In a global positioning system(GPS)passive radar,a high resolution requires a high sampling frequency,which increases the computational load.Balancing the computational load and the range resolution is challenging.Thi...In a global positioning system(GPS)passive radar,a high resolution requires a high sampling frequency,which increases the computational load.Balancing the computational load and the range resolution is challenging.This paper presents a method to trade off the range resolution and the computational load by experimentally determining the optimal sampling frequency through an analysis of multiple sets of GPS satellite data at different sampling frequencies.The test data are used to construct a range resolution-sampling frequency trade-off model using least-squares estimation.The theoretical analysis shows that the experimental data are the best fit using smoothing and nthorder derivative splines.Using field GPS C/A code signal-based GPS radar,the trade-off between the optimal sampling frequency is determined to be in the 20461.25–24553.5 kHz range,which supports a resolution of 43–48 m.Compared with the conventional method,the CPU time is reduced by approximately 50%.展开更多
In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ...In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.展开更多
基金supported by the National Natural Science Foundation of China(42001297)the Research Foundation of Education Department of Hunan Province(19B061)the National Natural Science Foundation of Hunan Province(2021JJ40631)。
文摘In a global positioning system(GPS)passive radar,a high resolution requires a high sampling frequency,which increases the computational load.Balancing the computational load and the range resolution is challenging.This paper presents a method to trade off the range resolution and the computational load by experimentally determining the optimal sampling frequency through an analysis of multiple sets of GPS satellite data at different sampling frequencies.The test data are used to construct a range resolution-sampling frequency trade-off model using least-squares estimation.The theoretical analysis shows that the experimental data are the best fit using smoothing and nthorder derivative splines.Using field GPS C/A code signal-based GPS radar,the trade-off between the optimal sampling frequency is determined to be in the 20461.25–24553.5 kHz range,which supports a resolution of 43–48 m.Compared with the conventional method,the CPU time is reduced by approximately 50%.
基金supported by the National Natural Science Foundation of China(61472441)
文摘In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
