CN115052295A - Real-time resource management method for phased array radar networking system - Google Patents
Real-time resource management method for phased array radar networking system Download PDFInfo
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Abstract
The invention belongs to the field of radar resource management, and provides a real-time resource management method for a phased array radar networking system. According to the method, firstly, the target with resources to be preferentially allocated is selected according to the deviation between the predicted tracking precision value obtained by calculation under the condition that no radar resource is allocated to each target and the expected tracking precision of each target, the closest radar is selected to form a group of matching relations according to the distance between the target and each radar, and the optimal radiation energy of the radar is calculated by adopting a one-dimensional searching method. And updating the tracking precision of the target, and repeating the process until all the targets in the target set have corresponding radars for tracking or all the radars in the radar set are used for tracking the target. The invention can realize real-time phased array radar networking system resource management, adaptively change the matching relation between the target and the radar according to different scenes, and select different energies according to different tracking precision requirements, thereby achieving the purpose of saving system resources.
Description
Technical Field
The invention belongs to the field of radar resource management, and provides a real-time resource management method for a phased array radar networking system.
Background
The phased array radar with the beam agility is the military radar which is most widely applied at present, controls the conversion of the beam direction by depending on the phase difference between different array elements, has the characteristics of rapid response, rapid data transmission and strong anti-interference capability, can provide various working modes, and is widely applied to the field of target tracking. In order to cope with increasingly complex battle scenes, a concept of radar networking is provided, and a networking system constructed based on a plurality of phased array radars can obtain better target detection, parameter estimation and target tracking performances by effectively fusing observation information of the plurality of radars.
In practical application, the resources of the phased array radar have an upper limit, for example, the emission energy of the system has an upper limit, and for a networking system constructed by a plurality of phased array radars, in a multi-target tracking scene, the resources of the whole networking system need to be distributed and adjusted among a plurality of tracking tasks, namely, the cooperative target tracking of the plurality of phased array radars is realized through effective resource management. Therefore, how to fully utilize the cognition of the radar network on the target information in the target tracking process, reasonably match the detection relation between the target and the radar and adjust the corresponding radar radiation energy becomes a problem which needs to be researched.
In the aspect of resource management of a phased array radar networking system, a certain research foundation exists. The document (Xie M, Yi W, Kong L. Joint node selection and power allocation for multi-target tracking in centralized radio networks [ J ]. IEEE Transactions on Signal Processing,2016,66(3): 729-. In the literature (Yan J, Liu H, Jiu B, et al. Power allocation algorithm for target tracking in unmodulated connected wave near network [ J ]. IEEE Sensors Journal,2014,15(2): 1098-. The problem is expanded in the literature (Yan J, Liu H, Zheng B. Power allocation scheme for target tracking with multiple radio system [ J ]. Signal Processing,2018,144:453-458.) and is likewise solved by a gradient-based method. In documents (Yan J, Pu W, Liu H, et al. cooperative target assignment and dwell allocation for multiple target tracking in a phased array radar network [ J ]. Signal Processing,2017,141:74-83.), a coordinated node selection and time resource allocation algorithm is proposed for the multi-target tracking problem in a phased array radar network, so as to effectively improve the tracking performance of the worst target. A centralized target tracking joint power bandwidth allocation scheme for multi-Radar systems is considered in the literature (Yan J, Pu W, Liu H, et al. Joint power and bandwidth allocation for centralized target tracking in multiple Radar system [ C ].2016CIE International Conference on Radar (RADAR), Guangzhou,2016: 1-5.). A Radar Network with limited residence time is considered in a document (Liu X, Xu Z, Wang L, et al. cognitive weighted time Allocation [ J ]. IEEE Sensors Journal,2020,20(99):5092 and 5101.), and the residence time Allocation is adjusted in an adaptive manner by solving an optimization problem through second-order cone programming so as to improve the multi-Target comprehensive Tracking performance, and a method for optimizing the distribution of the residence time in the document (Dai J, Yan J, Wang P, et al. optimal Resource Allocation for Multiple Target Tracking in phase Array radio Network [ C ].2019International reference Control, Automation and Information science (CAIS), chemistry 2019: 1-4) aims at the multi-Target Tracking Network and simultaneously reduces the Target Resource consumption precision compared with the multi-Target Tracking method.
