Diffuse reflection laser ranging is one of the feasible ways to realize high precision measurement of the space debris. However, the weak echo of diffuse reflection results in a poor signal-to-noise ratio. Thus, it is difficult to realize the real-time signal extraction for diffuse reflection laser ranging when echo signal photons are blocked by a large amount of noise photons. The Genetic Algorithm, originally evolved from the idea of natural selection process, is a heuristic search algorithm which is famous for the adaptive optimization and the global search ability. To the best of our knowledge, this paper is the first one to propose a method of real-time signal extraction for diffuse reflection laser ranging based on Genetic Algorithm. The extraction results are regarded as individuals in the population. Besides, short-term linear fitting degree and data correlation level are used as selection criteria to search for an optimal solution. Fine search in the real-time data part gives the suitable new data quickly in real-time signal extraction. A coarse search in both historical data and real-time data after the fine search is designed. The co-evolution of both parts can increase the search accuracy of real-time data as well as the precision of the history data. Simulation experiments show that our method has good signal extraction capability in poor signal-to-noise ratio circumstance, especially for data with high correlation.
The echo received from diffuse reflection laser ranging (DRLR) system for space debris and satellite without corner reflector is too weak to detect available echo-photon robustly. A new method based on image saliency feature for echophoton detection was proposed, which used the concept of image saliency in computer vision field to describe echophoton distribution feature. The O-C residue distribution information was used to generate O-C residue image, and then saliency feature recognition, Piecewise Hough detection and polynomial fitting were adopt orderly to obtain available echo signal. The actual experiment results show the effectiveness and robustness of the algorithm.