Optical scanning plays a very important role in LADAR system for large field of view (FOV). But it introduces a Optical scanning lag angle which depends on the detecting range and the angular rate of scanner. Optical scanning lag angle makes a part of received optical energy fall out of the optical sensitive area of detector which will greatly degrade the detecting performance of LADAR in many cases. Optical scanning lag effect and optical scanning lag angle are computational analyzed in detail in this paper. And two conceptual solutions, sub-area receiving way on unit optical sensitive area and electrical scan receiving way on detector array, are proposed.
In this paper, we study the technique of high precision terrain map generation using imaging laser radar. For getting equal distance square mesh terrain map, we use the multinomial function, the spline function, and the even B-spline function to fit the original scanning data, then resample using above results. In order to get real time terrain map, we have set up two model of resampling. One is the adjacent model, another is sub-adjacent model. The simulated experiment was finished on different conditions using different resampling methods. The simulated experimental results indicate that the imaging precision is higher using the spline function resampling than using other one, using the sub-adjacent model resampling has less error than using the adjacent model, but it has a less lag in time than using the adjacent model.
2-D entropic thresholding method is a very effective approach for image segmentation. But the computational complexity up to O(L4) greatly limits its application. Wu et al. proposed a fast recursive algorithm based on Abutaleb's 2-D entropic thresholding method, which reduced the computational complexity to O(L2) with the memory cost of 2*L floating words. This paper should present a novel fast searching algorithm for optimal thresholding vector, which reduces the computational complexity to about (3*L+w*w), where w is less than 10,with very little memory cost.
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