A phased array radar large-scale cluster target tracking beam power joint allocation method

Abstract

The application discloses a phased array radar large-scale cluster target tracking beam power joint distribution method, comprising the following steps: constructing a target state space model, a measurement model and a radar tracking optimization criterion target function based on a state vector of a target; determining a phased tracking error curve according to a total set of targets obtained through detection; regarding a target whose tracking precision is greater than a preset tracking precision as a first target set; determining a second target set and a first distribution power in the first target set; selecting a target in the second target set to perform power distribution or tracking each target in the second target set according to the first distribution power, and performing power distribution on the targets in the first target set except the targets in the second target set. In the case that phased array resources are insufficient, the application guarantees tracking more targets to a specified precision as fast as possible through twice distribution.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to a method for joint allocation of beam power for large-scale cluster target tracking in phased array radar. Background Technology

[0002] Phased array radars, with their flexible beam pointing and agility, can rationally allocate time and energy resources for different tasks such as airspace search and target tracking. Moreover, their operating parameters are controllable, thus playing an important role in multi-target tracking.

[0003] Many researchers have proposed technical solutions for resource allocation algorithms when phased array radar resources are insufficient. Wang Chengwei et al., in their paper "Multi-target Radar Network Sensor Resource Management Algorithm [J]. Firepower and Command Control, 2016, 41(05):89-92," used the number of tracked targets and tracking accuracy as optimization objectives. First, sensors were allocated according to a set allocation criterion to maximize the number of tracked targets. Then, a heuristic sensor allocation method based on sensor ranking was used for secondary allocation. By controlling the covariance level of the tracked targets, the tracking accuracy was made as close as possible to the desired value. This algorithm can quickly allocate multiple sensors in a short time, tracking more targets while achieving the desired tracking accuracy, and controlling resource consumption to a certain extent, reducing the total energy consumption of the system. However, the information gain of each radar tracking the target in this method is calculated offline, which does not conform to the real-world scenario. Furthermore, the final simulation scenario only had 4 targets and 10 radars, failing to reflect the situation where too many targets lead to insufficient radar resources. Insufficient phased array radar resources can cause traditional resource allocation algorithms to fail, making it impossible to track targets to the required accuracy within a specified time. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a method for joint allocation of beam power for large-scale cluster target tracking in phased array radar.

[0005] A method for joint allocation of beam power for large-scale cluster target tracking in phased array radar includes the following steps:

[0006] Construct a target state space model, a measurement model, and a radar tracking optimization criterion objective function based on the target's state vector;

[0007] A phased tracking error curve is determined based on multiple selected targets in the overall set of detected targets; the phased tracking error curve is a curve with a preset tracking accuracy within a preset time period.

[0008] Targets whose tracking accuracy is greater than the preset tracking accuracy are defined as the first target set;

[0009] Based on the radar tracking optimization criterion objective function and the stage tracking error curve, determine the second target set and the first power allocation in the first target set;

[0010] When the number of targets in the second target set is greater than the number of phased array beams, targets of the number of phased array beams in the second target set are selected for power allocation, and a second allocated power is obtained.

[0011] When the number of targets in the second target set is less than the number of phased array beams, each target in the second target set is tracked according to the first power allocation, and power is allocated to targets in the first target set other than those in the second target set.

[0012] In one embodiment of the present invention, the construction of the target state space model, measurement model, and radar tracking optimization criterion objective function based on the target's state vector includes:

[0013] The target motion state equation is constructed based on the target's state vector; wherein, the target motion state equation and the covariance matrix of the target motion state equation constitute the target state space model;

[0014] A measurement model is constructed based on the observation function and measurement error of the target motion state equation;

[0015] The radar tracking optimization criterion objective function is constructed based on the measurement model, the predicted covariance matrix of the target motion state equation, and the radar's transmitted signal power.

[0016] In one embodiment of the present invention, determining the stage tracking error curve based on multiple screened targets in the detected target set includes:

[0017] From the total set of detected targets, targets at a preset distance from the radar are selected as the filtered targets;

[0018] The simulation tracks the selected targets based on a preset radar power, and determines the phased tracking error curve.

[0019] In one embodiment of the present invention, the method further includes:

[0020] Determine whether the tracking accuracy of the targets in the total target set is less than the preset tracking accuracy;

[0021] Targets with tracking accuracy less than the preset tracking accuracy are transferred to the next task node.

