Target tracking-oriented aircraft cluster networking radar power distribution method

Abstract

The target tracking-oriented aircraft cluster networking radar power distribution method is suitable for tracking a target object, and comprises the following steps: S101, simulating a target scene of a tracking task; s102, determining a tracking performance index of the aircraft cluster to the target object; s103, establishing a power distribution mathematical model; and S104, solving a model exact solution of the power distribution optimization model based on an optimization power distribution algorithm of an iterative dichotomy to determine the power distribution degree, thereby improving the tracking efficiency of the target object.

Description

Technical Field

[0001] The invention belongs to the technical field of airborne radar data processing, and in particular relates to a radar power allocation method for aircraft cluster networking oriented to target tracking. Background Art

[0002] The flight simulation test mode is gradually changing from single-aircraft simulation to multi-aircraft collaborative simulation. Aircraft clusters often need to search, confirm or track multiple targets at the same time, and in environments with strong clutter, dense targets, close target distances or significant target RCS differences, how to choose the optimal resource scheduling method to effectively optimize the resource allocation inside the aircraft. In existing methods, due to the limited load and energy of the airborne radar platform, the total transmitter power of the multi-aircraft collaborative network radar will be limited when locating the target, reducing the tracking performance of the target. Summary of the invention

[0003] In view of this, the radar power allocation method for aircraft cluster networking for target tracking of the present invention can solve the technical problem of improving the tracking performance capability of the target object when the aircraft cooperate with each other.

[0004] A radar power allocation method for aircraft cluster networking for target tracking is suitable for tracking a target object, and the method includes:

[0005] S101: simulate the target scene of the tracking task;

[0006] S102: Determine the tracking performance index of the aircraft cluster on the target object;

[0007] S103: Establishing a power distribution mathematical model;

[0008] S104: Calculate the exact solution of the power allocation optimization model based on the optimized power allocation algorithm of iterative bisection method to determine the power allocation.

[0009] Beneficial Effects

[0010] Advantages / positive effects: The method proposed in the present invention can realize the reasonable allocation of network radar power resources, balance the relationship between resource consumption and tracking performance, and thus maximize the overall operation efficiency of the system. In order to minimize the requirements for the transmission power of the network radar when the collaborative target tracking performance of the aircraft cluster meets the requirements, it is proposed to realize the optimal power allocation strategy of the network radar through high-speed information transmission of the airborne data link. Allowing multi-machine collaborative network radars to realize the reasonable allocation of radar power resources according to different environmental conditions and tactical purposes not only improves the power utilization efficiency and balances the relationship between resource consumption and tracking performance, but also saves more power resources for tasks such as search and confirmation, significantly improving the overall work efficiency of the system, and its use is one of the core technologies for realizing the advantageous efficiency of aircraft cluster collaborative combat, and it has good scalability and adaptability, and can be widely used in multi-machine collaborative systems where the airborne radar power is limited or multiple tactical functions need to be realized simultaneously. It has broad application prospects and huge application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is the effect diagram of relative motion tracking between the aircraft cluster and the target;

[0013] Figure 2 This is a comparison chart of power allocation before and after optimization of networked radar nodes in the cluster;

[0014] Figure 3 It is a comparison chart of target tracking effects under different power allocation strategies. DETAILED DESCRIPTION

[0015] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0016] 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 a 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.

[0017] 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 can 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 can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can 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 can be used to implement this device and / or practice this method.

[0018] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure, and only show the components related to the present disclosure rather than being drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout type may also be more complicated.

[0019] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, one skilled in the art will appreciate that aspects may be practiced without these specific details.

[0020] like Figures 1 to 3 The target tracking-oriented aircraft cluster network radar power allocation method shown is suitable for tracking a target object, and the method includes:

[0021] S101: simulate the target scene of the tracking task;

[0022] S102: Determine the tracking performance index of the aircraft cluster on the target object;

[0023] S103: Establishing a power distribution mathematical model;

[0024] S104: Calculate the exact solution of the power allocation optimization model based on the optimized power allocation algorithm of iterative bisection method to determine the power allocation.

[0025] As a specific implementation method provided in this case, all carriers can communicate data with each other, and S101 includes:

[0026] Get the number of aircraft in the aircraft cluster M and the status of all aircraft Starting position of the target Among them, x n is the x-axis position of the carrier in the plane coordinate system, is the speed of the aircraft on the x-axis in the plane coordinate system, y n is the y-axis position of the carrier in the plane coordinate system, is the velocity of the carrier on the y-axis in the plane coordinate system.

