Biblio

Compressive Sampling and Sparse reconstruction theory is applied to a linearly frequency modulated continuous wave hybrid lidar/radar system. The goal is to show that high resolution time of flight measurements to underwater targets can be obtained utilizing far fewer samples than dictated by Nyquist sampling theorems. Traditional mixing/down-conversion and matched filter signal processing methods are reviewed and compared to the Compressive Sampling and Sparse Reconstruction methods. Simulated evidence is provided to show the possible sampling rate reductions, and experiments are used to observe the effects that turbid underwater environments have on recovery. Results show that by using compressive sensing theory and sparse reconstruction, it is possible to achieve significant sample rate reduction while maintaining centimeter range resolution.

A robust appearance model is usually required in visual tracking, which can handle pose variation, illumination variation, occlusion and many other interferences occurring in video. So far, a number of tracking algorithms make use of image samples in previous frames to update appearance models. There are many limitations of that approach: 1) At the beginning of tracking, there exists no sufficient amount of data for online update because these adaptive models are data-dependent and 2) in many challenging situations, robustly updating the appearance models is difficult, which often results in drift problems. In this paper, we proposed a tracking algorithm based on compressive sensing theory and particle filter framework. Features are extracted by random projection with data-independent basis. Particle filter is employed to make a more accurate estimation of the target location and make much of the updated classifier. The robustness and the effectiveness of our tracker have been demonstrated in several experiments.