[0022] In one embodiment of the present invention, determining a second target set and a first power allocation in the first target set based on the radar tracking optimization criterion objective function and the staged tracking error curve includes:

[0023] A first power allocation mathematical model is constructed based on the radar tracking optimization criterion objective function and the stage tracking error curve;

[0024] Solving the first power allocation mathematical model yields the second target set and the first allocated power.

[0025] In one embodiment of the present invention, the specific steps for selecting targets in the second target set that are greater than the number of phased array beams, performing power allocation, and obtaining the second allocated power when the number of targets in the second target set is greater than the number of phased array beams are as follows:

[0026] When the number of targets in the second target set is greater than the number of phased array beams, the first number of targets with the largest product of threat level and tracking accuracy at the previous moment are selected from the second target set for power allocation, and a second allocated power is obtained; wherein the first number is the number of phased array beams.

[0027] In one embodiment of the present invention, when the number of targets in the second target set is less than the number of phased array beams, tracking each target in the second target set according to the first allocated power, and allocating power to targets in the first target set other than those in the second target set, includes:

[0028] When the number of targets in the second target set is less than the number of phased array beams, each target in the second target set is tracked according to the first allocated power;

[0029] When the number of targets in the first target set other than those in the second target set is greater than the difference between the number of phased array beams and the number of targets in the second target set, the second largest number of targets with the highest threat level are selected from the targets in the first target set other than those in the second target set, and power is allocated according to the remaining power; wherein, the second number is the difference between the number of phased array beams and the number of targets in the second target set; and the remaining power is the difference between the total power and the first allocated power;

[0030] When the number of targets in the first target set other than those in the second target set is less than the difference between the number of phased array beams and the number of targets in the second target set, the targets in the first target set other than those in the second target set are allocated power according to the remaining power.

[0031] In one embodiment of the present invention, the state vector of the target at time k is:

[0032] Wherein, the target moves at a constant linear velocity, q represents the target, (x q,k ,y q,k () represents the position of the target q at time k. This represents the velocity of the target q at time k;

[0033] The expression for the target's motion state equation is:

[0034] x q,k =Fx q,k-1 +μ q,k-1 ;

[0035] in, Here is the state transition matrix. I2 denotes the Kronecker product; I2 denotes the 2nd order identity matrix; μ q,k-1 ΔT represents Gaussian white noise with zero mean, and ΔT represents the tracking time interval.

[0036] The covariance matrix Q of the target motion state equation q,k-1 for:

[0037]

[0038] Where, r q Indicates the intensity of process noise.

[0039] In one embodiment of the present invention, the expression of the measurement model is:

[0040] z q,k =h q,k (x q,k )+w q,k ;

[0041] Among them, h q,k (·) represents the observation function, h q,k (x q,k )=[r q,k θ q,k f q,k ] T , r q,k θ q,k and f q,k These represent distance, azimuth, and Doppler measurements, respectively. q,k This represents the measurement error, which follows a zero-mean Gaussian distribution.

[0042] The measurement covariance matrix R of the measurement modelq,k for:

[0043] in, and These represent the estimated variances of the distance, azimuth, and elevation measurements, respectively. α q,k ∝1 / (r q,k ) 4 Indicates path fading, |h q,k | 2 For the target RCS, B k E k and T k These represent the effective bandwidth of the transmitted signal, the received beamwidth, and the time width, respectively.

[0044] In one embodiment of the present invention, the radar tracking optimization criterion objective function is:

[0045]

[0046] Where Tr[·] represents the trace operation of a matrix. The posterior Cramero lower bound of the target tracking error covariance matrix is ​​represented by P, which is determined based on the prediction covariance matrix and the measurement covariance matrix of the measurement model. q,k The target tracking error covariance matrix represents the radar's transmitted signal power, and is obtained by tracking the target using the measurement model.

[0047] The beneficial effects of this invention are:

[0048] This invention optimizes the number of targets tracked and the tracking accuracy, while using the phased array radar beam and power as optimization variables. Under conditions of limited system resources, it aims to track as many targets as possible and as quickly as possible to a specified accuracy, while maintaining the ability to search and detect in a specific airspace. This allows for application in cooperative detection scenarios. By allocating resources twice, this invention ensures that as many targets as possible are tracked to a specified accuracy as quickly as possible, even when phased array resources are insufficient.