[0027] As a specific implementation method provided in this case, by calculating in a recursive form, a lower bound can be provided for the target state estimation error at the next moment, which is used as an evaluation index for the maneuvering target tracking error. S102 includes:

[0028] 201: According to the starting position of the target object and the states of all carriers, the state of the target object ξ is obtained by using the “posterior Cramer-Rao bound” method k Mean square error of the estimate The MSE lower bound J -1 (ξ k ),

[0029] It is expressed as:

[0030]

[0031] In the formula, The state of the target object ξ k The estimated value of Represents the state of the target object ξ k and measure Z k The expected value from the start time to time K, C PCRLB (ξ k ) is the posterior Cramer-Rao bound PCRLB matrix, and is the inverse matrix of the Fisher information matrix FIM, is the estimated value of the target state obtained from K measurements from time 1 to time K, Z 1:k is the measurement data of the target object collected by each radar node in the aircraft cluster from the first moment, and Z 1:k ={z 1，1:k , z 2，1:k ..., z M，1:k};

[0032] S202: Under normal circumstances, when the cluster illuminates the target at time K, the radar system has collected all the measurement data from the start time to time K-1. Based on the above data, a more accurate lower bound of the MSE of the target state estimate can be calculated in real time. When the aircraft cooperates to track the target, based on the measurement Z at time K-1, k-1 , calculate the posterior Cramer-Rao bound J -1 (ξ k |Z k-1 ), defined as follows:

[0033]

[0034] The inverse matrix of FIM is J -1 (ξ k |Z k-1 ), expressed as:

[0035] In the formula, represents logp(ξ k , Z k |Z k-1 ) for the target state ξ k The second-order partial derivative of k , Z k |Z k-1 ) is the joint probability density function;

[0036] FIM is expressed as J(ξ k |Z k-1 ) and satisfy:

[0037] J(ξ k |Z k-1 )=J P (ξ k |Z k-1 )+J D (ξ k |Z k-1 ), where J P (ξ k |Z k-1 ), J D (ξ k |Z k-1 ) are the prior information and data information of FIM respectively;

[0038] but In the formula, p(ξ k |Z k-1 ) The prior probability density function of the prediction, usually J P (ξ k |Z k-1) is difficult to obtain an analytical solution, so in the case of Gaussian distribution, the predicted covariance matrix can be obtained through the filter to approximate the prior information, that is, the predicted information matrix J P (ξ k |Z k-1 ), expand it to get:

[0039] In the formula, the state of the target object ξ k Estimated value of

[0040] The data information matrix J D (ξ k |z m，k-1 ) is expressed as:

[0041]

[0042] In the formula, z m，k is the measurement of the mth aircraft at the kth moment, is the covariance matrix of the mth aircraft at the kth time, and As g m，k (ξ k ), G m，k Represented as g m，k (ξ k ) and transpose, satisfying:

[0043] S203: Step S202 calculates the data information matrix of each aircraft. Since the signals received by different radar nodes in the aircraft cluster are independent of each other, the data information matrix J of all aircraft is D (ξ k |Z k-1 ) is expressed as:

[0044]

[0045] According to the Kalman filter model, we can get:

[0046] In the formula, ξ k is the state of the target object at time k, is the prediction information of the target object, is the state estimation value of the target object at time k-1, f(k-1) is the state transfer matrix, σ 滤波噪声 To filter noise;

[0047] In the scenario of aircraft cluster tracking targets, compared with ground fixed network radar, due to the uneliminable aircraft control algorithm jitter error σ 飞控 There is a delay σ in the instantaneous communication with the aircraft cluster in the air通信 , and according to the empirical formula: σ 飞控 +σ 通信 >>σ 滤波噪声 ;

[0048] Ignoring σ 滤波噪声 After the influence of Instead of E(ξ k|k-1 ), J D (ξ k |z m，k-1 ) is simplified to:

[0049] In the formula, Represents the prediction information of the target object;

[0050] It can be obtained that the data information matrix J of all aircraft D (ξ k |Z k-1 ) is expressed as:

[0051]

[0052] As a specific implementation method provided in this case, S103 includes:

[0053] S301: The networked radar power allocation problem is transformed into a power allocation problem under the condition of satisfying the expected optimal tracking performance. The posterior Cramer-Rao bound PCRLB is used as a tracking error measurement indicator. The networked radar system of the aircraft cluster predicts the posterior Cramer-Rao bound PCRLB at the current time k based on the target state and power allocation at time k-1. The purpose is to enable the networked radar system to make decisions in advance based on the prediction results and convert F(P k ) is used as the standard measurement indicator of target tracking error and is expressed as:

[0054] In the formula, tr(C PCRLB ()) is the trace of the posterior Cramer-Rao bound PCRLB matrix, P k is the vector of the transmit power of each radar, F(P k ) reflects the overall tracking performance of the networked radar at time K, P k =[P 1，k , P 2，k , ..., P N，k ] represents the radar power vector, N is the total number of radars;

[0055] S302: Establishing a power allocation optimization model to measure the target tracking error standard index F(P k) is optimized. The transmission power of all radars in the aircraft cluster is constant. The transmission power of each radar at different times is optimized to improve the tracking performance of the distributed network radar system, that is, to minimize the target tracking error.

