Computationally Efficient Constraint-Optimized Radar Mismatched Filtering for Waveform-Agile Systems

Abstract

Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression; however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficiently computed (for possible real-time operation) with the consideration of possible interference via collisions with other spectral users. Here, a computationally efficient radar mismatched filter (MMF) design framework is proposed, which provides the optimal integrated sidelobe level (ISL) or spectral template match for a constrained signal-to-interference-plus-noise ratio loss via the Lagrange dual problem with known interference. This approach builds off of previous works, which examine ISL MMFs with constrained signal-to-noise ratio loss and provides further extensions, analysis, and bounds for alternative quadratic constraints and design objectives. Furthermore, by leveraging the Toeplitz structure that arises in time-series problems and the bounds of the Lagrange dual function, efficient solvers are developed that leverage infinite-impulse-response-based circulant approximations and/or preconditioned conjugate gradient. The proposed filter design is compared against current linear solvers and is found to have a lower order computational complexity with less required sequential calculations. Filter performance is assessed via hardware-in-the-loop and open-air experimental measurements.