Efficient pulse compression for LPI waveforms based on a nonparametric iterative adaptive approach

In order to achieve low probability-of-intercept (LPI), radar waveforms are usually long and randomly generated. Due to the randomized nature, Matched filter responses (autocorrelation) of those waveforms can have high sidelobes which would mask weaker targets near a strong target, limiting radar"™s ability to distinguish close-by targets. To improve resolution and reduced sidelobe contaminations, a waveform independent pulse compression filter is desired. Furthermore, the pulse compression filter needs to be able to adapt to received signal to achieve optimized performance. As many existing pulse techniques require intensive computation, real-time implementation is infeasible. This paper introduces a new adaptive pulse compression technique for LPI waveforms that is based on a nonparametric iterative adaptive approach (IAA). Due to the nonparametric nature, no parameter tuning is required for different waveforms. IAA can achieve super-resolution and sidelobe suppression in both range and Doppler domains. Also it can be extended to directly handle the matched filter (MF) output (called MF-IAA), which further reduces the computational load. The practical impact of LPI waveform operations on IAA and MF-IAA has not been carefully studied in previous work. Herein the typical LPI waveforms such as random phase coding and other non- PI waveforms are tested with both single-pulse and multi-pulse IAA processing. A realistic airborne radar simulator as well as actual measured radar data are used for the validations. It is validated that in spite of noticeable difference with different test waveforms, the IAA algorithms and its improvement can effectively achieve range-Doppler super-resolution in realistic data.