[0056] The optimization objective of the power allocation optimization model is min F(P k ), the constraints are:

[0057] P min ≤P m，k ≤P max , m = 1, 2, ..., M and In the formula, The two constraints represent: 1) the radar radiation power has minimum and maximum power constraints; 2) for target tracking tasks, the power occupied by the networked radar cluster system must not exceed the total power.

[0058] As a specific implementation method provided in this case, S104 includes:

[0059] The power allocation optimization algorithm based on iterative dichotomy is used to find the exact solution of the power allocation optimization model to determine the power allocation solution, wherein:

[0060] S401: define the initialization iteration number j=0, initialize power allocation Step size λ k，j ∈[λ min ,λ max ],initialization Convergence condition ε, parameter α k，j Take [α min , α max ]Random value;

[0061] S402: Calculate the objective function minF(P k，j )For P k，j The gradient of

[0062] S403: Calculate spectral gradient Pr() represents the orthogonal projection operator;

[0063] S404: Calculate P k，a =P k，j +λ min d k，j , P k，b =P k，j +λ max d k，j , P k，c =λ k，j +λ max d k，j ,

[0064] If: P k，a ≤P k，b ≤P k，c , then update

[0065] If: P k,c ≤P k，b ≤P k,a , then update

[0066] Otherwise, λ k，j constant;

[0067] S405: Update parameter α k，j is [α min , α max ] any random value;

[0068] S406: Update power vector P k，j+1 =P k，j +λ k，j d k，j , and judge, if |d k，j |<ε, terminate the iteration and output the optimal power Otherwise, j=j+1, go to step S402.

[0069] Furthermore, a heuristic solution algorithm is used to obtain a better feasible solution, where:

[0070] S501: define the initialization iteration number j=0, and initialize the maximum iteration number D;

[0071] S502: Ignore constraint P min ≤P m，k ≤P max，m =1,2,...,M and Get the original problem minF(P k ) is a relaxation problem min F j (P k );

[0072] S503: Solve the problem min F j (P k ) to get P k，j ;

[0073] S504: If P k，j Satisfy the constraint P min ≤P m，k ≤P max , m = 1, 2, ..., M and The iteration ends and the feasible solution P is output. k，j , otherwise, execute step S505;

[0074] S505: P k，j The constraint P is satisfied min ≤P m，k ≤P max , m=1, 2, ..., the decision variables of M are set to fixed values, and min F is substituted j (P k ), update it to min F j+1 (P k );

[0075] S506: If j≤D, set j=j+1 and execute step S503; otherwise, execute step S507;

[0076] S507: For all P of m=1, 2, ..., M m，k Make a judgment, if P m，k >P max , let P m，k =P max , P m，k Put into set I;

[0077] If P m，k <P min , let P m，k =P min , P m，k Put it into set J, if P min ≤P m，k ≤P max , P m，k Put it into set I and calculate Let all P in set I m，k = k·P m，k , output "the feasible solution of each radar transmit power vector under the jth algorithm iteration" P k，j .

[0078] The invention is described in detail below through the construction of a simulation scenario.

[0079] Set up a simulation scenario consisting of a cluster of four aircraft and the target to be tracked as follows Figure 1 As shown in the figure. The monitoring cycle of the four aircraft is 30 frames, the observation time interval is 2s, and the main parameters of the radar system are: radar node effective time width 1ms, effective bandwidth 1MHz, and total system power 1000Kw. The power constraints of the single radar acting on the tracking task are P min =0.005P total / 4 and P max =0.5P total / 4. The initial coordinates and positions of each aircraft and radar are shown in Table 1:

[0080] Table 1 Initial coordinates and speeds of the aircraft and target

[0081]

[0082]

[0083] According to the above scenario settings, the simulation results are as follows Figure 1 As shown in the figure, it mainly reflects the relative motion between the target and the aircraft cluster. For the maneuvering target flying at a constant speed relative to the cluster, the root mean square error RMSE of the networked radar target tracking error is basically lower than 0.45km, maintaining a stable tracking effect on the target throughout the process, which meets the engineering requirements.

