Doppler Estimation for High-Velocity Targets Using Subpulse Processing and the Classic Chinese Remainder Theorem

TL;DR

This paper presents a rigorous statistical analysis of Doppler estimation in high-velocity radar targets, combining subpulse processing and the Chinese Remainder Theorem to improve detection accuracy and reduce false alarms.

Contribution

It introduces novel closed-form expressions for detection probabilities considering subpulse processing and CCRT, and compares their effectiveness against classic pulse processing.

Findings

SP-plus-CCRT reduces false alarm rate significantly

Analytical expressions match Monte Carlo simulation results

Enhanced radar detection performance with proposed techniques

Abstract

In pulsed Doppler radars, the classic Chinese remainder theorem (CCRT) is a common method to resolve Doppler ambiguities caused by fast-moving targets. Another issue concerning high-velocity targets is related to the loss in the signal-to-noise ratio (SNR) after performing range compression. In particular, this loss can be partially mitigated by the use of subpulse processing (SP). Modern radars combine these techniques in order to reliably unfold the target velocity. However, the presence of background noise may compromise the Doppler estimates. Hence, a rigorous statistical analysis is imperative. In this work, we provide a comprehensive analysis on Doppler estimation. In particular, we derive novel closed-form expressions for the probability of detection (PD) and probability of false alarm (PFA). To this end, we consider the newly introduce SP along with the CCRT. A comparison analysis between SP and the classic pulse processing (PP) technique is also carried out. Numerical results and Monte-Carlo simulations corroborate the validity of our expressions and show that the SP-plus-CCRT technique helps to greatly reduce the PFA compared to previous studies, thereby improving radar detection.