Active and passive radar resource allocation method for maneuvering target tracking and related equipment

Abstract

The invention relates to the technical field of radar detection, and discloses an active and passive radar resource allocation method for maneuvering target tracking and related equipment. The method comprises the steps of establishing an active and passive radar network system and a related model; based on an active and passive radar network system and related models, target state estimation and prediction are carried out by using an interactive multi-model algorithm, and target tracking performance measurement is derived based on optimal fitting Gaussian according to a target state prediction result; based on target tracking performance measurement, taking minimization of normalized resource consumption of the system as a target and taking total resource limitation and preset tracking precision of the system as constraints, establishing an active and passive radar resource allocation model; and performing decoupling solution on the active and passive radar resource allocation model based on a decoupling solution algorithm to obtain an active and passive radar resource allocation result. According to the invention, maneuvering target precision-guaranteed tracking is realized, and emission resources of the system and the number of fusion nodes are saved.

Description

Technical Field

[0001] The present invention relates to the field of radar detection technology, and in particular to an active and passive radar resource allocation method for maneuvering target tracking and related equipment. Background Art

[0002] Non-cooperative targets have the characteristics of low scattering cross-section (RCS) and strong mobility. During the penetration process, they may cause the observation traces of single-station ground air defense radars to be intermittent, making it difficult to form a stable and continuous track. In order to detect weak targets at a long distance, single-station radars need to transmit high-power signals, which exposes their positions and makes it difficult to survive in a strong confrontation environment. Bistatic radar uses spatial diversity to obtain the forward scattering signal of the target. It is networked with a single-station radar and reuses the transmitter, which can not only reduce the transmission cost and radiation power, but also improve the trace continuity of non-cooperative targets through multiple perspectives. Therefore, active and passive radar networks have attracted widespread attention from researchers. The active and passive radar network consists of a transmitting station deployed in the rear and multiple receiving stations placed in the front line. Its detection configuration is shown in the attached figure. Figure 1 shown.

[0003] In active and passive radar target tracking applications, the tracking accuracy depends on the power of the transmitting station on the one hand, and on the topological configuration of the transmitting-receiving pair on the other. In theory, although the full-power radiated radar signal helps to detect weak targets at a long distance, it is easy to expose the radar position; the more transmitting and receiving pairs there are, the higher the tracking accuracy under the centralized data fusion architecture, but the computing load and communication requirements involved will also increase accordingly.

[0004] How to resolve the above two contradictions and achieve covert detection of non-cooperative targets while ensuring accurate tracking has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an active and passive radar resource allocation method and related equipment for maneuvering target tracking in view of the problems existing in the prior art.

[0006] In a first aspect, the present application provides an active and passive radar resource allocation method for maneuvering target tracking, comprising:

[0007] Establishing an active and passive radar network system and related models; wherein the related models include a target motion model and a radar observation model;

[0008] Based on the active and passive radar network system and related models, the interactive multi-model algorithm is used to estimate and predict the target state, and the target tracking performance measure is derived based on the best fitting Gaussian according to the target state prediction results;

[0009] Based on the target tracking performance measurement, the active and passive radar resource allocation model is established with the goal of minimizing the system normalized resource consumption and the constraints of limited total resources and system preset tracking accuracy.

[0010] The active and passive radar resource allocation model is decoupled and solved based on the decoupling solution algorithm to obtain the active and passive radar resource allocation results.

[0011] In a second aspect, the present application provides an active and passive radar resource allocation system for maneuvering target tracking, which is used to implement the active and passive radar resource allocation method for maneuvering target tracking provided by the above technical solution, and the system includes:

[0012] A system model building module is used to build an active and passive radar network system and related models; wherein the related models include a target motion model and a radar observation model;

[0013] A performance measure determination module is used to estimate and predict the target state based on the active and passive radar network system and related models using an interactive multiple model algorithm, and derive the target tracking performance measure based on the best fitting Gaussian according to the target state prediction result;

[0014] The resource allocation model building module is used to establish the active and passive radar resource allocation model based on the target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource limitation and the system preset tracking accuracy as constraints;

[0015] The resource allocation model solving module is used to perform decoupling solution on the active and passive radar resource allocation model based on the decoupling solution algorithm to obtain the active and passive radar resource allocation results.

