Long-term coherent integration can significantly improve the ability to detect maneuvering targets by radar. Especially for weak targets, longer integration times are needed to improve. But for non-radially moving targets, the time-varying angle between target moving direction and radar line of sight will cause non-linear range migration (NLRM) and non-linear Doppler frequency migration (NLDFM) within long-time coherent processing, which precludes existing methods that ignore angle changes, and seriously degrades the performance of coherent integration. To solve this problem, an efficient method based on Radon Fourier transform (RFT) with modified variant angle model (ARFT) is proposed. In this method, a new parameter angle is introduced to optimize the target motion model, and the NLRM and NLDFM are eliminated by range-velocity-angle joint three-dimensional searching of ARFT. Compared with conventional algorithms, the proposed method can more accurately compensate for the NLRM and NLDFM, thus achieving better integration performance and detection probability for non-radial moving weak targets. Numerical simulations verify the effectiveness and advantages of the proposed method.

This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

In this paper, we propose a technique for detection and real-time tracking of moving targets. This uses a color segmentation algorithm robust to irregular illumination variation and a line-based tracker. The former is based on statistical representation of a color. And, we can obtain a real-time property for detection and tracking of moving targets from the latter.

A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.