The resource management strategy aims to improve the target tracking precision, or simultaneously optimize the target tracking precision and the resource consumption. In the actual target tracking, the expected tracking precision can be set for each target according to the importance degree of each target, the resource consumption of the system is minimized under the condition of ensuring the expected tracking precision of multiple targets, and the tracking precision of the targets does not need to be seriously improved without limit. In the literature (Sun W, Yi W, Xie M, et al, adaptive node and power adaptive scheduling algorithm for target tracking in distributed multi-radar systems [ C ].2017International Conference on Information Fusion (Fusion), Xi' an,2017:1-6.), an adaptive node selection and power control method is proposed, and the total emission energy can be minimized under a given expected tracking precision by optimizing and scheduling nodes and power resources through a feasible direction method. A joint beam and residence time distribution strategy for multi-target tracking in a phased array radar system is proposed in documents (Wang X, Yi W, Kong L.Joint beam selection and dwell time for multi-target tracking in a phased array radar system [ J ]. Journal of Radars,2017.), and under the condition that preset expected tracking accuracy is met, multi-target tracking is realized by optimizing residence time and using fewer system resources. The whole solving process of the optimization problem adopts a method in an optimization theory to solve through a relaxation means, the operation complexity is high, and the real-time performance is not achieved.
Aiming at the problems, the invention provides a real-time resource management method for a phased array radar networking system, which realizes the matching between multiple radar nodes and multiple targets by utilizing heuristic rules on one hand, and completes the radiation energy optimization of activated nodes by adopting a golden section method on the other hand, thereby minimizing the total resource consumption of the system under the condition of ensuring the expected tracking precision.
Disclosure of Invention
Suppose that the phased array radar networking system tracks Q targets, the set of targets is {1,2 … Q }, and at t k The state set of each target at a moment is
Wherein t is k(q) Is the latest update time of the target q,
is t k(q) State estimation after time update, P q (t k(i) ) Is the estimated error covariance matrix of the target q. The radar networking system comprises N phase control array radars, the radar set is {1,2 … N }, and the coordinate of the nth radar is (x) n ,y n ) Wherein x is n Yn represent the coordinates of the radar n in the x-direction and y-direction, respectively. The desired tracking accuracy of each target is
Q is 1,2 … Q, and the maximum emission energy and the minimum emission energy of the radar n are respectively e n,max 、e n,min . A real-time resource management method of a phased array radar networking system comprises the following specific steps:
wherein F represents the state transition matrix, Q is the process noise covariance matrix, J q (t k ) Indicating the BFIM currently acquired for target q. Wherein
Using formulas
Obtaining the predicted tracking precision of each target, wherein trace (·) represents the tracing operation, and Λ is a tracking precision extraction matrix which satisfies the requirement
Wherein x n 、y n Respectively representing the coordinates of the radar n in the x-direction and the y-direction,
respectively represent t k+1 Time of day target q * Coordinates in the x-direction and y-direction. Target q * The distances between the radar sets and each radar are sorted, and then the distance target q in the radar set is sorted * Nearest radar n * Is assigned to a target q * . At this time, the targets q in the target set * And (5) deleting.
And 5, after the group of matching relations are obtained, calculating the energy required by the radar under the current matching relation.