[0016] In a third aspect, the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enable the computer to execute the active and passive radar resource allocation method for maneuvering target tracking as provided in the above technical solution.

[0017] In a fourth aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the active and passive radar resource allocation method for maneuvering target tracking as provided in the above technical solution is implemented.

[0018] In a fifth aspect, the present application also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the active and passive radar resource allocation method provided by the above solution.

[0019] The beneficial effects of the present invention are as follows: aiming at the hidden target detection requirements of active and passive radar systems, the influence of system topology and transmission power on maneuverable target tracking is taken into consideration, and a maneuverable target tracking measure related to system topology and transmission power is derived based on the best fitting Gaussian and interactive multi-models. A method for optimizing the topology and transmission power resources of an active and passive radar network combined system for maneuverable target tracking is proposed, which realizes precision tracking of maneuverable targets and saves the system's transmission resources and the number of fusion nodes.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of active and passive radar network detection configuration;

[0022] Figure 2 A flow chart of an active and passive radar resource allocation method for maneuvering target tracking shown as an exemplary embodiment of the present application;

[0023] Figure 3 for Figure 2 A flowchart of S2 in an exemplary embodiment shown in the embodiment;

[0024] Figure 4 It is a schematic diagram of radar position and target motion trajectory;

[0025] Figure 5 Schematic diagram of target RCS changing with time;

[0026] Figure 6 A schematic diagram showing the comparison results of the tracking accuracy between the method provided by the embodiment of the present invention and the benchmark method;

[0027] Figure 7 A schematic diagram showing a comparison result of the normalized resource saving rate between the method provided by an embodiment of the present invention and a benchmark method;

[0028] Figure 8 A schematic diagram of resource allocation results obtained according to a method provided in an embodiment of the present invention;

[0029] Fig. 9 A block diagram of an active and passive radar resource allocation system for maneuvering target tracking is shown as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0030] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0031] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0032] In the prior art, most active and passive radar networks schedule resources based on the premise that the target RCS remains unchanged and the target moves at a near-uniform speed. The relationship between the target RCS and the dual-station angle (the angle between the transmitting station, the target, and the receiving station) and its impact on resource scheduling have not been analyzed, making it difficult to achieve active and passive radar concealed maneuvering target tracking under strong confrontation conditions. To this end, the embodiment of the present invention proposes a joint system topology configuration and a transmitting station power optimization method for the maneuvering target tracking problem, which saves the resources of active and passive radars while ensuring precision tracking.

[0033] The solution provided in the embodiments of the present invention can be executed by any electronic device, for example, a terminal device, or jointly executed by a terminal device and a server. The server can be an independent server, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, etc., and is not limited here.

[0034] Figure 2FIG. 1 is a flow chart of an active and passive radar resource allocation method for maneuvering target tracking shown in an exemplary embodiment of the present application. Figure 2 As shown, in an exemplary embodiment, the active and passive radar resource allocation method for maneuvering target tracking may include steps S1 to S4, which are described in detail as follows:

[0035] S1, establishing an active and passive radar network system and related models; wherein the related models include a target motion model and a radar observation model;

[0036] S2, based on the active and passive radar network system and related models, uses the interactive multiple model algorithm to estimate and predict the target state, and derives the target tracking performance measure based on the best fitting Gaussian according to the target state prediction result; among them, the target tracking performance measure is related to the topological configuration and transmission power of the active and passive radar network system;

[0037] S3, based on the target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource limit and the system preset tracking accuracy as constraints, an active and passive radar resource allocation model is established;

[0038] S4, based on the decoupling solution algorithm, the active and passive radar resource allocation model is decoupled and solved to obtain the active and passive radar resource allocation results.

