An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Yan, Baixing; Xu, Luping; Zhao, Kun; Yan, Junkun

doi:10.1155/2016/3025642

Cited by 5 publications

(22 citation statements)

References 25 publications

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“…The methods in [16,17,18] have difficulty being performed on the current hardware (MPC8640D PowerPC). The efficient methods in our former work [19,20,21] were developed to meet the requirement of real-time processing and low memory. However, we found that the methods [19,20,21] are insufficient to cope with the complex environment.…”

Section: Models and Notationsmentioning

confidence: 99%

“…A series of algorithms has been developed in our former works [19,20,21,22] to address excellent performance, real-time processing, and low memory requirement simultaneously. A contour tracking algorithm is used in [19] to meet the real-time processing in advance. The detection approach in [20], which uses a region growing algorithm, is developed to improve location precision.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the sampling-based spatiotemporal detection method and the methods in [20,21,22] are integrated into the novel detection framework. The methods in [19,20,21] detect targets only by the current frame. In this work, both the current frame and several past frames are utilized to estimate the intensity of the clutter.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, both the current frame and several past frames are utilized to estimate the intensity of the clutter. Compared with former work [19,20,21], more video data are utilized to improve the detection performance. Thus, our former works [19,20,21,22] are components in the proposed framework.…”

Section: Introductionmentioning

confidence: 99%

“…The framework is designed to make each component work well with the others. Past frames have not been used in extended target detection before and the detection methods are not combined with target tracking methods in [19,20,21,22]. Their excellent performance can be improved by fine detection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Yan

Zhao

et al. 2019

Sensors

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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.

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