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for joint allocation of beam power for large-scale cluster target tracking in a phased array radar, as provided in an embodiment of the present invention.

[0051] Figure 2 The target motion trajectory map and radar spatial distribution map provided in the embodiments of the present invention;

[0052] Figure 3 The stage tracking error curve simulated by selecting target 3 and setting the power to 1 / 6 of the total radar power is provided in the embodiment of the present invention;

[0053] Figure 4 The results of 50 Monte Carlo simulations were performed to illustrate a method for joint allocation of beam power for large-scale cluster target tracking in a phased array radar according to an embodiment of the present invention.

[0054] Figures 5a-5l The method for joint allocation of beam power for large-scale cluster target tracking in phased array radar provided in this embodiment of the invention performed 50 Monte Carlo simulations to obtain the root mean square error of tracking accuracy for 12 targets. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0056] like Figure 1 As shown, a method for joint allocation of beam power for large-scale cluster target tracking in phased array radar includes the following steps:

[0057] Step 10: Construct the target state-space model, measurement model, and radar tracking optimization criterion objective function based on the target's state vector. The target in this step refers to any target. The specific steps of step 10 include steps 11-13:

[0058] Step 11: Construct the target motion state equation based on the target's state vector; wherein, the target motion state equation and the covariance matrix of the target motion state equation constitute the target state space model.

[0059] Specifically, assuming the target moves at a constant speed, the target's state vector at time k is... Where q represents any target, (x q,k ,y q,k () represents the position of target q at time k. This represents the velocity of target q at time k;

[0060] The expression for the target's state equation of motion is:

[0061] x q,k =Fx q,k-1 +μ q,k-1 ;

[0062] in, Here is the state transition matrix. I2 denotes the Kronecker product; I2 denotes the 2nd order identity matrix; μ q,k-1 ΔT represents Gaussian white noise with zero mean, and ΔT represents the tracking time interval.

[0063] If a target is assumed to be moving at a constant velocity in a straight line, then the covariance matrix Q of the target's state equation is... q,k-1 for:

[0064]

[0065] Where, r q Indicates the intensity of process noise.

[0066] Step 12: Construct a measurement model based on the observation function and measurement error of the target motion state equation.

[0067] Specifically, assuming that the radar tracks the target q and generates a measurement model z q,k The expression for the measurement model is:

[0068] z q,k =h q,k (x q,k )+w q,k ;

[0069] Among them, h q,k (·) represents the observation function, h q,k (x q,k )=[r q,k θ q,k f q,k ] T , r q,k θ q,k and f q,k These represent distance, azimuth, and Doppler measurements, respectively. q,k This represents the measurement error, which follows a zero-mean Gaussian distribution.

[0070] The measurement covariance matrix R of the measurement model q,k for:

[0071] in, and These represent the estimated variances of the distance, azimuth, and elevation measurements, respectively. α q,k ∝1 / (r q,k ) 4 Indicates path fading, |h q,k | 2 For the target RCS, B k E k and T k These represent the effective bandwidth, received beamwidth, and time width of the transmitted signal, respectively. The measurement covariance matrix R of the measurement model... q,k It can be seen that the higher the power, the higher the measurement noise w.q,k The smaller.

[0072] Step 13: Construct the radar tracking optimization criterion objective function based on the measurement model, the predicted covariance matrix of the target motion state equation, and the radar's transmitted signal power.

[0073] Specifically, the target motion state is predicted and updated based on the motion equation. At time k, the predicted target motion state equation x for target q is... q,k|k-1 And the prediction covariance matrix of the predicted target motion state equation. for:

[0074] x q,k|k-1 =Fx q,k-1

[0075]

[0076] The posterior conditional Cramer-Rao lower bound (PC-CRLB) of the target tracking error covariance matrix can be expressed as:

[0077]

[0078] in, It is of dimension n z ×n x The observation function h q,k (x q,k The Jacobian matrix of the target tracking error is given by [the measurement model]. The target tracking error covariance matrix is ​​obtained by tracking the target using a measurement model.

[0079] From the formula and The measurement covariance matrix R can be obtained. q,k Inversely proportional to the power variable, It can be rewritten as: Among them, D q,k This represents the remaining parameter matrix.

[0080] Target tracking performance is defined as a weighted average of the PC-CRLB traces, which is also the objective function of the radar tracking optimization criterion, expressed as:

[0081]

[0082] Where Tr[·] represents the trace operation of a matrix.

