Moving Target Tracking Based on Improved GMPHD Filter in Circular SAR System

Zhang, Yun; Mu, Huilin; Jiang, Yicheng; Ding, Chuxiong; Wang, Yong

doi:10.1109/lgrs.2018.2878467

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…In the depthwise convolution, the size of 1×1×C' convolution kernel is used to weight these feature maps obtained through the depthwise convolution in the depth direction, where the C' stands for the number of convolution kernels, and then a new feature map Fout can be get. For the pointwise convolution, its parameters' number can be calculated through (8).…”

Section: A Aff Submodulementioning

confidence: 99%

“…Wang et al [7] proposes a new ship detector based on the rich target scattering information available from polarized synthetic aperture radar, which combines two-parameter constant false alarm rate (TP-CFAR) for detection that is able to suppress false alarms caused by SAR image cross partials in addition to further reducing the number of missed targets. A moving target identification and tracking approach based on the Gaussian mixed probability hypothesis density filter was suggested [8] to increase the detection accuracy in cluttered environments, this method can detect targets by removing stationary strong ground clutter. Considering the scattered noise, Li et al [9] proposed a twodomain sparse reconstruction method, in which the intensity information of image domain and the structure information of feature domain are both used to improve the robustness of detection performance.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Accurate Small Target Detection Algorithm Based on Feature Fusion and Cross-Layer Connection Network for the SAR Images

Sun,

Li,

Chen

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Target detection technology has been greatly improved for the synthetic aperture radar (SAR) images recently, due to the advancement in deep learning (DL) domain. However, because of the existence of clutter in the SAR images, it's still a challenge to detect small targets with high accuracy and low computational complexity. To solve this problem, a detection algorithm based on feature fusion and cross-layer connection (FFCLC) network is proposed in this paper. Firstly, the attention feature fusion (AFF) is applied to improve the feature fusion ability for the small targets through allocating weights to various feature maps adaptively. Meanwhile, the depthwise separable convolution (DW-Conv) is used to reduce the computational complexity caused by the increasement of network layers. Then, a cross-layer connection (Cross-Connect) submodule is proposed to fuse shallow features with deep features further. Finally, a multi-scale target detection (Multi-Detect) submodule is designed to improve the detection ability for the small targets. We compare the proposed algorithm with the other representative methods on the SAR-Ship-Dataset and SSDD, quantitative evaluations show that our proposed algorithm's can reach the highest computational efficiency. Therefore, because of the superior performance in terms of accuracy and efficiency, the algorithm proposed in this paper is more suitable to detect small targets for the SAR images.

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Section: A Aff Submodulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast and Accurate Small Target Detection Algorithm Based on Feature Fusion and Cross-Layer Connection Network for the SAR Images

Sun,

Li,

Chen

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…In practice, the receiver of a bistatic SAR system flies at a constant altitude, as a stable flight can stabilize the accuracy of the SAR [28]. For this reason, we consider a horizontally flying UAV at a constant altitude, H (in meters), in line with practical implementations, and focus this paper on 2D trajectory planning.…”

Section: B Uav Mobilitymentioning

confidence: 99%

Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing

Yuan

et al. 2022

IEEE Trans. Commun.

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Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.Index Terms-Joint communication and radar sensing, bistatic synthetic aperture radar, cellular-connected unmanned aerial vehicle, block coordinate descent. I. INTRODUCTIONRecent advancements in the fifth-generation and beyond (5G/B5G) networks are increasingly enabling extensive background situation-and position-aware smart applications, including autonomous driving, distant medical care, and smart industry [1]. 5G/B5G networks are also envisioned to offer high-resolution sensing in support of these applications [2]. For this reason, joint communication and radar sensing (JCAS) has been deemed as one of the essential technologies in 5G/B5G systems [3]. By integrating radio communication and sensing into a single system, JCAS measures the reflections of communication signals to sense the location, velocity, and feature signal of targets and motions [4], [5]. This is

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“…For multi-target tracking (MTT) task, the number and the states of targets are jointly estimated from a sequence of measurements. MTT is widely used in many applications, such as radar [1,2], sonar [3], computer vision [4], robotics [5], etc. Compared with single target tracking, the number of targets changes with time in MTT because of target birth and target death.…”

Section: Introductionmentioning

confidence: 99%

Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking

Huang

Wang

2019

Applied Sciences

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The likelihood determined by the distance between measurements and predicted states of targets is widely used in many filters for data association. However, if the actual motion model of targets is not coincided with the preset dynamic motion model, this criterion will lead to poor performance when close-space targets are tracked. For rigid target tracking task, the structure of rigid targets can be exploited to improve the data association performance. In this paper, the structure of the rigid target is represented as a hypergraph, and the problem of data association is formulated as a hypergraph matching problem. However, the performance of hypergraph matching degrades if there are missed detections and clutter. To overcome this limitation, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than the distance between predicted states and measurements. Consequently, the probability of each target associated with each measurement (joint association probabilities) can be obtained. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, because all undetected cases is considered, proposed approach is usable in real time only for a limited number of targets. Extensive simulations have demonstrated the significant performance improvement of our proposed method.

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Moving Target Tracking Based on Improved GMPHD Filter in Circular SAR System

Cited by 12 publications

References 13 publications

A Fast and Accurate Small Target Detection Algorithm Based on Feature Fusion and Cross-Layer Connection Network for the SAR Images

A Fast and Accurate Small Target Detection Algorithm Based on Feature Fusion and Cross-Layer Connection Network for the SAR Images

Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing

Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking

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