Nonstationary Target Detection Using Time–Frequency Multisqueezing Transform for DRM-Based Passive Radar

The digital radio mondiale (DRM)-based passive radar (PR) has now attracted wide attention for long-range aircraft detection. The use of long coherent integration time (CIT) is an efficient way to compensate for the large attenuation when the PR works via skywave propagation, but it also introduces nonstationarity into the echo signals due to target maneuver and ionospheric channel variation. This nonstationarity will severely degrade the performance of conventional constant false alarm rate (CFAR) detectors. To account for this problem, a novel processing scheme is proposed to improve the nonstationary target detection on the time–frequency (TF) plane. First, the TF multisqueezing transform (TFMST) is used to catch the TF signature and address energy divergence in the TF representation due to the ionospheric propagation. Next, morphological methodologies are applied to determine the regions containing potential targets. Finally, for each potential target, the duration, velocity, and energy are checked to decide whether it is a target. Simulation results show that the detection probability of the proposed method is significantly increased compared with that of the existing methods for a low signal-to-clutter-and-noise ratio (SCNR). When tested on a dataset collected in a PR experiment with an 1150 km far DRM illuminator, the proposed method can detect targets up to 125 km that cannot be detected by those existing methods, which further proves its superiority for nonstationary target detection.