The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA (via its Inexact Augmented Lagrange Multiplier (IALM) model) can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart (i.e., processing batched data with RPCA), and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method.

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots' path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles' sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.

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Detection and recognition of obstacles are the most important concerns for fish robots to avoid collision for path planning as well as natural and smooth movements. The more information about obstacle shapes we obtain, the better control of fish robots we can apply. The method employing only simple distance measuring sensors without cameras is proposed. We use three fixed IR sensors and one IR sensor, which is mounted on a motor shaft to scan a certain range of foreground from the head of a fish robot. The fish robot's ability to recognize the features of an obstacle is improved to avoid collision based on the fuzzy neural networks. Evident features such as obstacles' sizes and angles are obtained from the scanned data by a simple distance sensor through neural network training algorithms. Experimental results show the successful path control of the fish robot without hitting on obstacles.