[0039] The embodiment of the present invention aims at the hidden target detection demand of active and passive radar systems, takes into account the influence of system topology and transmission power on maneuverable target tracking, derives maneuverable target tracking measures related to system topology and transmission power based on the best fit Gaussian and interactive multi-models, and proposes a method for optimizing the topology and transmission power resources of the active and passive radar network combined system for maneuverable target tracking, thereby achieving precision tracking of maneuverable targets and saving the system's transmission resources and the number of fusion nodes.

[0040] It should be noted that Figure 2 In an exemplary embodiment, S1 in the illustrated embodiment can be implemented in the following manner, which is described in detail as follows:

[0041] 1. Establish active and passive radar network systems.

[0042] Consider a system consisting of 1 transmitting station and N r The active and passive radar network consists of receiving stations, and the location of the transmitting station is set as (x t ,y t ) T , the position of the i-th receiving station is (x i ,y i ) T , the time synchronization between the sending and receiving nodes has been completed, and the tracking data rate and scheduling period are both set to T0. kAt time k (referred to as time k), the state of the target is in and Represent the position, velocity and acceleration of the target respectively.

[0043] 2. Establish a motion model for targets in the active and passive radar network system and an observation model for each radar.

[0044] The embodiment of the present invention uses a multi-model including possible motion modes to describe the motion process of a maneuvering target. Model variable r k It is represented by a discrete random process, and its value is one of s possible models. The target motion model is:

[0045]

[0046] Among them, ξ k represents the state of the target at time k, r k represents the model variables that affect the time from k-1 to k. is the k-1 moment r k The corresponding state transfer matrix is, represents the k-1 moment r k The corresponding zero-mean process noise has a covariance matrix of The initial probability of the model is μ n,k＝1 =Pr{r k＝1 =n} and μ n,1 ≥0, as well as The evolution of the model sequence is represented as a first-order homogeneous Markov chain containing s states with transition probabilities π mn for:

[0047] π mn =Pr{r k =n|r k-1 =m} (2)

[0048] Where m,n∈S. The order of the state transfer matrix S is s×s, and its (m,n)th element [S] m,n =π m,n satisfy π m,n ≥0,

[0049] Assuming that the active radar can measure the radial distance and azimuth of the target, its measurement model is:

[0050] z k =h(ξ k )+u k (3)

[0051] Among them, zk represents the measurement value of the active radar at time k, including ranging and azimuth, h(ξ k ) represents the measurement function of active radar, u k Represents the measurement error of the active radar, which has a mean of zero and a covariance of R k Gaussian distribution, R k It is related to the transmit power and the target RCS. Similarly, the measurement model of passive radar i is:

[0052] z i,k =h i (ξ k )+u i,k (4)

[0053] Among them, z i,k represents the measurement value of the passive radar, u i,k represents the measurement error of passive radar i, which has a mean of zero and a covariance of R i,k Gaussian distribution, R i,k Related to the transmit power and target RCS, the measurement function h i (ξ k )for:

[0054]

[0055] In formula (5) represents the distance between the transmitting station and the target (also the distance measurement function of the active radar), (x t,k ,y t,k ) represents the location of the transmitting station, represents the distance from the target to the receiving station, (x i,k ,y i,k ) represents the receiving station position, and the measurement error covariance is in and are the Cramer-Rao bounds for the maximum likelihood estimates of the two-station distance and azimuth measurements:

[0056]

[0057] In formula (6), p t,k Indicates the transmit power, B t,k represents the transmitter bandwidth, θ i,3dB represents the effective beam width, κ ik The target RCS of the i-th receiver and transmitter forming a dual-station radar can be calculated by the empirical formula:

[0058] κ i,k =κ t,k [1+exp(K|α|-2.4K-1)] (7)

[0059] Among them, κ t,k represents the RCS of the self-transmitting and self-receiving radar, α represents the dual-station angle (in radians), and K represents the empirical coefficient determined by the target structure and complexity:

[0060]

[0061] In formula (8), A represents the target area projected perpendicular to the radar beam direction, and λ represents the wavelength.

