Detection of Weak Radar Return Signals Based on Pseudo-Time Domain Algorithm

When the signal-to-noise ratio (SNR) falls below −10 dB, signals are considered weak. Radar systems, particularly those on small platforms like drones and satellites, often face this challenge due to limited power, resulting in low transmit power and weak signals. Additionally, ambient noises in the detection environment can further obscure the echo signal, leading to low SNR. Satellite-based radar systems are further constrained by processing capabilities and limitations in data transmission bandwidth and communication time windows. As a result, received radar signals are often transformed into a frequency spectrum and relayed to ground stations for offline processing. To address these challenges, this study proposes a novel method that treats the frequency spectrum of the echo signal as an impulse signal and interprets the frequency domain as a “pseudo-time domain”. By applying conventional time-domain waveform detection methods, such as wavelet analysis, in this “pseudo-time domain”, we can significantly enhance the success rate and accuracy of offline detection for weak echo signals. Numerical simulations show that this approach can handle SNR as low as −34 dB or even lower, which achieves notable denoising effects and improves signal quality. In cases where the SNR reaches −40 dB, the detection success rate exceeds 95% in most instances. Outdoor experiments also maintain a 90% accuracy rate even at −34 dB SNR.