Study on the method to improve the signal-to-noise ratio of photon heterodyne long-distance detection

In long-distance detection using Geiger-mode avalanche photodiodes (GM-APDs) in photon heterodyne coherent lidar systems, background light reflected from the target and the limited pixel count of the detector significantly impact the signal-to-noise ratio (SNR) of the intermediate-frequency (IF) signals. This degradation in SNR is a key factor limiting the system's maximum detection range. To address this challenge, we propose a novel signal processing method, the Singular Spectral Constraint Ensemble Empirical Mode Decomposition (SSC_EEMD) with threshold screening. Initially, the IF signal frequency is a priori determined, followed by Ensemble Empirical Mode Decomposition (EEMD) applied to each signal frame. Subsequently, the sub-signals are processed based on their singular value properties, using threshold screening to enhance the detection probability for individual frame signals. Multi-frame signals are then processed using accumulation statistics. A phase compensation algorithm is introduced to resolve the issue of initial phase inconsistency in the detected signals, ensuring phase coherence and facilitating multi-frame time-domain accumulation to generate high-SNR signals. To validate the proposed method, we construct a GM-APD photon heterodyne coherent lidar system for outfield long-distance target detection. Experimental results demonstrate that the system achieves a maximum detection range of 4 km, with an improvement in detection probability of approximately 25.7 % and an SNR enhancement of up to 19.28 dB. This work lays the groundwork for improving detection probabilities and extending the detection range in future lidar systems.