Step 5.1: firstly, calculating a tracking accuracy value when the target is irradiated by the maximum emission energy:
wherein
The expression of (a) is as follows:
in the formula, G t,n Representing the transmission gain, G, of the radar r,n Indicates the reception gain, ζ, of the radar n Representing the wavelength, alpha, of the radar signal q,n (t k+1/k ) RCS of target q with respect to radar n, k denotes boltzmann constant, k is 1.38 × 10 -23 ，T n As noise temperature, F n Is the noise coefficient, L n Is a loss factor, r q,n (t k+1/k ) Representing the predicted distance between the target q relative to the radar n, c 1 ＝1.57，c 2 1.81 is a constant, Δ r n Indicating the range resolution of the radar. B is n Represents the beam width of the radar n and satisfies B n ＝1.76/M n ，M n Is the array element number of the phased array radar n,
representing a linearized measurement transfer matrix satisfying the formula at the measurement function
The time is calculated as:
in the formula
Respectively representing the x coordinate and the y coordinate of the target state under the Cartesian coordinate system of the radar n in one step prediction.
Comparing the tracking accuracy when the target is irradiated with the maximum emission energy with the desired tracking accuracy, if
Proceed to step 5.2. Otherwise, the maximum emission energy is directly output as the radiation energy of the radar at the moment.
Step 5.2: first order
The end length of the interval is Δ e at [ a ] 1 ,b 1 ]Inserting a dividing point:
λ 1 ＝a 1 +0.382(b 1 -a 1 ) (8)
μ 1 ＝a 1 +0.618(b 1 -a 1 ) (9)
calculating the prediction tracking precision at each segmentation point
Calculating the absolute value of the difference value between the prediction tracking precision and the expected tracking precision at each division point to obtain a function value at the corresponding division point:
let k equal to 1;
step 5.3: if b is k -a k Stopping the calculation when delta e is less than or equal to delta e, and outputting b k For optimal emission energy. Otherwise, if f (λ) k )＞f(μ k ) Go to step 5.4, if f (λ) k )≤f(μ k ) Go to step 5.5.
Step 5.4: let a be k+1 ＝λ k ，b k+1 ＝b k ，λ k+1 ＝μ k ,μ k+1 ＝a k+1 +0.618(b k+1 -a k+1 ) Calculating the function value f (mu) k+1 ) Go to step 5.6.
And step 5.5: let a k+1 ＝a k ，b k+1 ＝μ k ，μ k+1 ＝λ k ,λ k+1 ＝a k+1 +0.382(b k+1 -a k+1 ) Calculating the function value f (lambda) k+1 ) Go to step 5.6.
Step 5.6: let k be k +1 and return to step 5.3.
And 7, outputting the optimal matching relation matrix and the radiation energy matrix at the current moment as the optimization result at the current moment.
Principle of the invention
Suppose that a networking system constructed by N phase control array radars realizes the tracking of Q targets. In the tracking process, the resource consumption of the system is minimized under the condition of ensuring the expected tracking precision of each target by adaptively adjusting the matching relation between the radar and the target and the transmitting energy of the radar.
First, a table is defined to represent t k+1 Time phased array radarA target radar matching relationship matrix of the irradiation relationship with the target:
in the formula u q,n (t k+1 ) The variable is 0-1, and represents the condition that the nth phased array radar irradiates the target q:
since each radar beam can only illuminate one target in the same tracking interval, U (t) k+1 ) The medium element needs to satisfy the constraint:
in addition, corresponding to the target-radar matching relation matrix, a matrix for representing t is defined k+1 A radar resource allocation matrix of the time phased array radar transmitting energy:
in the formula, e q,n (t k+1 ) Is a continuous variable, which represents the radiation energy of the nth phased array radar irradiating the target q, obviously:
e q,n (t k+1 )≥0,q＝1,…,Q,n＝1,…,N (18)
meanwhile, the emission energy e of the phased array radar n aiming at the target q q,n Satisfies the following conditions:
under the conditions of the current matching relationship and the emission energy, in order to meet the requirement of tracking precision, the tracking precision of each target needs to meet the expected tracking precision:
based on this, the problem of using as little system emission energy as possible to ensure that the target tracking accuracy meets the expected value can be expressed as an optimization problem model as follows: (for convenience of description, the time scale is omitted in the following description)
And solving the optimization problem, and determining which target is matched with the radar resource in sequence in order that each target can meet the tracking precision requirement, so that the tracking precision of all targets under the condition that any radar emission energy resource is not allocated is calculated at first, and whether the target expected tracking precision can be met or not when the resource is not allocated is judged. Since the tracking performance improves as the resource consumption increases, the corresponding radar resource should be allocated to the target that does not meet the desired tracking accuracy first, and the target that has met the desired tracking accuracy does not need to be updated and deleted from the target set. And for the targets with tracking accuracy which is not full of the feet, sorting the absolute values of the differences between the predicted tracking accuracy and the expected tracking accuracy, selecting the target with the largest difference, and preferentially matching the target with the corresponding radar for tracking, as shown in the step 1-3.
