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Sparsity Aware Adaptive Radar Sensor Imaging in Complex Scattering Environments

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Technical Report,09 May 2014,08 May 2015

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University of Maryland - Eastern Shore Princess Anne

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In this reporting period, we develop new radar imaging, estimation, and waveform encoding techniques that exploit prior knowledge of the target and its environment to improve system performance through sensing, learning, and exploitation. Our research accomplishment is three fold. First, we develop a variational Bayesian based framework to address the problem of multi-parameter estimation under compound Gaussian clutter in the context of cognitive radar. Results demonstrate an accelerated convergence of the proposed sequential estimation method with an improved asymptotic Cramer Rao bound compared with the conventional expectation-maximization EM method and the classic Bayesian approach, especially under small sample size. Second, we develop estimation method for range and Doppler using weighted OFDM waveforms for radar targets. We demonstrates that the proposed weighted OFDM modulation scheme results in a lower Cramer-Rao bounds for delay estimation compared with the classic constant-envelope OFDM modulation while meeting the requirement on the peak to average power ratio. Third, we study impact of waveform encoding on nonlinear electromagnetic tomographic imaging algorithms using multiple simultaneous excitation sources. By numerical simulations, we show that the proposed iterative image reconstruction algorithm using coded multiple source excitation achieves faster convergence and better quality images than the conventional single source excitation imaging.

Subject Categories:

  • Active and Passive Radar Detection and Equipment

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