A resource allocation strategy based on game theory was proposed in order to solve the real-time problem of search and tracking (SAT) resource allocation (RA). The system model of search and tracking was established, and SATRA was modeled as a non-cooperative game problem. The resource allocation problem between the search subspace and the tracking multi-object in the model was regarded as the cooperative game relationship. The Shapley value was used to complete the corresponding problem, and the double objective optimization model of Nash equilibrium was given. The above double objective optimization model was transformed into a single objective optimization problem by using the dynamic weighted ideal point method in order to quickly find the distribution solution that meets the preference of decision maker. A parallel hybrid genetic particle swarm optimization (PHGAPSO) algorithm was proposed to solve the optimal allocation scheme of the above optimization problem. The simulation results verified the effectiveness and advancement of the game allocation strategy and the superiority of the performance of the hybrid algorithm. The method was compared with the Pareto bi-objective optimization method under the same conditions. The experimental results show that the game theory method has higher flexibility. The average search signal-to-noise ratio is increased by 1.02%, and the tracking target error is reduced by 1.55%.
Yi-ming LIU,Wen SHENG. Game strategy of resource allocation for phased array radar search and tracking. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1883-1891.
Fig.2Schematic diagram of target motion trajectory in radar area of responsibility
参数
数值
参数
数值
L
100
c1
2
Pc
0.4
c2
2
Pm
0.05
Pres
0~1.0
T
50
v
0.002~0.010
ω
0.7
PH(t)
0.5
Tab.3Parameter design of PHGAPSO algorithms
Fig.3Comparison of resource allocation results between GT and PBO methods
Fig.4Comparison of search utility between GT and PBO methods
Fig.5Comparison of track utility values between GT and PBO methods
Fig.6Actual subspace resource allocation map at typical frame period
Fig.7Actual multi-target resource allocation map at typical frame period
算法
Hmax
tcom/s
PHGAPSO
0.007 368
3.505 643
GA
0.007 682
1.842 562
PSO
0.007 906
1.822 971
Tab.4Statistical performance index of each algorithm
Fig.8Comparison curve of performance curves of various optimization algorithms
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