Adaptive Waveform Selection for Optimizing Radar Practical Resolution Based on Euclidean Distance
Chengcheng Si, Bo Peng, Xiang Li · 2019
This paper focuses on radar resolution and corresponding waveform optimization. We introduce the Euclidean Distance between the probability density functions of radar measurements as the representation of radar practical resolution and propose a new adaptive waveform selection criterion maximizing the radar practical resolution. The Euclidean Distance-based resolution involves the effect of transmitted waveform, measurement noise and measurement model while the latter two are ignored in the conventional ambiguity function-based radar resolution. Experiment results with simulated data demonstrate the validation of the proposed waveform selection algorithm in decreasing the probability of error when distinguishing two targets.