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Waveform Design for Target Class Discrimination with Closed-Loop Radar

Waveform Design for Target Class Discrimination with Closed-Loop Radar

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The basic concepts of cognitive radar and considered the Bayesian framework as an engine for its operation has been summarized in this chapter. Clearly, two critical functions of CR are a probability update function that quantifies the system's understanding and an ability to respond by adapting subsequent transmit waveforms. We applied two waveform strategies to the problem of discriminating an unknown target realization in additive Gaussian noise. The waveforms are based on the SNR and MI criteria. The potential benefits of closed-loop operation are apparent, and we have proposed a diversity-based explanation for the observed performance.

Inspec keywords: AWGN; Bayes methods; object detection; waveform analysis; radar detection

Other keywords: unknown target realization; cognitive radar; SNR criteria; target class discrimination; closed-loop radar; waveform design; CR functions; probability update function; Bayesian framework; MI criteria; additive Gaussian noise

Subjects: Signal detection; Radar equipment, systems and applications; Mathematical analysis

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