[0083] Step 20: Determine the staged tracking error curve based on multiple selected targets in the overall target set obtained from the detection; the staged tracking error curve is the curve with a preset tracking accuracy within a preset time period. The overall target set includes multiple different targets q.

[0084] Specifically, from the total set of detected targets Ω, targets at a suitable distance from the radar are selected, and an appropriate power is chosen. A phased tracking error curve η is then fitted, in which the tracking accuracy reaches the preset tracking accuracy Hopt within a specified time. q (The generated curve can be modified appropriately to ensure that the tracking error at the last moment is Hopt), stage tracking error curve η q This means that if the target can be tracked to a specified accuracy Hopt at the last moment, then its tracking error at each time step needs to be less than the stage tracking accuracy η at time step k. q,k Step 20 includes steps 21-22:

[0085] Step 21: Select targets from the total set of detected targets that are at a preset distance from the radar as the screening targets.

[0086] Step 22: Simulate the tracking of the selected targets based on the preset radar power and determine the phased tracking error curve.

[0087] For example, such as Figure 2 and Figure 3 As shown, the target moves toward the radar, where the radar can track 3 targets simultaneously. Target 3 is selected, and the power is selected as 1 / 6 of the total power of the radar to simulate the staged tracking error curve.

[0088] Step 30: Determine whether the tracking accuracy of targets in the overall target set is less than the preset tracking accuracy; transfer targets with tracking accuracy less than the preset tracking accuracy to the next task node. Select targets with tracking accuracy greater than the preset tracking accuracy as the first target set.

[0089] In this step, it is determined whether the target tracking accuracy in the target set Ω is less than the preset tracking accuracy Hopt. If it is less than Hopt, the target is transferred to the subsequent task node, and the subsequent phased array radar will no longer track it. Targets with tracking accuracy greater than Hopt are stored in the first target set Ω1.

[0090] Step 40: Based on the radar tracking optimization criterion objective function and the stage tracking error curve, determine the second target set and the first power allocation from the first target set. In this step, target allocation is performed on the targets in the first target set Ω1, and the power allocation is used to find the stage tracking accuracy threshold η that can be satisfied at time k+1 to time k-1. q,k-1The second target set Ω2 and the power allocation result (first allocated power P1). The specific steps of step 40 include steps 41-42:

[0091] Step 41: Construct the first power allocation mathematical model based on the radar tracking optimization criterion objective function and the stage tracking error curve. The first power allocation mathematical model is as follows:

[0092]

[0093]

[0094] P min ≤P q,k ≤P max

[0095] P q,k ≥0

[0096] in, The target threat level is represented by Γ(m), which is an indicator function. When m ≥ 0, Γ(m) = 1; when m ≥ 0, Γ(m) = 1. Hopt is the final preset tracking accuracy, P min and P max These are the lower and upper limits of the system power, P. tot This represents the total power.

[0097] Step 42: Solve the mathematical model of the first power allocation to obtain the second target set Ω2 and the first allocated power P1.

[0098] Specifically, since the indicator function Γ(m) is a non-convex and non-smooth function, it is not conducive to solving the problem. Therefore, the activation function is used. Smooth it. The mathematical model for the first power allocation is now formed as follows:

[0099]

[0100]

[0101] P min ≤P q,k ≤P max

[0102] P q,k ≥0

[0103] Since the objective function monotonically increases with respect to power, the spectral gradient projection method is used to solve it, resulting in the second objective set Ω2 and the power allocation result P1.

[0104] Step 50: Determine whether the number of targets in the second target set is greater than the number of phased array beams.

[0105] Step 51: When the number of targets in the second target set is greater than the number of phased array beams, select targets in the second target set whose number of phased array beams is greater than the number of phased array beams for power allocation, and obtain the second allocated power. In this step, when the number of targets M in the second target set Ω2 is greater than the number of phased array beams L, select the top L targets in the second target set Ω2 with the largest product of threat level and tracking accuracy at the previous moment for power reallocation, and obtain the second allocated power.

[0106] Specifically, based on the second power allocation mathematical model, the power of the first L targets is redistributed. Solving the second power allocation mathematical model yields the power allocation result P2 (second allocated power). The expression for the second power allocation mathematical model is:

[0107]

[0108]

[0109]

[0110] Since solving the mathematical model of the second power allocation is a convex optimization problem, a convex optimization toolkit can be used to solve it.

