A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positio...A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.展开更多
To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framewo...To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks.Firstly,an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established.Secondly,the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models.Finally,the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences,to enhance the training effectiveness and improve the algorithm convergence speed.Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.展开更多
A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, a...A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.展开更多
The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array ...The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array radar are described. The software system comprising a number of tasks is written in C language and implemented. The results show that the algorithm for the multitask adaptive scheduling and the multitarget data processing is suitable for multifunction phased array radars.展开更多
基金the High Technology Research and Development Programme of China (2003AA134030)
文摘A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.
基金supported by the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-23-04)。
文摘To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks.Firstly,an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established.Secondly,the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models.Finally,the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences,to enhance the training effectiveness and improve the algorithm convergence speed.Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.
基金supported by the National Natural Science Foundation of China (61372165)the Postdoctoral Science Foundation of China (201150M15462012T50874)
文摘A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.
文摘The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array radar are described. The software system comprising a number of tasks is written in C language and implemented. The results show that the algorithm for the multitask adaptive scheduling and the multitarget data processing is suitable for multifunction phased array radars.
