An adaptive waveform-detection threshold joint optimization method for target tracking
来源期刊：中南大学学报(英文版)2013年第11期
论文作者：WANG Hong-qiang(王宏强) XIA Hong-en(夏洪恩) CHENG Yong-qiang(程永强) WANG Lu-lu(王璐璐)
文章页码：3057 - 3064
Key words：cognitive radar; adaptive waveform selection; target tracking; joint optimization; detection-tracking system
Abstract: The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association (MPDA) filter. The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance. The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function, while the optimization problem is solved through the genetic algorithm (GA). The detection probability, false alarm probability and measurement noise covariance are all considered together, which significantly improves the tracking performance of the joint detection and tracking system. Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method, which will reduce the tracking error. The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m, while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m. Similar error reduction occurs for the velocity error and acceleration error.
WANG Hong-qiang(王宏强)1, XIA Hong-en(夏洪恩)2, CHENG Yong-qiang(程永强)1, WANG Lu-lu(王璐璐)1
(1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;
2. 66242 PLA Troops, Suniteyou Banner 011216, China)
Abstract:The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association (MPDA) filter. The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance. The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function, while the optimization problem is solved through the genetic algorithm (GA). The detection probability, false alarm probability and measurement noise covariance are all considered together, which significantly improves the tracking performance of the joint detection and tracking system. Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method, which will reduce the tracking error. The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m, while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m. Similar error reduction occurs for the velocity error and acceleration error.
Key words:cognitive radar; adaptive waveform selection; target tracking; joint optimization; detection-tracking system