[0111] The radar tracks each target in the second target set Ω2 according to the second allocated power P2.

[0112] Step 52: When the number of targets in the second target set is less than the number of phased array beams, track each target in the second target set according to the first power allocation, and allocate power to targets in the first target set other than those in the second target set. The specific steps of step 52 include steps 521-524:

[0113] Step 521: When the number of targets M in the second target set Ω2 is less than the number of phased array beams L, the radar tracks each target in the second target set Ω2 according to the first allocated power P1.

[0114] Step 522: Determine whether the remaining target number N in set Ω1-Ω2 is greater than LM.

[0115] Step 523: When the number N of targets in the first target set Ω1 excluding targets in the second target set Ω2 is greater than the difference between the number of phased array beams L and the number of targets M in the second target set Ω2, that is, when Ω1-Ω2 is N and N is greater than LM, select the top LM targets with the highest threat level from the N targets in the first target set Ω1 excluding targets in the second target set Ω2, and determine the targets based on the remaining power P. tot -P1 performs power distribution.

[0116] In this step, power is allocated to the first LM targets according to the third power allocation mathematical model. Solving the third power allocation mathematical model yields the power allocation result P3 (third allocated power). The expression for the third power allocation mathematical model is:

[0117]

[0118]

[0119] P min ≤P i,k ≤P max

[0120] P i,k ≥0

[0121] Since solving the mathematical model of the third power distribution is a convex optimization problem, a convex optimization toolkit can be used to solve it.

[0122] The radar tracks each of the LM targets in set Ω1-Ω2 according to the third power distribution P3.

[0123] Step 524: When the number N of targets in the first target set Ω1 excluding targets in the second target set Ω2 is less than the difference between the number of phased array beams L and the number of targets M in the second target set Ω2, that is, when Ω1-Ω2 is N and N is less than LM, the N targets in the first target set Ω1 excluding targets in the second target set Ω2 are allocated power according to the remaining power Ω1-Ω2.

[0124] In this step, power is allocated to N targets according to the third power allocation mathematical model mentioned above. Solving the third power allocation mathematical model yields the power allocation result P4 (fourth power allocation).

[0125] The radar tracks each target in the set Ω1-Ω2 according to the fourth power distribution P4, that is, it tracks N targets.

[0126] For example, such as Figure 4 As shown in Figure 5, the method of the present invention can track 7 targets to a specified accuracy within a specified time, and at the same time maintain stable tracking of an additional 4 targets.

[0127] This invention establishes a novel mathematical model that solves the resource allocation problem in two steps. The first step, target allocation, calculates the set of targets that meet the required tracking accuracy at the current moment, prioritizing the power requirements of targets that can be tracked to the specified accuracy at the last possible moment. The second step, power reallocation, ensures that targets can be tracked to the specified accuracy more quickly, and uses remaining power to allocate to targets that cannot meet the required tracking accuracy at the current moment, maximizing power utilization to ensure the tracking accuracy of as many targets as possible. This invention is applicable to scenarios where the radar does not continuously track targets after they have been tracked to the specified accuracy and handed over to subsequent task nodes, but the large number of targets leads to insufficient radar resources, while the system requires it to track as many targets as possible to the specified tracking accuracy within a specified time.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0129] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0130] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for jointly allocating beam power for tracking large-scale cluster targets by a phased array radar, characterized in that, The method comprises the following steps: constructing a target state space model, a measurement model and a radar tracking optimization criterion target function based on a target state vector; determining a phased tracking error curve according to a plurality of screening targets in a total set of targets detected; regarding a target whose tracking accuracy is greater than the preset tracking accuracy as a first target set; determining a second target set and a first allocated power in the first target set according to the radar tracking optimization criterion target function and the phased tracking error curve; when the number of targets in the second target set is greater than the number of phased array beams, selecting the number of targets in the second target set as the number of phased array beams for power allocation, and obtaining a second allocated power; when the number of targets in the second target set is less than the number of phased array beams, tracking each target in the second target set according to the first allocated power, and allocating power to the targets in the first target set other than the targets in the second target set.

