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The sensitivity impact of range straddling in the form of mismatch loss is well known. What is less appreciated, however, is the effect upon dynamic range, particularly for receive filtering that seeks to minimize range sidelobes. For FMbased waveforms, which are readily implementable in a highpower radar system, it is shown that least-squares (LS) mismatched filtering (MMF) realizes a penalty in sidelobe suppression when range straddling occurs. This degradation can be partially compensated through modification of the LS MMF implementation. Alternatively, adaptive pulse compression (APC), appropriately modified for application to FM waveforms, demonstrates robustness to both straddling and eclipsing effects. Simulated and experimentally measured results are provided to demonstrate the efficacy of these filtering approaches.

Much work has been done to discover pulse compression methods that alleviate the effects of range sidelobes, though pulse compression filters that deviate from the matched filter suffer from varying degrees of mismatch loss. The Minimum Mean-Square Error (MMSE) based Adaptive Pulse Compression (APC) algorithm is capable of suppressing range sidelobes into the noise by employing a unique pulse compression filter for each range cell. Recently, Fast APC (FAPC) has been developed to reduce the computational cost of APC while maintaining much of the sidelobe suppression capability. This paper utilizes the MVDR framework to facilitate inclusion of a unity gain constraint within the APC and FAPC cost functions in an effort to mitigate mismatch loss. The APC algorithm exhibits almost no mismatch loss and, as such, the full-dimension algorithm benefits little from the gain constraint. However, FAPC occasionally suppresses small targets in dense scattering environments due to fewer degrees of freedom inherent to reduced-dimensionality processing. The constrained FAPC algorithm preserves gain on small targets consequently improving detection performance.

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Adaptive filtering for radar pulse compression has been shown to improve sidelobe suppression through the estimation of an appropriate pulse compression filter for each individual range cell of interest. However, the relatively high computational cost of full-dimension, adaptive range processing may limit practical implementation in many current real-time systems. Dimensionality reduction techniques are here employed to approximate the framework for pulse compression filter estimation. Within this approximate framework, two new minimum mean square error (MMSE) based adaptive algorithms are derived. The two algorithms are denoted as specific embodiments of the fast adaptive pulse compression (FAPC) method and are shown to maintain performance close to that of full-dimension adaptive processing, while reducing computation cost by nearly an order of magnitude (in terms of the discretized waveform length N).

Abstract-Some radar systems utilize pulse agility to achieve higher range resolution or mitigate range ambiguities. However, transmitting different waveforms on a pulse-to-pulse basis can have deleterious effects when traditional pulseDoppler processing is employed. In this paper a non-adaptive technique, entitled Non-Identical Multiple Pulse Compression (NIMPC), is derived that facilitates pulse-agile clutter cancellation and is readily implementable via fast convolution techniques. The new method is extended to account for clutterDoppler spread as well as multiple range-ambiguous clutter intervals. NIMPC is assessed via simulation of a synthetic wideband pulse train.

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