Under the same radar radiation energy condition, the measurement signal-to-noise ratio of the target is mainly in inverse proportion to the 4 th power of the distance between the target and the phased array radar, so that a short-distance radar is allocated to the target, and the consumption of the radar radiation energy can be reduced as much as possible. After the target with the radar preferentially allocated is determined, the distance between the target and each vacant radar in the radar set is calculated according to the formula (4), and the nearest radar is selected to be allocated to the current target, as shown in step 4.
After the current set of matching relationships is obtained, the radiation energy of the radar in the matching relationships needs to be calculated.Under the matching relation, zero-mean measurement noise v corresponding to the measurement process when the target is tracked q,n The covariance satisfies:
wherein
And
respectively satisfy:
SNR in the formula q,n (t k+1 ) Signal-to-noise ratio representing the corresponding measurements:
it can be seen that the emission energy resource of the radar mainly influences the lower bound of the covariance of the measurement error by influencing the signal-to-noise ratio of the measurement, and further influences the measurement FIM of the target. Therefore, the magnitude of the energy is correlated with the predictive tracking accuracy of the target. In order to save the corresponding energy resources, the optimal radiation energy should be such that the actual tracking accuracy is exactly equal to the desired tracking accuracy. On one hand, the radiation energy is not enough, the requirement of the expected tracking precision cannot be met, and on the other hand, the radiation energy is too much, so that the actual tracking precision is lower than the expected tracking precision, and the waste of resources is caused. Therefore, a mapping relation is constructed, wherein the absolute value of the deviation between the actual tracking precision and the expected tracking precision is a function value, and the distributed energy is used as an independent variable. The optimal radiation energy is the energy value that makes the function take the value 0.
Firstly, the formula (5) is used to calculate whether the maximum emission energy under the matching relation can meet the expected tracking precision,
if this is the case, the optimum radiant energy value needs to be determined. Since the functional relationship is in a unimodal form, the optimal radiation energy just meeting the expected tracking accuracy can be calculated by adopting the golden section method. When one radar exhausts all resource quantity, namely the maximum emission energy is selected and cannot meet the expected tracking precision of the target, in order to achieve a better tracking precision value, the maximum energy is directly output as the radiation energy of the radar at the moment.
Updating a matching relation matrix U (t) based on the matching relation and radar transmission energy k+1 ) And a radar energy resource allocation matrix E (t) k+1 ) And further updating the tracking precision of the target. And continuously traversing the other targets in the target set and the other radars in the radar set, and repeating the process until the radar set or the target set is empty, so that the matching relation between the radar and the targets under the tracking and the radar radiation energy can be obtained as an optimization result, as shown in step 6.