[0062] It should be noted that Figure 2 In an exemplary embodiment, S2 in the illustrated embodiment uses an interactive multi-model algorithm to estimate and predict the target state based on an active and passive radar network system and related models. This can be achieved in the following manner, which is described in detail as follows: Based on the target motion model and the radar observation model, the interactive multi-model algorithm is used to calculate the target state estimation value, state estimation covariance, and target state estimation value and state estimation covariance after multi-model interaction of each model. Then, the state prediction value and state prediction covariance can be obtained based on the state estimation value and state estimation covariance, thereby realizing target state prediction.

[0063] See also Figure 3 , Figure 3 for Figure 2 The flowchart of S2 in the embodiment shown in an exemplary embodiment is to derive a target tracking performance measure based on the best fitting Gaussian according to the target state estimation and prediction results. Figure 3 As shown, deriving the target tracking performance measure based on the best fitting Gaussian according to the target state estimation and prediction results may include steps S21 to S24. The target tracking performance measure is obtained through the above steps, which is described in detail as follows:

[0064] S21, the multi-model prior probability density function (Probability Density Function, PDF) is approximated to a single model PDF using the best fitting Gaussian, that is, the model uncertainty of the target motion model in the relevant multi-model is described as the mean and covariance corresponding to the best fitting Gaussian distribution.

[0065] In the multi-model tracking framework, since the target state and model probability need to be jointly estimated, maneuvering target tracking is a mixed estimation problem, and the joint probability density function PDF of the target state and measurement does not meet the quadratic differentiability condition. To this end, the embodiment of the present invention uses the best fitting Gaussian to approximate the multi-model prior PDF to a single model PDF, that is, the model uncertainty in formula (1) is described as the mean and covariance corresponding to the best fitting Gaussian distribution, so that the joint probability density function PDF of the target state and measurement is continuously differentiable.

[0066] S22, determining the target state transfer matrix and the state prediction covariance matrix through the best fitting Gaussian according to the target state prediction result.

[0067] In the embodiment of the present invention, the target state transfer matrix and the state prediction covariance matrix are determined by the best fitting Gaussian according to the target state prediction result, and the formula is as follows:

[0068]

[0069] Among them, F k-1 represents the target state transfer matrix, n represents the nth motion model of the target, S represents the target motion model set, μ n,kk-1 represents the transition probability of the target motion model n from time k-1 to time k, represents the state transfer matrix of the target motion model n, P k|k-1 represents the state prediction covariance from the k-1 moment prediction to the k moment target, P k-1 represents the state estimation covariance of the target at time k, represents the estimated state value of the target at time k-1, and P k-1 The target measurement value is filtered by the interactive multiple model algorithm. express The transpose of express The transpose of represents the process noise covariance matrix of model n at time k-1, Indicates F k-1 The transpose of .

[0070] S23, determining a predicted Fisher information matrix according to the target state transfer matrix and the state prediction covariance matrix.

[0071] After the best-fit Gaussian approximation, the predicted PDF of the target obeys the Gaussian distribution. In the embodiment of the present invention, the predicted Fisher information matrix is ​​determined according to the target state transfer matrix and the state prediction covariance matrix, and the formula is as follows:

[0072]

[0073] Among them, ξ k represents the state of the target at time k, Z 1:k represents the set of measurement values ​​of the target from time 1 to time k, J(ξ k |Z 1:k ) represents the predicted Fisher information matrix of the target, N represents the inverse matrix of the state prediction covariance from the prediction at time k-1 to the target at time k, r represents the number of receiving stations, p t,krepresents the transmission power of the transmitting station at time k, b i,k represents the selected variable of receiving station i, and denotes the inverse matrix of the fixed parameter matrix in the active radar and passive radar measurement covariance matrix, H t,k and H i,k Represent the Jacobian matrices of active radar and passive radar respectively, and H t,k and H i,k The inverse matrix of k|k-1 =F k-1 ξ k-1 Represents the predicted state of the target from time k-1 to time k.

[0074] S24, determining a predicted conditional Cramer-Rao bound according to the inverse of the predicted Fisher information matrix, and using the trace of the normalized predicted conditional Cramer-Rao bound as a target tracking performance measure.

