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Sparsity-Based Signal Processing for Noise Radar Imaging

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Noise radar systems transmitting incoherent signal sequences have been proposed as powerful candidates for implementing compressively sampled detection and imaging systems. This paper presents an analysis of compressively sampled noise radar systems by formulating ultra-wideband compressive noise radar imaging as a problem of inverting ill-posed linear systems with circulant system matrices. The non-linear nature of compressive signal recovery presents challenges in characterizing the performance of radar imaging systems. The suitability of noise waveforms for compressive radar is demonstrated using phase transition diagrams and transform point spread functions. The numerical simulations are designed to provide a compelling validation of the system. Non-idealities occurring in practical compressive noise radar systems are addressed by studying the properties of the transmit waveform. The results suggest that waveforms and system matrices that arise in practical noise radar systems are suitable for compressive signal recovery. Our analytical results are validated by field measurements made using an ultra-wideband millimeter-wave noise radar. Experimental verification is achieved by performing imaging experiments on various target scenarios. Even with a simple and inexpensive noise radar system, it is possible to benefit from the promises offered by the theoretical guarantees of compressive sensing.
Noise radar systems involve transmitting random-noise waveforms for radar imaging applications. In this paper, we propose that noise radar systems are suitable for implementing compressively sampled radar systems. We examine this hypothesis via simulations and experiments. The experiments were performed using a millimeter wave radar system transmitting signals with a bandwidth of about 500 MHz. We present models for non-idealities occurring in practical compressive radar systems and verify the prediction of the models via experiments. We formulate compressive sensing-based noise radar imaging as a problem of inverting ill-posed linear systems with circulant system matrices. We show that target scenes can be successfully recovered from reflected waveforms that are sampled at frequencies lower than the Nyquist rate. We demonstrate that even with a simple and inexpensive noise radar system, one can benefit from the promises offered by the theoretical guarantees of compressive sensing.

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Ram Narayanan

Field of Interest

“The field of interest shall be the organization, systems engineering, design, development, integration, and operation of complex systems for space, air, ocean, or ground environments. These systems include but are not limited to navigation, avionics, mobile electric power and electronics, radar, sonar, telemetry, military, law-enforcement, automatic test, simulators, and command and control."

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