Study on dim target detection and discrimination from sea clutter

Wang, Wenguang; Sun, Zhilin; Li, Chen-ming; Wang, Jun

doi:10.1007/s13344-013-0016-x

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The proposed method and other typical grouping algorithms in the literatures [5,11,20,21] are compared. Among them, the idea of the grouping algorithm in the literature [5] is classification-based grouping, while the grouping algorithms in the literatures [11,20,21] belong to clustering-based grouping, except that the clustering methods used are different.…”

Section: Performance Comparison With Other Grouping Algorithmsmentioning

confidence: 99%

“…They define the similarity calculation method between target attributes and construct the similarity matrix, and then achieve the target clustering by row-column change criterion and judging criterion. Literature [20] constructs a similarity matrix for naval targets, which can be used to group targets by finding the connection path of the similarity matrix. In the literature [21], a target clustering algorithm based on similarity matrix is proposed, which achieves matrix target clustering by designing row column transformation criteria and decision criteria.…”

Section: Introductionmentioning

confidence: 99%

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An aerial targets grouping method based on relation network

Liang

Zhou²,

Dong³

et al. 2023

The Journal of Engineering

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Aerial targets grouping is an important content of air battlefield situation awareness, and intelligent aerial targets grouping is an urgent need for intelligent air combat. Traditional aerial targets grouping methods have problems such as difficulty extracting air target features, difficulty designing formation similarity measures, and lack of data samples. To solve the above problems, this paper proposes a relational network based aerial targets grouping method to achieve end‐to‐end grouping under small sample conditions. Firstly, an adaptive clustering algorithm is proposed to preliminarily cluster the aerial targets in the air battlefield situation to obtain the group to be tested. Then, a representation of aerial target groups is designed, which unifies the multi‐dimensional features of the group's temporal, spatial, and attribute. Next, based on this representation, combining 3D convolutional neural networks (3DCNN) and long short‐term memory (LSTM), a neural network structure that can automatically extract multi‐dimensional features of groups is designed. After that, the relation module is used to learn the distance metric between groups. After training the relation network, the type of group to be tested can be identified, thereby achieving the goal of grouping. A dataset of 30 types of aerial target group patterns was established through simulation. The ablation experiments were conducted to verify the performance of each module, and the experimental results showed that the designed feature extraction module can effectively extract temporal sample features, while the designed relationship module performs better in extracting temporal sample information. By comparing several typical traditional aerial target grouping methods, the proposed method improves the grouping accuracy by 11.2% to 13.4%, achieving an accuracy of 91.1% in the 5‐way 1‐shot task and 95.6% in the 5‐way 5‐shot task.

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Section: Performance Comparison With Other Grouping Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An aerial targets grouping method based on relation network

Liang

Zhou²,

Dong³

et al. 2023

The Journal of Engineering

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show abstract

“…Dynamic programming tracking before detection (DP-TBD) has gained popularity due to it offering the advantages of simplicity and less pre-information being required [33][34][35]. It transforms the optimal estimate of the target physical motion trajectory into the merit function maximum integral, which is a multi-frame joint test statistic [36].…”

Section: Introductionmentioning

confidence: 99%

A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model

et al. 2021

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The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection.

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“…Track before detection (TBD) and coherent integration are the two main methods that have been studied and applied. The TBD method realises detection by increasing the cost function or the existence probability of the dim target through the accumulation of multi‐frame data [1–4]. This accumulation is essentially a non‐coherent integration that has specific requirements for the SCNR of single‐frame data; i.e.…”

Section: Introductionmentioning

confidence: 99%