[0084] Figure 2 The power resource allocation of a certain aircraft radar before and after optimization during target tracking in a networked radar system is shown. It can be seen that the power allocation after optimization is lower than that before optimization in most cases, and the allocation is more even. The relative distance and speed between the maneuvering target and the radar vary in different directions, which will affect the power resource allocation results. The networked radar system will optimize the power allocation strategy between the radars based on these factors to maximize the target tracking accuracy while meeting the constraints.

[0085] Figure 3 It shows the comparison of target tracking accuracy under different methods. When both PCRLB and RMSE are used as target tracking performance measurement indicators, the RMSE of the target tracking error of the average power allocation strategy is 0.56km on average, and PCRLB is 0.51km on average. The RMSE of the target tracking error of the optimized power allocation strategy is 0.45km on average, and PCRLB is 0.38km on average. It is not difficult to see that the power optimization allocation algorithm in this paper improves the target tracking performance by about 25.5% compared with the uniform power allocation algorithm. The results show that the power allocation method can achieve the requirements of higher precision and stable target tracking under power constraints.

[0086] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A radar power allocation method for aircraft cluster networking for target tracking, suitable for tracking a target, characterized in that: The methods include: S101: simulate the target scene of the tracking task; S102: Determine the tracking performance index of the aircraft cluster on the target object; S103: Establishing a power distribution mathematical model; S104: Calculate the exact solution of the power allocation optimization model based on the optimized power allocation algorithm of iterative bisection to determine the power allocation.

2. The method for allocating radar power for aircraft cluster networking according to claim 1, characterized in that: All carriers can communicate with each other, S101 includes: Get the number of aircraft in the aircraft cluster M and the status of all aircraft Starting position of the target Among them, x n is the x-axis position of the carrier in the plane coordinate system, is the speed of the aircraft on the x-axis in the plane coordinate system, y n is the y-axis position of the carrier in the plane coordinate system, is the velocity of the carrier on the y-axis in the plane coordinate system.

3. The method for allocating radar power for aircraft cluster networking according to claim 2, characterized in that: S102 includes: 201: According to the starting position of the target object and the states of all carriers, the state of the target object ξ is obtained by using the posterior Cramer-Rao bound method k Mean square error of the estimate The MSE lower bound J -1 (ξ k ), It is expressed as: In the formula, is the estimated value of the target state ξk, Represents the state of the target object ξ k and measure Z k The expected value from the start time to time K, C PCRLB (ξ k ) is the posterior Cramer-Rao bound PCRLB matrix, and is the inverse matrix of the Fisher information matrix FIM, is the estimated value of the target state obtained from K measurements from time 1 to time K, Z 1：k is the measurement data of the target object collected by each radar node in the aircraft cluster from the first moment, and Z 1：k ={z 1，1：k , z 2，1：k ..., z M，1：k }; S202: When the aircraft cooperates to track the target, based on the measurement Z at time K-1 k-1 , calculate the posterior Cramer-Rao bound J -1 (ξ k |Z k-1 ), defined as follows: The inverse matrix of FIM is J -1 (ξ k |Z k-1 ), expressed as: In the formula, represents log p(ξ k , Z k |Z k-1 ) for the target state ξ k The second-order partial derivative of k , Z k |Z k-1 ) is the joint probability density function; FIM is expressed as J(ξ k |Z k -1) and satisfy: J(ξ k |Z k-1 )=J P (ξ k |Z k-1 )+J D (ξ k |Z k-1 ), where J P (ξ k |Z k-1 ), J D (ξ k |Z k-1 ) are the prior information and data information of FIM respectively; but In the formula, p(ξ k |Z k-1 ) The predicted prior probability density function; In the case of Gaussian distribution, the predicted covariance matrix is ​​obtained through the filter to obtain prior information, which can be expanded to obtain: In the formula, the target state ξ k Estimated value of The data information matrix J D (ξ k |z m，k-1 ) is expressed as: In the formula, Z m,k is the measurement of the mth aircraft at the kth moment, is the covariance matrix of the mth aircraft at the kth time, and As g m，k (ξ k ), G m,k Represented as g m，k (ξ k ) and transpose, satisfying: S203: Step S202 calculates the data information matrix of each aircraft. Since the signals received by different radar nodes in the aircraft cluster are independent of each other, the data information matrix J of all aircraft is D (ξ k |Z k-1 ) is expressed as: According to the Kalman filter model, we can get: In the formula, ξ k is the state of the target object at time k, is the prediction information of the target object, is the state estimation value of the target object at time k-1, f(k-1) is the state transfer matrix, σ 滤波噪声 To filter noise; In the scenario of aircraft cluster tracking targets, compared with ground fixed network radar, due to the uneliminable aircraft control algorithm jitter error σ 飞控 There is a delay σ in the instantaneous communication with the aircraft cluster in the air 通信 , and satisfy: σ 飞控 +σ 通信 >> 滤波噪声 ; Ignoring σ 滤波噪声 After the influence of Instead of E(ξ k|k-1 ), J D (ξ k |z m，k-1 ) is simplified to: In the formula, Represents the prediction information of the target object; It can be obtained that the data information matrix J of all aircraft D (ξ k |Z k-1 ) is expressed as:

4. The method for allocating radar power for aircraft cluster networking according to claim 3, characterized in that: S103 includes: S301: The power allocation problem of the networked radar is transformed into a power allocation problem under the condition of satisfying the expected optimal tracking performance. The posterior Cramer-Rao bound PCRLB is used as a tracking error measurement indicator. The networked radar system of the aircraft cluster predicts the posterior Cramer-Rao bound PCRLB at the current time k based on the target state and power allocation at time k-1, and converts F(P k ) is used as the standard measurement indicator of target tracking error and is expressed as: In the formula, tr(C PCRLB ( )) is the trace of the posterior Cramer-Rao bound PCRLB matrix, P k is the vector of the transmit power of each radar, F(P k ) reflects the overall tracking performance of the networked radar at time K, P k =[P 1，k , P 2，k , ..., P N，k ] represents the radar power vector, N is the total number of radars; S302: Establishing a power allocation optimization model to measure the target tracking error standard index F(P k ) is optimized, where The optimization objective of the power allocation optimization model is min F(P k ), the constraints are: P min ≤P m.k ≤P max , m = 1, 2, ..., M and In the formula, 5. The method for allocating radar power for aircraft cluster networking according to claim 4, characterized in that: S104 includes: The power allocation optimization algorithm based on iterative bisection method is used to find the exact solution of the power allocation optimization model to determine the power allocation solution, wherein: S401: define the initialization iteration number j=0, initialize power allocation Step size λ k，j ∈[λ min ,λ max ],initialization Convergence condition ε, parameter α k，j Take [α min , α max ] Random value: S402: Calculate the objective function minF(P k，j )For P k，j The gradient of S403: Calculate spectral gradient Pr() represents the orthogonal projection operator; S404:calculationP k，a =P k，j +λ min d k，j ,P k，b =P k，j +λ max d k，j ,P k，c =λ k，j +λ max d k，j , If: P k，a ≤P k，b ≤P k，c , then update If: P k，c ≤P k，b ≤P k，a , then update Otherwise, λ k，j constant; s405: Update parameter α k，j is [α min , α max ] any random value; s406: Update power vector P k，j+1 =P k，j +λ k，j d k，j , and judge, if |d k，j |<ε, terminate the iteration and output the optimal power Otherwise, j=j+1, go to step S402.

6. The method for allocating radar power for aircraft cluster networking according to claim 5, characterized in that: S104 includes using a heuristic solution algorithm to obtain a better feasible solution, wherein: S501: define the initialization iteration number j=0, and initialize the maximum iteration number D; S502: Ignore constraint P min ≤P m，k ≤P max , m = 1, 2, ..., M and Get the original problem min F(P k ) is a relaxation problem min F j (P k ); S503: Solve the problem min F j (P k ) to get P k，j ; S504: If P k，j Satisfy the constraint P min ≤P m，k ≤P max , m = 1, 2, ..., M and The iteration ends and the feasible solution P is output. k，j , otherwise, execute step S505; S505: P k，j The constraint P is satisfied min ≤P m，k ≤P max , m=1, 2, ..., the decision variables of M are set to fixed values, and min F is substituted j (P k ), update it to min F j+1 (P k ); S506: If j≤D, set j=j+1 and execute step S503; otherwise, execute step S507; S507: For all Ps of m=1, 2, ..., M m,k Make a judgment, if P m，k >P max , let P m，k =P max , P m，k Put into set I; If P m，k <P min , let P m，k =P min , P m，k Put it into set J, if P min ≤P m，k ≤P max , P m，k Put it into set I and calculate Let all P in set I m，k = k·P m，k , output feasible solution P k，j , P k，j It is represented as the feasible solution of each radar transmit power vector under the jth algorithm iteration.

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