Yonghua Xue's research while affiliated with Yantai University and other places

The research on maritime target detection signals has significant value in various fields. Conventional statistical theory-based target detection methods are limited by the complex sea clutter environment and target characteristics, making it challenging to achieve high-performance detection. In practical scenarios such as maritime observation, the radar observation area is expansive. And the radar beam cannot remain fixed in one direction for prolonged intervals. Consequently, it is not feasible to accumulate multiple pulses within a single azimuth cell. Therefore, extraction of effective features from echo signals is not practical. To address this issue, this paper proposes a maritime target detection method based on the Spatial-Temporal Feature Attention Graph Convolutional Network (STFA-GCN) and radar signal graph data. Firstly, the multi-frame radar signal is converted into graph data to represent spatial-temporal features. Then a STFA-GCN model perform feature extraction and classification on the graph data nodes, realizing target detection in complex sea clutter backgrounds. The proposed method was tested and evaluated using various target datasets, exhibiting superior detection performance and generalization capabilities. On real measured signal test, the proposed method can achieve 0.917 detection probability at false alarm rate of 1.26×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . While the 3-frame accumulation CACFAR is 0.839 at 1.65×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .

Radar weak target detection under complex background has always been a worldwide difficult problem in the field of radar signal processing. With the development of novel radar system, more spatial dimensions can be provided for signal processing. Orthogonal waveform multiple-input-multiple-output (MIMO) radar's wide-transmission and narrow-reception with ubiquitous observation can effectively extend the dwell time of the target. It can realize the joint signal processing in the time, space, and frequency domain, and high-resolution estimation, thereby helping to accumulate target’s energy and suppress clutter, which can improve the ability for weak and small targets detection in strong clutter background. In view of the advantages of orthogonal waveform MIMO radar, the recent research progress of long-time integration and target detection technology are summarized in this paper. The concept and classification of orthogonal waveform MIMO radar long-time integration are introduced. Effective solutions of maneuvering targets integration using orthogonal waveform MIMO radar are provided from the aspects of characteristics of maneuvering target, transform domain coherent integration, tracking-before-detection, long-time coherent integration, and sparse time-frequency analysis etc. Finally, the problems in the existing research are summarized, and the future development of technology is provided as well.

Low observable moving target-detection technology under clutter background is the key factor affecting the radar performance. Here, frequency diverse array (FDA) MIMO radar is investigated for low-observable moving target detection. FDA-MIMO radar not only has all the advantages of phased array and MIMO radar, but also can make a two-dimensional joint estimation of the target distance and azimuth. In order to achieve coherent integration for Doppler processing, a novel three-dimensional processing method is proposed, i.e. space-range-Doppler focus (SRDF) processing. It utilises the property of FDA and high-resolution Doppler processing of MIMO. After discussing signal model of FDA-MIMO radar, the flowchart of SRDF-based low-observable moving target detection and estimation is provided. Finally, simulation of moving target detection in clutter background verifies that proposed method has better ability for joint angle-range-Doppler processing with higher resolutions.

Reliable and fast detection of maneuvering target in complex background is important for both civilian and military applications. It is rather difficult due to the complex motion resulting in energy spread in time and frequency domain. Also, high detection performance and computational efficiency are difficult to balance in case of more integration pulses. In this paper, we propose a fast and refined processing method of radar maneuvering target based on hierarchical integration detection, utilizing the advantages of moving target detection (MTD), fractional Fourier transform (FRFT) and fractional ambiguity function (FRAF). The method adopts two-stage threshold processing. The first stage is the coarse detection processing screening out the range units having possible moving targets. The second stage is called the refined processing, which uses FRFT or FRAF for coherent integration dealing with high-order motions, i.e., accelerated or jerk motion. And the second stage is carried out only within the rangebins after the first stage. Therefore, the amount of calculation can be greatly reduced while ensuring high detection performance. Finally, real radar data are used for verification of the proposed method, which shows better performance than the traditional MTD method with higher integration gain.