A Target Detection and Tracking Method for Multiple Radar Systems

Yan, Baixing; Xu, Liyun; Lü, Hongmin

doi:10.1109/tgrs.2022.3183387

Cited by 18 publications

(5 citation statements)

References 39 publications

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“…In multiple target tracking, radar target detection stands as a pivotal step towards achieving accurate target tracking. It enables the precise identification and localization of targets, thus furnishing indispensable inputs for subsequent tracking processes [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Wang,

Liu,

Wang

2024

Remote Sensing

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In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

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Section: Introductionmentioning

confidence: 99%

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Wang,

Liu,

Wang

2024

Remote Sensing

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“…Detecting and tracking weak targets in complex sea environments has always been a significant area of research in radar target tracking. When radar receives target echoes, the detections of targets are often interfered by clutters, affecting the tracking results [1]. Because sea clutter fluctuates randomly with time and space, detecting and tracking targets on the sea surface has become a challenging task in the field of tracking, especially for weak maneuvering targets [2].…”

Section: Introductionmentioning

confidence: 99%

Track-before-detect algorithm-based optimized interactive multiple model particle filtering for tracking maneuvering weak target

WU,

YANG

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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This paper proposes an optimized algorithm for tracking maneuvering weak radar targets in complex sea environments.In the proposal, we first preprocesses radar echo images using the fractional Fourier transform (FrFT) to reduce clutter interference. Then, Feedback factor and residual resampling are introduced to optimize the interactive multiple model particle filtering (IMMPF) algorithm to achieve effective tracking of maneuvering targets. Additionally, the optimized IMMPF is combined with track before detect algorithm (TBD) to suppress clutter and accumulate target trajectory. Simulation experiment results demonstrate that the proposed algorithm can effectively track targets compared to other TBD algorithms.

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“…Object detection in remote sensing images is crucial in interpreting aerial and satellite images, which is widely used in many fields, such as resource exploration [1], intelligent navigation [2], environmental monitoring [3] and target tracking [4]. The main task of remote sensing target detection is to determine whether there are targets of interest in remote sensing images and provide their spatial location.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Object Detection Based on Convolution and Swin Transformer

Jiang

2023

IEEE Access

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Remote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets. To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features. To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets. To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets. The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness. Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior. Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results.

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A Target Detection and Tracking Method for Multiple Radar Systems

Cited by 18 publications

References 39 publications

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

Track-before-detect algorithm-based optimized interactive multiple model particle filtering for tracking maneuvering weak target

Remote Sensing Object Detection Based on Convolution and Swin Transformer

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