Space-time adaptive processing （STAP） is an effective method adopted in airborne radar to suppress ground clutter. Multi- ple-input multiple-output （M1MO） radar is a new radar concept and has superiority over conv...Space-time adaptive processing （STAP） is an effective method adopted in airborne radar to suppress ground clutter. Multi- ple-input multiple-output （M1MO） radar is a new radar concept and has superiority over conventional radars. Recent proposals have been applying STAP in MIMO configuration to the improvement of the performance of conventional radars. As waveforms transmitted by MIMO radar can be correlated or uncorrelated with each other, this article develops a unified signal model incor- porating waveforms for STAP in MIMO radar with waveform diversity. Through this framework, STAP performances are ex- pressed as functions of the waveform covariance matrix （WCM）. Then, effects of waveforms can be investigated. The sensitivity, i.e., the maximum range detectable, is shown to be proportional to the maximum eigenvalue of WCM. Both theoretical studies and numerical simulation examples illustrate the waveform effects on the sensitivity of MIMO STAP radar, based on which we can make better trade-off between waveforms to achieve optimal system performance.展开更多
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 ad...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.展开更多
基金National Natural Science Foundation of China (60901056)National Basic Research Program of China (6139303)
文摘Space-time adaptive processing （STAP） is an effective method adopted in airborne radar to suppress ground clutter. Multi- ple-input multiple-output （M1MO） radar is a new radar concept and has superiority over conventional radars. Recent proposals have been applying STAP in MIMO configuration to the improvement of the performance of conventional radars. As waveforms transmitted by MIMO radar can be correlated or uncorrelated with each other, this article develops a unified signal model incor- porating waveforms for STAP in MIMO radar with waveform diversity. Through this framework, STAP performances are ex- pressed as functions of the waveform covariance matrix （WCM）. Then, effects of waveforms can be investigated. The sensitivity, i.e., the maximum range detectable, is shown to be proportional to the maximum eigenvalue of WCM. Both theoretical studies and numerical simulation examples illustrate the waveform effects on the sensitivity of MIMO STAP radar, based on which we can make better trade-off between waveforms to achieve optimal system performance.
文摘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.
