Extending the Radar Dynamic Range using Adaptive Pulse Compression

The matched filter in the radar receiver is only adapted to the transmitted signal version and its output will be wasted due to non-matching with the received signal from the environment. The sidelobes amplitude of the matched filter output in pulse compression radars are dependent on the transmitted coded waveforms that extended as much as the length of the code on both sides of the target location. In order to detect a weak target in vicinity of strong target, the sidelobes of the matched filter output resulting from the strong target masked the weak target and didn’t detect its. Generally, the radar dynamic range is defined by the maximum power ratio to the minimum detectable power that is dependent on the level of the threshold and the sidelobe levels. Adaptive algorithms suppress the sidelobe levels to noise level with condition of maintain the range resolution and therefore increase the dynamic range. In this paper, an improved algorithm (in terms of computational cost and Doppler robustness) is proposed based on the minimum mean square error (MMSE) estimator denoted as Flexible Filter Length-Adaptive Pulse Compression Repair (FFL-APCR), which filter length depends on the length of transmitted code. It is also shown that the length of the code is influenced by determining the asymptotic peak sidelobe level and the dynamics range. In addition, the influence of the high-speed target on mainlobe broadening and the performance degradation of adaptive filters is investigated. Finally, extending of radar dynamic range with the proposed FFL-APCR algorithm is shown in various conditions and its performance evaluated by mean square error criteria. Where return signals coincide with the transmission of a pulse, pulse eclipsing can occur which results in detection performance loss. The mismatches (Doppler phase shift and pulse eclipsing) degrades performance of sidelobes suppression algorithms. The FFL-APCR algorithm suppresses range sidelobes by using a smaller filter length and reduces the computational cost. Consequently, this algorithm should be computationally efficient (real-time) to enable the practical application of RMMSE.