[0075] The inverse of the predicted Fisher information matrix is ​​the predicted conditional Cramer-Rao bound PC-CRLB, whose diagonal elements correspond to the performance lower bounds of the target state component prediction accuracy, and therefore have different dimensions and cannot be directly used as an objective function. The present invention uses the trace of the normalized PC-CRLB as a target tracking performance measure. Specifically, the target tracking performance measure formula is as follows:

[0076] f(p t,k ,b k )=tr(ΛJ -1 (ξ k |Z 1:k )Λ T ) (12)

[0077] Among them, p t,k represents the transmission power of the transmitting station at time k, represents the receiving station selection vector, b 1,k represents the selection variable of the first receiving station, Indicates the Nth r The selection variables of the receiving stations, b r,k =1 means the rth receiving station is selected at the kth time, r = 1, 2, 3...N r ; is the normalized matrix, Ι2 is the unit matrix, represents the Kronecker product, T0 represents the scheduling time interval, represents the vector diagonalization matrix operation, tr(·) represents the trace, J -1 (ξ k |Z 1:k ) represents J(ξ k |Z1:k ) is the inverse matrix of .

[0078] The above embodiment of the present invention obtains multi-radar measurement values ​​based on the target motion model and the active and passive radar observation model, uses the interactive multi-model algorithm to estimate and predict the target state, and derives the target tracking performance measure related to the system topology configuration and transmission power based on the best fitting Gaussian. The embodiment of the present invention establishes the connection between the tracking performance and the transmission resources and topology configuration, laying a foundation for the construction of the resource allocation model.

[0079] Optionally, Figure 2 In an exemplary embodiment, S3 in the illustrated embodiment is an active and passive radar resource allocation model, and the formula is as follows:

[0080]

[0081] The objective function is the sum of the normalized power utilization rate and the node utilization rate, 1 T represents the transpose of a column vector of all 1s, p t,k represents the transmission power of the transmitting station at time k, b k represents the vector of receiving stations selected at time k, N r represents the number of receiving stations, p total represents the total transmission power of the transmitting station, γ k represents the target tracking accuracy requirement preset at time k, p min represents the minimum radiated power, N r dimensional 0,1 vector set. Since the optimization variables include continuous power and discrete node selection variables, and the tracking accuracy function f(p t,k ,b k ) is nonlinear, so Equation (13) is a mixed integer nonlinear programming problem.

[0082] In the embodiment of the present invention, based on the maneuvering target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource finiteness and the system preset tracking accuracy as constraints, an active and passive radar network joint system topology configuration and transmission power joint optimization model, namely, an active and passive radar resource allocation model, is established. Using the active and passive radar resource allocation model, the embodiment of the present invention can achieve precision tracking of maneuvering targets under low interception requirements and extend the working time of the active radar engine.

[0083] In a physical sense, the greater the transmission power, the more passive nodes are used, and the higher the tracking accuracy. On the one hand, high radiation power does not allow the radar to survive under strong confrontation conditions. On the other hand, the more passive nodes are used, the higher the computational load and communication requirements involved in centralized processing, and each dual-station channel has a different observation angle, which has a large difference in the gain of tracking accuracy.

[0084] If the preset tracking accuracy requirement is too high and the existing resources cannot meet the accuracy requirement, model (13) cannot be solved. Therefore, before solving, it is necessary to determine whether the existing resources are sufficient. In addition, after the variables are selected for a given node, model (13) degenerates into a convex power optimization problem, which can be solved by the interior point method under the condition of sufficient resources. k and When , the power consumed is the least, and the active and passive radar resource allocation model is transformed into the following formula:

[0085]

[0086] According to the characteristics of the topological configuration of the active and passive radar network joint system and the transmission power joint optimization model, the embodiment of the present invention designs a decoupling solution algorithm to obtain a resource allocation result.

