Pulse-repetition-interval transform-based vibrating target detection and estimation in synthetic aperture radar

Abstract

A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority.

Hough transform‐based large micro‐motion target detection and estimation in synthetic aperture radar

“…Owing to the limitation of PRF, the TF curves of large micro‐motion target azimuth echo signals are no longer in the form of the sinusoid. Hence, many SFM signal detection algorithms such as PRI transform algorithm [8] and IRT algorithm [17] will become inapplicable.…”

“…In recent years, many in‐depth types of researches on the SAR micro‐Doppler effects have been carried out [1–23]. Owing to the micro‐motion characteristics of targets with rotation and vibration, the azimuth echo signals of micro‐motion targets are generally modelled as the form of the sinusoidal frequency modulation (SFM) signal.…”

“…This algorithm demonstrates better detection performance even under low signal‐to‐noise ratio (SNR) conditions. In [8], Deng et al found the similarities between the SFM signal spectrum and the pulse sequences and propose an algorithm to process the spectrum of the SFM signal by using Pulse repetition interval (PRI) transform for the first time, and this algorithm can detect vibration targets well. In [9], a two‐channel Displaced phase center antenna (DPCA) vibration target detection algorithm is proposed.…”

“…In [19], Yang et al demonstrate the IRT image of the aliased sinusoidal curves cannot be mapped to peaks. Hence, the algorithms proposed in [8, 15–17] may become invalid when detecting large micro‐motion targets. In general, most of the large micro‐motion targets are rotating ones (such as rotating antennas, helicopter rotors etc.…”

“…Since the large micro‐motion targets are common in the scene, and most of them are military‐sensitive targets, it is of great military significance to study the effective target detection and parameter estimation algorithms for large micro‐motion targets. However, most of the previously proposed algorithms are mainly applicable for small micro‐motion targets such as targets with vibration [8–12, 20–25], while effective SAR detection and estimation algorithms for large micro‐motion targets are rarely studied, which motivates this paper. This paper proposes a new algorithm for SAR detection and parameter estimation of large micro‐motion targets.…”