state vector; For the first Posterior sampling points of each search sub-interval; This is the result after iteration of the particle swarm optimization algorithm; For the first The initial state vector of each search sub-interval; For the first The initial covariance of each search sub-interval; , N r The number of sub-intervals for the distance value. N d is the number of Doppler search subintervals.

4. The method according to claim 3, characterized in that, The step of using a set of unscented Kalman filters to filter the measurement vector based on the initial state vector and initial covariance of each search sub-interval to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked includes: Based on the initial state vector and the initial covariance, the second sampling point and the second weight corresponding to the second sampling point are calculated; The state evolution equation is used to calculate the second sampling point, and the probability of the search sub-interval is calculated based on the calculation result and the second weight. Based on the probability, the joint state vector estimate and the joint covariance estimate are calculated.

5. The method according to claim 4, characterized in that, The joint state vector estimate and joint covariance estimate of the target to be tracked are calculated using the following formulas: In the formula, This is the second sampling point; This is the second weight corresponding to the second sampling point; State vector The dimension; To meet scalar parameters; The predicted value of the state vector; The covariance of the predicted values ​​of the state vector; It is the covariance matrix of the process noise; The predicted value is the measurement result; The covariance of the measured predicted values; For matrix transpose; The covariance between the state vector and the measurement; This is the filter gain; It is the difference between the predicted value and the observed value of the measurement; for Time of the first The estimated values ​​of the model state vectors; for Time of the first The covariance of the estimated values ​​of the model state vectors; The likelihood value; for The probability at any given moment; To reach the first k The set of all measurements up to a given moment; For joint state vector estimation; This is for joint covariance estimation.

6. A range-domain target tracking device based on bistatic range-Doppler observation, applied to the method as described in any one of claims 1-5, characterized in that, include: A determining unit is configured to determine the state vector of the target to be tracked based on the spatial information of the target; wherein the state vector includes the distance from the target to the transmitter, the distance from the target to the receiver, the Doppler effect of the target relative to the transmitter, the Doppler effect of the target relative to the receiver, and the square of the velocity of the target. The calculation unit is used to calculate the state vector based on the Cartesian approximate uniform linear motion model to obtain the state evolution equation of the target to be tracked. The first processing unit is used to initialize the state vector of the target to be tracked using a Monte Carlo sampling strategy and a particle swarm optimization algorithm to obtain the initial state vector and initial covariance of each search sub-interval; wherein, the search sub-interval is obtained by dividing the search interval through a preset scheme, and the search interval is determined by measurement vectors; The second processing unit is used to filter the measurement vector using a set of unscented Kalman filters based on the initial state vector and initial covariance of each search sub-interval, to obtain the joint state vector estimate and joint covariance estimate of the target to be tracked; wherein the joint state vector estimate and the joint covariance estimate are both used to track and locate the target to be tracked.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-5.

Images