[0087] Based on the model structure, an embodiment of the present invention proposes a fast decoupling solution algorithm. Figure 2 In the illustrated embodiment, S4 in an exemplary embodiment may specifically include steps S41 to S45. The active and passive radar resource allocation results are obtained through the above steps, which are described in detail as follows:

[0088] S41. Calculate the full power distribution and select the objective function corresponding to all passive receiving stations. If the accuracy requirement is met, execute S42. Otherwise, exit the optimization and let the optimal solution be 1 Nr N r dimensional all-1 column vector;

[0089] S42. gradually try to delete a receiving station, and calculate each possible corresponding target tracking performance measurement value;

[0090] S43. If the target tracking performance measure after deleting a node is less than the preset tracking accuracy requirement γ k possible situations, find the possible situation with the smallest target tracking performance measure among all possible situations, set the receiving station selection variable corresponding to this possible situation to zero, and get

[0091] S44.Fixed The interior point method is used to solve the transformed active and passive radar resource allocation model and update

[0092] S45. If Return to S42, otherwise output and

[0093] The procedural descriptions corresponding to the above steps S41 to S45 are as follows:

[0094] A, order Tracking accuracy If the accuracy requirement is met, execute B, otherwise exit the optimization;

[0095] B, order Let i=1 and repeat:

[0096] (a) If make if

[0097] make Calculate f i (p t,k ,b k )| bi,k＝0 ;

[0098] (b) Let i=i+1,

[0099] Until i=N r ;

[0100] C, if , so that f i (p t,k ,b k )| bi,k＝0 ≤γ k ,make make to D;

[0101] D, fixed The interior point method is used to solve the transformed active and passive radar resource allocation model and update

[0102] E, if Return B, otherwise output and

[0103] With respect to the active and passive radar resource allocation method for maneuvering target tracking provided in the above embodiment, on the basis of a given set of specific experimental parameters, the technical effects of the present invention are explained in combination with the following embodiments.

[0104] The active and passive radar network considered in the embodiment of the present invention consists of one active radar (self-transmitting and self-receiving) and N r =3 passive radars (receiving stations), the transmitting station position is set as the coordinate origin, and the positions of receiving stations 1-3 are set to [20,18] respectively T , [40,20] T and [60,18] T The bandwidth of the transmitted signal is B t =1MHz, effective beam width is θ i,3dB=1°, the total transmission power is p total =200kw, minimum transmission power p min =0.1p total , the scheduling time interval is T0 = 3s, and the wavelength is λ = 0.3m. The target moves in a nearly uniform linear motion in the first 14 frames, in a nearly uniform turning motion in frames 15 to 28, and in a nearly uniform accelerated linear motion in frames 29 to 40. The tracking accuracy is set to γ k = 80m. The radar position and target motion trajectory are as follows Figure 4 As shown. The initial model probability of the interacting multiple model algorithm is μ1 = [0.8, 0.1, 0.1] T , the model transfer matrix is ​​set to have main diagonal elements of 0.8 and the remaining elements of 0.1.

[0105] In the current frame, the active radar can estimate the target RCS based on the echo power, and then predict the target RCS in the next frame based on equation (7) and the target position. The change of target RCS over time is shown as Figure 5 shown.

[0106] Figure 6 and Figure 7 Schematic diagrams showing the comparison results of the tracking accuracy and resource saving rate of the method proposed in the embodiment of the present invention relative to the benchmark method, wherein the benchmark method refers to full power allocation and selection of all passive radars. It can be seen that the method proposed in the embodiment of the present invention can control the tracking accuracy to be exactly equal to (occasionally less than) the preset accuracy requirement, thereby saving the most resources. When the target is maneuvering (the 30th frame), the system will increase the radiated power (e.g. Figure 8 In the tracking process, passive radar 3 was not selected because the distance between the two stations was far in the first 14 frames, the signal-to-noise ratio was low, and the RCS of the radar was low in the subsequent frames, as shown in Figure 2. Figure 5 shown.