2. The method of claim 1, wherein, The method of constructing a target state space model, a measurement model and a radar tracking optimization criterion target function based on a target state vector comprises: constructing a target motion state equation based on a target state vector; wherein the target motion state equation and a covariance matrix of the target motion state equation constitute the target state space model; constructing a measurement model according to an observation function of the target motion state equation and a measurement error; constructing the radar tracking optimization criterion target function according to the measurement model, a predicted covariance matrix of a predicted target motion state equation of the target motion state equation and a transmitting signal power of a radar.

3. The method of claim 1, wherein, The method of determining a phased tracking error curve according to a plurality of screening targets in a total set of targets detected comprises: selecting, in the total set of targets detected, a target with a preset distance from a radar as the screening target; simulating tracking of the screening target according to a preset radar power to determine the phased tracking error curve.

4. The method of claim 1, wherein, The method further comprises: judging whether the tracking accuracy of the target in the total set of targets is less than the preset tracking accuracy; handing over the target with tracking accuracy less than the preset tracking accuracy to a next task node.

5. The method of claim 3, wherein, The method of determining a second target set and a first allocated power in the first target set according to the radar tracking optimization criterion target function and the phased tracking error curve comprises: constructing a first power allocation mathematical model according to the radar tracking optimization criterion target function and the phased tracking error curve; solving the first power allocation mathematical model to obtain a second target set and a first allocated power.

6. The method of claim 1, wherein, The specific steps of selecting the number of targets in the second target set as the number of phased array beams for power allocation when the number of targets in the second target set is greater than the number of phased array beams, and obtaining a second allocated power, are as follows: When the number of targets in the second target set is greater than the number of phased array beams, the first number of targets with the maximum product of threat degree and tracking accuracy at the previous time in the second target set are selected to perform power allocation, and the second allocated power is obtained; wherein the first number is the number of phased array beams.

7. The method of claim 1, wherein, When the number of targets in the second target set is less than the number of phased array beams, each target in the second target set is tracked according to the first allocated power, and the targets in the first target set other than the targets in the second target set are allocated power. When the number of targets in the second target set is less than the number of phased array beams, each target in the second target set is tracked according to the first allocated power. When the number of targets in the first target set other than the targets in the second target set is greater than the difference between the number of phased array beams and the number of targets in the second target set, the second number of targets with the maximum threat degree in the targets in the first target set other than the targets in the second target set are selected, and power is allocated according to the remaining power; wherein the second number is the difference between the number of phased array beams and the number of targets in the second target set; the remaining power is the difference between the total power and the first allocated power. When the number of targets in the first target set other than the targets in the second target set is less than the difference between the number of phased array beams and the number of targets in the second target set, the targets in the first target set other than the targets in the second target set are allocated power according to the remaining power.

8. The method of claim 2, wherein, The state vector of the target at the kth moment is wherein the target uniform linear motion, q represents the target, (x q,k ,y q,k ) represents the position of the target q at time k, represents the velocity of the target q at time k; The expression of the target motion state equation is: x q,k = Fx q,k-1 + μ q,k-1 ; wherein is the state transition matrix, denotes the Kronecker product; I2denotes the 2x2 identity matrix; μ q,k-1 denotes a Gaussian process white noise with zero mean, ΔTdenotes the tracking time interval; The covariance matrix Q of the target motion state equation q,k-1 is: where r q represents the process noise intensity.

9. The method of claim 2, wherein, The expression of the measurement model is: z q,k = h q,k (x q,k ) + w q,k ; where h q,k (·) denotes the observation function, h q,k (x q,k ) = [r q,k θ q,k f q,k ] T , r q,k , θ q,k and f q,k denote the range, azimuth and Doppler measurement, respectively, w q,k denotes the measurement error, which is assumed to be zero-mean Gaussian distributed; a measurement covariance matrix R of the metrology model q,k is: where and denote the estimated variance of the range, azimuth and elevation measurements, respectively, α q,k ∝ 1 / (r q,k ) 4 denotes the path loss, |h q,k | 2 is the target RCS, B k , E k and T k denote the effective bandwidth of the transmitted signal, the receive beamwidth and the time duration, respectively.

10. The method of claim 2, wherein, The radar tracking optimization criterion target function is: wherein Tr[·] denotes the matrix trace operation, denotes a posteriori CRLB of the target tracking error covariance matrix, the posteriori CRLB of the target tracking error covariance matrix being determined according to the prediction covariance matrix and a measurement covariance matrix of the measurement model; P q,k denotes a transmit signal power of the radar, the target tracking error covariance matrix being obtained by tracking the target through the measurement model.

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