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Abstract
Cognitive radar is a new framework of radar system proposed by Simon Haykin recently. Adaptive waveform selection is an important problem of intelligent transmitter in cognitive radar. In this paper, the problem of adaptive waveform selection is modeled as stochastic dynamic programming model. Then Q-learning is used to solve it. Q-learning can solve the problems that we do not know the explicit knowledge of state-transition probabilities. The simulation results demonstrate that this method approaches the optimal wave-form selection scheme and has lower uncertainty of state estimation compared to fixed waveform. Finally, the whole paper is summarized.
Highlights
Radar is the name of an electronic system used for the detection and location of objects
The simulation results demonstrate that this method approaches the optimal waveform selection scheme and has lower uncertainty of state estimation compared to fixed waveform
Adaptive waveform selection is an important problem in cognitive radar, with the aim of selecting the optimal waveform and tracking targets with more accuracy according to different environment
Summary
Radar is the name of an electronic system used for the detection and location of objects. Adaptive waveform selection is an important problem in cognitive radar, with the aim of selecting the optimal waveform and tracking targets with more accuracy according to different environment. In [3], an adaptive waveform selective probabilistic data association algorithm for tracking a single target in clutter is presented. In [6], radar waveform selection algorithms for tracking accelerating targets are considered. In [8], Incremental Pruning method is used to solve the problem of adaptive waveform selection for target detection. The problem of optimal adaptive waveform selection for target tracking is presented in [9]. The problem of adaptive waveform selection in cognitive radar is viewed as a problem of stochastic dynamic programming and Q-learning is used to solve it
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