[0107] Fig. 9 FIG. 1 is a block diagram of an active and passive radar resource allocation system for maneuvering target tracking according to an exemplary embodiment of the present application. Fig. 9 As shown, in an exemplary embodiment, the active and passive radar resource allocation system for maneuvering target tracking is used to implement the active and passive radar resource allocation method for maneuvering target tracking provided in any of the above embodiments. The system may include a system model building module, a performance measurement determination module, a resource allocation model building module and a resource allocation model solving module.

[0108] The system model building module is used to build the active and passive radar network system and related models; wherein the related models include the target motion model and the radar observation model;

[0109] A performance measurement determination module is used to estimate and predict the target state based on the active and passive radar network system and related models using an interactive multiple model algorithm, and derive the target tracking performance measurement based on the best fitting Gaussian according to the target state prediction result, wherein the target tracking performance measurement is related to the topological configuration and transmission power of the active and passive radar network system;

[0110] The resource allocation model building module is used to establish the active and passive radar resource allocation model based on the target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource limitation and the system preset tracking accuracy as constraints;

[0111] The resource allocation model solving module is used to perform decoupling solution on the active and passive radar resource allocation model based on the decoupling solution algorithm to obtain the active and passive radar resource allocation results.

[0112] An embodiment of the present invention further provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enable the computer to execute the active and passive radar resource allocation method for maneuvering target tracking provided in any of the above embodiments.

[0113] An embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the active and passive radar resource allocation method for maneuvering target tracking provided in any of the above embodiments is implemented.

[0114] The embodiment of the present invention further provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the active and passive radar resource allocation method for maneuvering target tracking provided in the above various optional embodiments.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0117] 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 embodiments of the present invention.

[0118] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for allocating active and passive radar resources for tracking maneuvering targets, characterized in that: include: Establishing an active and passive radar network system and related models; wherein the related models include a target motion model and a radar observation model; Based on the active and passive radar network system and related models, the interactive multiple model algorithm is used to estimate and predict the target state, and the target tracking performance measure is derived based on the best fitting Gaussian according to the target state prediction result; Based on the target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource being limited and the system preset tracking accuracy being constrained, an active and passive radar resource allocation model is established; The active and passive radar resource allocation model is decoupled and solved based on a decoupling solution algorithm to obtain an active and passive radar resource allocation result.

2. The method according to claim 1, characterized in that The target tracking performance measure is derived based on the best fitting Gaussian according to the target state estimation and prediction results, including: Describing the model uncertainty of the target motion model in the related multi-model as the mean and covariance corresponding to the best fitting Gaussian distribution; Determine the target state transfer matrix and the state prediction covariance matrix by the best fitting Gaussian according to the target state estimation and prediction results; Determine a predicted Fisher information matrix according to the target state transfer matrix and the state prediction covariance matrix; A predicted conditional Cramer-Rao bound is determined based on the inverse of the predicted Fisher information matrix, and a trace of the normalized predicted conditional Cramer-Rao bound is used as a target tracking performance measure.

3. The method according to claim 2, characterized in that The target state transfer matrix and the state prediction covariance matrix are determined by the best fitting Gaussian according to the target state estimation and prediction results, and the formula is as follows: Among them, F k-1 represents the target state transfer matrix, n represents the nth motion model of the target, S represents the target motion model set, μ n,k|k-1 represents the transition probability of the target motion model n from time k-1 to time k, represents the state transfer matrix of the target motion model n, P k|k-1 represents the state prediction covariance from the k-1 moment prediction to the k moment target, P k-1 represents the state estimation covariance of the target at time k, represents the estimated state value of the target at time k-1, and P k-1 The target measurement value is filtered by the interactive multiple model algorithm. express The transpose of express The transpose of represents the process noise covariance matrix of model n at time k-1, Indicates F k-1 The transpose of .

