Gradient-Based Optimization of PCFM Radar Waveforms

Abstract

While a number of signal structures have been proposed for radar, frequency modulation (FM) remains the most common in practice because it is well-suited to high-power transmitters, which tend to introduce significant distortion to other waveform classes. That said, various forms of coding provide useful parameterizations for which a variety of optimization methods can be readily applied to accomplish different operational goals. To that end, the polyphase-coded FM (PCFM) implementation was previously devised as a means to bridge this gap between optimizable parameters and physically realizable waveforms. However, the original method employed to optimize PCFM waveforms involved a piecewise greedy search that, while relatively effective, was rather slow and cumbersome. Here, the continuous nature of this framework is leveraged to formulate a gradient-based optimization approach that updates all parameters simultaneously and can be efficiently performed using fast Fourier transforms, thus facilitating a general design methodology for practical waveforms that is directly extensible to myriad waveform-diverse arrangements. Results include a large number of optimization assessments to discern performance trends in aggregate and detailed analysis of specific cases, as well as both loopback and free-space experimental measurements to demonstrate practical efficacy.