Abstract-This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)-Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans. In the proposed CS-TBD algorithm, the improved StOMP algorithm together with the temporal tracking, can effectively distinguish true targets from false targets and clutter based on information from multiple illuminations.
In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases.
Abstract-Space time adaptive processing (STAP) is a signal processing technique for detecting slowly moving targets using airborne radars. The traditional STAP algorithm uses a lot of training cells to estimate the space-time covariance matrix, which occupies large computer memory and is time-consuming. Recently, a number of compressed sensing based STAP algorithms are proposed to detect moving target in strong clutter situation. However, the coherence of the sensing matrix is not low due to the high resolution of the DOA (direction of arrival)-Doppler plane, which does not guarantee a good reconstruction of the sparse vector with large probability. Consequently, the direct estimation of the target amplitude may be unreliable using sparse representation when locating a moving target from the surrounding strong clutter. In this study, a novel method named similar sensing matrix pursuit is proposed to reconstruct the sparse radar scene directly based on the test cell, which reduces the computing complexity efficiently. The proposed method can efficiently cope with the deterministic sensing matrix with high coherence. The proposed method can estimate the weak elements (targets) as well as the prominent elements (clutter) in the DOA-Doppler plane accurately, and distinguish the targets from clutter successfully.
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