Shao Fei, Wu Chun, Wang Li-feng. Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network[J]. Journal of Electronics & Information Technology, 2010, 32(6): 1520-1524. doi: 10.3724/SP.J.1146.2009.01092
Citation:
Zheng Shi-Chao, Liu Ya-Bo, Song Hong-Jun, Yan He, Wu Kun. A Research on Moving Target Trajectory Simulation and Tracking in Wide Area Surveillance Ground Moving Target Indication Mode[J]. Journal of Electronics & Information Technology, 2013, 35(9): 2108-2113. doi: 10.3724/SP.J.1146.2013.00068
Shao Fei, Wu Chun, Wang Li-feng. Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network[J]. Journal of Electronics & Information Technology, 2010, 32(6): 1520-1524. doi: 10.3724/SP.J.1146.2009.01092
Citation:
Zheng Shi-Chao, Liu Ya-Bo, Song Hong-Jun, Yan He, Wu Kun. A Research on Moving Target Trajectory Simulation and Tracking in Wide Area Surveillance Ground Moving Target Indication Mode[J]. Journal of Electronics & Information Technology, 2013, 35(9): 2108-2113. doi: 10.3724/SP.J.1146.2013.00068
Wide Area Surveillance Ground Moving Target Indication (WAS-GMTI) mode is an important mode in airborne radar systems since it can monitor extensive area in a short time. However, there are few systems that possess WAS-GMTI mode. Therefore, real data of WAS-GMTI mode are difficult to obtain which have a great impact on the corresponding algorithm validation. In this paper, a target trajectory simulation method combined with electronic map is proposed. Since information of moving targets is based on information of platforms and electronic maps, simulating the echo of scenes can be avoided. Furthermore, the moving targets are combined with geographic environment reasonably and repetitious information of the same target can be used in tracking. Simulation results demonstrate the effectiveness of the proposed method.
Shao Fei, Wu Chun, Wang Li-feng. Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network[J]. Journal of Electronics & Information Technology, 2010, 32(6): 1520-1524. doi: 10.3724/SP.J.1146.2009.01092
Shao Fei, Wu Chun, Wang Li-feng. Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network[J]. Journal of Electronics & Information Technology, 2010, 32(6): 1520-1524. doi: 10.3724/SP.J.1146.2009.01092