4. The method according to claim 3, characterized in that The predicted Fisher information matrix is ​​determined according to the target state transfer matrix and the state prediction covariance matrix, and the formula is as follows: Among them, ξ k represents the state of the target at time k, Z 1:k represents the set of measurement values ​​of the target from time 1 to time k, J(ξ k |Z 1:k ) represents the predicted Fisher information matrix of the target, N represents the inverse matrix of the state prediction covariance from the prediction at time k-1 to the target at time k, r represents the number of receiving stations, p t,k represents the transmission power of the transmitting station at time k, b i,k represents the selected variable of receiving station i, and denotes the inverse matrix of the fixed parameter matrix in the active radar and passive radar measurement covariance matrix, H t,k and H i,k Represent the Jacobian matrices of active radar and passive radar respectively, and H t,k and H i,k The inverse matrix of k|k-1 =F k-1 ξ k-1 Represents the predicted state of the target from time k-1 to time k.

5. The method according to claim 4, characterized in that The target tracking performance measure f(p t,k ,b k )The formula is as follows: f(p t,k ,b k )=tr(ΛJ -1 (ξ k |From 1:k )Λ T ) Among them, p t,k represents the transmission power of the transmitting station at time k, represents the receiving station selection vector, b 1,k represents the selection variable of the first receiving station, Indicates the Nth r The selection variables of the receiving stations, b r,k =1 means the rth receiving station is selected at the kth time, r = 1, 2, 3...N r ; is the normalized matrix, Ι2 is the unit matrix, represents the Kronecker product, T0 represents the scheduling time interval, represents the vector diagonalization matrix operation, tr(·) represents the trace, J -1 (ξ k |Z 1:k ) represents J(ξ k |Z 1:k ) is the inverse matrix of .

6. The method according to any one of claims 1 to 5, characterized in that: The active and passive radar resource allocation model is as follows: The objective function is the sum of the normalized power utilization rate and the node utilization rate, 1 T represents the transpose of a column vector of all 1s, p t,k represents the transmission power of the transmitting station at time k, b k represents the vector of receiving stations selected at time k, N r represents the number of receiving stations, p total represents the total transmission power of the transmitting station, γ k represents the target tracking accuracy requirement preset at time k, p min represents the minimum radiated power, N r A set of 0,1-dimensional vectors; When given b k and When , the power consumed is the least, and the active and passive radar resource allocation model is converted into the following formula:

7. The method according to claim 6, characterized in that The decoupling solution of the active and passive radar resource allocation model is performed based on the decoupling solution algorithm to obtain the active and passive radar resource allocation result, including: Step 1. Calculate the total power allocation and select the tracking performance measurement corresponding to all passive receiving stations. If the accuracy requirement is met, execute step 2. Otherwise, exit the optimization and let the optimal solution be 1 Nr N r dimension all-1 column vector; Step 2. Try to delete a receiving station step by step and calculate the target tracking performance measurement value of each possible corresponding target; Step 3. If the target tracking performance measure after deleting a node is less than the preset tracking accuracy requirement γ k possible situations, find the possible situation with the smallest target tracking performance measure among all possible situations, set the receiving station selection variable corresponding to this possible situation to zero, and get Step 4. Fix The interior point method is used to solve the transformed active and passive radar resource allocation model and update Step 5. If Return to step 2, otherwise output and 8. An active and passive radar resource allocation system for tracking maneuvering targets, characterized in that: For implementing the method described in any one of claims 1 to 7, the system comprises: A system model building module is used to build an active and passive radar network system and related models; wherein the related models include a target motion model and a radar observation model; A performance measure determination module is used to estimate and predict the target state based on the active and passive radar network system and related models using an interactive multiple model algorithm, and derive a target tracking performance measure based on the best fitting Gaussian according to the target state prediction result; A resource allocation model establishment module is used to establish an active and passive radar resource allocation model based on the target tracking performance measurement, with the goal of minimizing the system normalized resource consumption, and with the total resource being limited and the system preset tracking accuracy being constrained; The resource allocation model solving module is used to perform decoupling solution on the active and passive radar resource allocation model based on a decoupling solution algorithm to obtain active and passive radar resource allocation results.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a computer, the computer is enabled to execute the active and passive radar resource allocation method for maneuvering target tracking according to any one of claims 1 to 7.

10. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the active and passive radar resource allocation method for maneuvering target tracking as described in any one of claims 1 to 7 is implemented.

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