An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Abstract

Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework.

A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

“…The R C means the radar coverage range. The historical measurement Z c is exactly used to estimate the clutter components, i.e., (26) and (27). The clutter components in each grid cell and in N v detection bins ({S c j |j = 1, .…”

“…In S c j and S T j , the distributions of false alarm points in each cell generated by background (26) and clutter regions (27) are the same, as the variation of clutter is quite slow compared with that of the target cell. Therefore, after the subtraction of S c j and S T j (15), only the components of target (25) are reserved in S j , and it is much larger than the residual error of subtraction in (15).…”

“…In these scenarios, the clutter region suppression is significant in reducing false tracks and improving true target track life [25]. To address this issue, Spatio-temporal detection [26], a clutter map [27], and a prior knowledge-based method [25] have been developed to suppress the clutter region. In [25,[28][29][30], clutter region suppression using a clutter map is applied as preprocessing before target tracking.…”

A Novel Smooth Variable Structure Smoother for Robust Estimation

“…Unlike using the SVSF gain, the proposed SVSS method is capable of getting a better state estimation by using the innovation sequence and projection theory [8]. Our former work focus on maneuvering target tracking [50][51][52][53], the SVSS is also applied in maneuvering target tracking. The rest of the paper is structured as follows.…”