Drawings
FIG. 1 is a tracking scene graph
FIG. 2 is a graph of matching relationships and energy optimization
FIG. 3 is a graph comparing actual tracking accuracy and expected tracking accuracy of a target
FIG. 4 is an energy comparison graph of the algorithm and the fixed matching relation algorithm
Detailed description of the preferred embodiments
Assume that in a two-dimensional target tracking scenario, there is a distributed phased array radar network, which includes 4 phased array radars with different positions, and the target measurement information that can be obtained by each radar is the distance and azimuth of the target, and the radar parameters are shown in table 1. The radar networking system is utilized to track 4 point targets in the area, the position distribution of the radar and the initial state information of the targets are shown in table 2, and the tracking accuracy requirements of the 4 targets are set to be
TABLE 1 cognitive Multi-target tracking Radar parameter setting for distributed phased array Radar network
TABLE 2 cognitive multi-target tracking simulation scene setting of distributed phased array radar network
| Radar serial number | Position of | Target serial number | |
| 1 | 10km,10km | 1 | 30km,0m/s,20km,400m/ |
| 2 | 20km,90km | 2 | 20km,400m/s,70km,0m/ |
| 3 | 90km,90km | 3 | 70km,0m/s,80km,-400m/ |
| 4 | 90km,10km | 4 | 80km,-400m/s,30km,0m/s |
As shown in fig. 2, the algorithm can adaptively select the energy value with the change of the tracking state on the premise of meeting the requirement of the tracking accuracy. Meanwhile, as the distance between the radar and the target changes, the matching relationship between the radar and the target also changes in a self-adaptive manner, and when the target moves to a radar closer to the target, the radar closer to the target is switched to be used for tracking.
As shown in fig. 3, the tracking accuracy of the algorithm in each tracking frame can be maintained near the expected tracking accuracy, and only fluctuates during the switching process of the matching relationship, and then the tracking accuracy returns to near the expected value, which indicates that the algorithm can meet the requirement of the tracking accuracy, i.e. the constraint condition is ensured
In order to verify the advantages of the algorithm, as shown in fig. 4, the algorithm is compared with an algorithm with a fixed matching relationship, wherein the fixed relationship is that the radar 1 tracks the target 2, the radar 2 tracks the target 3, the radar 3 tracks the target 4, and the radar 4 tracks the target 1. Under the fixed matching relation, the energy consumed by each radar always adopts the maximum value in the tracking process, and the energy consumption is far higher than that of the algorithm.
The processor of the algorithm operation platform is (Intel (R) core (TM)) i5-6300HQ CPU @2.30GHz 2.30GHz, the operation software is MATLAB 2020a, 100 Monte Carlo simulations are carried out, the average operation time is 1.2173ms, and the requirement of real-time property can be met.
In conclusion, the algorithm provided by the invention is a real-time resource management method for a phased array radar networking system, which can adjust the matching relationship between a radar and a target in real time according to the target condition, and realize the self-adaptive selection of the radar transmitting energy on the premise of meeting the tracking precision requirement, thereby achieving the purpose of saving system resources.
Claims (1)
1. A real-time resource management method for a phased array radar networking system comprises the following specific technical scheme:
suppose that the phased array radar networking system tracks Q targets, the set of targets is {1,2 … Q }, and at t k The state set of each target at a moment is
Wherein t is k(q) Is the latest update time of the target q,
is t k(q) State estimation after time update, P q (t k(i) ) Is the estimated error covariance matrix of the target q; the radar networking system comprises N phase control array radars, the radar set is {1,2 … N }, and the coordinate of the nth radar is (x) n ,y n ) Wherein x is n 、y n Respectively representing the coordinates of the radar n in the x direction and the y direction; the desired tracking accuracy of each target is
The maximum transmitting energy and the minimum transmitting energy of the radar n are respectively e n,max 、e n,min (ii) a A real-time resource management method of a phased array radar networking system comprises the following specific steps:
step 1, initializing a target-radar matching relation matrix U (t) k+1 ) Radar energy resource allocation matrix E (t) k+1 ) Is an all-zero matrix with Q rows and N columns;
step 2, calculating BFIM (Bayes Fisher Information matrix) of the targets in the target set under the condition that no radar emission energy resource is allocated:
wherein F represents the state transition matrix, Q is the process noise covariance matrix, J q (t k ) Representing the currently acquired BFIM of the target q; wherein
Using formulas
Obtaining the predicted tracking precision of each target, wherein trace (·) represents the tracing operation, and Λ is a tracking precision extraction matrix which satisfies the requirement
Step 3, judging whether all the targets are met
If the target prediction tracking precision is satisfied, deleting the target prediction tracking precision from the target set, and calculating the absolute value of the error between the prediction tracking precision of each target at the current moment and the expected tracking precision of each target for the unsatisfied target
Finding a target q corresponding to the maximum value of the absolute value of the tracking precision error * For the target q, as shown in step 4 * Allocating radar resources;
step 4, firstly, calculating a target q * Distance to each radar in the radar set
Wherein x n 、y n Respectively representing the coordinates of the radar n in the x-direction and the y-direction,
respectively represent t k+1 Time of day target q * Coordinates in the x-direction and y-direction; target q * The distances between the radar sets and each radar are sorted, and then the distance target q in the radar set is sorted * Nearest radar n * Is assigned to a target q * (ii) a At this time, the targets q in the target set * Deleting;
step 5, after the group of matching relations are obtained, calculating the energy required by the radar under the current matching relation;
step 5.1: firstly, calculating a tracking accuracy value when the target is irradiated by the maximum emission energy:
wherein
The expression of (a) is as follows:
in the formula, G t,n Representing the transmission gain, G, of the radar r,n Indicates the reception gain, ζ, of the radar n Representing the wavelength, alpha, of the radar signal q,n (t k+1/k ) RCS of target q with respect to radar n, k denotes boltzmann constant, k is 1.38 × 10 -23 ，T n As noise temperature, F n Is the noise coefficient, L n Is a loss factor, r q,n (t k+1/k ) Representing the predicted distance between the target q relative to the radar n, c 1 ＝1.57，c 2 1.81 is a constant, Δ r n Indicating the range of the radarResolution, B n Represents the beam width of the radar n and satisfies B n ＝1.76/M n ，M n Is the array element number of the phased array radar n,
representing a linearized measurement transfer matrix satisfying the formula at the measurement function
The time is calculated as:
in the formula
Respectively representing the x coordinate and the y coordinate of the target state under the Cartesian coordinate system of the radar n in one step prediction;
comparing the tracking accuracy when the target is irradiated with the maximum emission energy with the desired tracking accuracy, if
Then go to step 5.2; otherwise, directly outputting the maximum transmitting energy as the radiation energy of the radar at the moment;
step 5.2: first order
The end length of the interval is delta e and is in [ a ] 1 ,b 1 ]Inserting a dividing point:
λ 1 ＝a 1 +0.382(b 1 -a 1 ) (8)
μ 1 ＝a 1 +0.618(b 1 -a 1 ) (9)
calculating the prediction tracking precision at each segmentation point
Calculating the absolute value of the difference value between the prediction tracking precision and the expected tracking precision at each division point to obtain a function value at the corresponding division point:
let k equal to 1;
step 5.3: if b is k -a k Stopping the calculation when delta e is less than or equal to delta e, and outputting b k For optimal emission energy; otherwise, if f (λ) k )＞f(μ k ) Go to step 5.4, if f (λ) k )≤f(μ k ) Turning to step 5.5;
step 5.4: let a k+1 ＝λ k ，b k+1 ＝b k ，λ k+1 ＝μ k ,μ k+1 ＝a k+1 +0.618(b k+1 -a k+1 ) Calculating the function value f (mu) k+1 ) Turning to step 5.6;
step 5.5: let a k+1 ＝a k ，b k+1 ＝μ k ，μ k+1 ＝λ k ,λ k+1 ＝a k+1 +0.382(b k+1 -a k+1 ) Calculating the function value f (lambda) k+1 ) Turning to step 5.6;
step 5.6: making k equal to k +1, and returning to the step 5.3;
step 6, updating the matching relation matrix U (t) k+1 ) Let it be q-th * Line n * The column is 1; updating radar energy resourcesDistribution matrix E (t) k+1 ) Let it be q th * Line n * Listing as the energy value obtained by calculation in the step 5; at this time, the radars n in the radar set are combined * Deleting, checking whether the target set and the radar set are empty, and if not, returning to the step 2 until the radar set is empty or the target set is empty;
and 7, outputting the optimal matching relation matrix and the radiation energy matrix at the current moment as the optimization result of the current moment.
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