Abstract
Adaptive waveform design for cognitive radar in the case of extended target detection under compound-Gaussian (CG) sea clutter is addressed. Based on the CG characteristics of sea clutter, the texture component is employed to characterize the clutter ensemble during each closed-loop feedback and its estimation can be used for the next transmitted waveform design. The resulting waveform design problem is formulated according to the following optimization criterion: maximization of the output signal-to-interference-plus-noise ratio (SINR) for sea clutter suppression, and imposing a further constraint on sidelobes level of the waveform autocorrelation outputs for decreasing the false alarm rate. Numerical results demonstrate the effectiveness of this approach.
Highlights
IntroductionRadar detection in an ocean environment is challenging due to the non-Gaussian traits of sea clutter returns as viewed by high-resolution radars and/or at low grazing angles [1]
Radar detection in an ocean environment is challenging due to the non-Gaussian traits of sea clutter returns as viewed by high-resolution radars and/or at low grazing angles [1].Most traditional studies only focus on adaptive detection algorithms at the receiver end, but ignore the adaptive adjustment of transmitter’s illumination of the environment
Based on the CG characteristics of sea clutter, an investigation of intrapulse agility waveforms, which rely on the longer decorrelation time of the texture component, is feasible for improved extended target detection in sea clutter at least one dwell time by a cognitive radar system
Summary
Radar detection in an ocean environment is challenging due to the non-Gaussian traits of sea clutter returns as viewed by high-resolution radars and/or at low grazing angles [1]. The Gaussian assumption fails to handle clutter returns emerging non-Gaussian traits such as the increased occurrence of higher spikes or amplitudes To overcome this condition, a compound-Gaussian (CG) process is introduced [9] and several models for the CG process have been developed [10,11]. Sensors 2019, 19, 3957 improved detection under sea clutter has been considered and the transmitted waveform is adapted by minimizing its autocorrelation function where the clutter is estimated to be strong for clutter suppression where sea clutter returns are modelled as a CG process in this case, but this algorithm can only be used for detecting a point target. We attack a novel waveform design algorithm, namely, low range sidelobes of cognitive radar (LRSCR), for extended target detection in sea clutter backgrounds. An improved waveform design algorithm takes the output SINR improvement as well as peak sidelobes level (PSL) characteristics into consideration
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