Mr. Jinlei Ren Profile

Jinlei Ren

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Publications (3)

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Proceedings Article | 23 July 2022 Paper

Research status of collaborative detection of battlefield situation and its development trend in intelligent battlefield

Jia Zhang, Bin Hu, Ying Lu, Hu Huang, Zongyuan Wang, Yue Gu, Benchang Zheng, Jinlei Ren, Jun Li

Proceedings Volume 12277, 122771C (2022) https://doi.org/10.1117/12.2620696

KEYWORDS: Sensors, Target recognition, Radar, Target detection, Data fusion, Detection and tracking algorithms, Situational awareness sensors, Composites, Infrared radiation

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Battlefield situational awareness is the core condition that determines the success or failure of the battlefield, and it is also an important application direction of photodetectors. The rapid development of AI technology in recent years is about to cause major changes in future wars. The new AI battlefield will also put forward new urgent needs for situational awareness. This article summarizes the current main modes of collaborative detection of battlefield situation awareness and its research status, including radar / infrared composite detection, multi-source data fusion of radar / infrared detection, cooperative target recognition, target tracking, etc. On this basis, combined with the current development trend of the intelligence level of the main battlefield equipment, we get the development needs of future intelligent battlefield situational awareness for new types of collaborative detection, including requirements for its style, angle, speed, and detection targets of distributed collaborative detection. Based on this, the key development directions and core issues to be solved for intelligent battlefield situational awareness in the future are proposed.

Proceedings Article | 12 March 2021 Paper

Review of the overall parameter analysis of detection system of STSS

Jia Zhang, Ying Lu, Hu Huang, Jinlei Ren, Jijun Liu, Bin Hu, Jun Li

Proceedings Volume 11763, 1176395 (2021) https://doi.org/10.1117/12.2587650

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This article summarizes the reports and researches on detection system of STSS published in domestic and foreign literature, summarizes the development process of STSS detection system from a single geostationary orbit to a combined large elliptical orbit, then to the development process of the high-track network and the low-track network cooperate with each other. It also summarizes the development process of infrared detectors from single-band to multi-band, from line scan with less detection pixels to line scan with more detection pixels and large-area staring array. The application scenarios, overall scheme, and overall parameters of the STSS detection system are sorted out, and the core technical indicators are analyzed for the overall parameters. From the perspective of the development process of the US early warning satellite system and the technical characteristics of the space-based infrared system, US early warning satellite technology is of great significance to research on US defense programs, equipment, and system capabilities.

Proceedings Article | 12 October 2020 Paper

Small infrared target detection via iteratively reweighted nuclear norm

Haoran Gao, Jinlei Ren, Chunming He , Lizhen Deng

Proceedings Volume 11574, 115740I (2020) https://doi.org/10.1117/12.2575815

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The process of infrared images via computer-based algorithms for better application is a frontier field integrating physical technology with computer science. One of the key techniques in infrared image processing is the detection of infrared targets. This technique is extensively applied in security and defense systems and search and tracking systems. However, due to their small size, dim light and lack of texture, the detection of infrared targets is a technical problem. One strategy to address this problem is to transform the detection work into a non-convex optimization problem of recovering a low-rank matrix (background) and a sparse matrix (target) from a patch-image matrix (original image) based on IPI (infrared patch-image) model. When targets are clear and recognizable, the APG (accelerated proximal gradient) algorithm works effectively to solve it. However, when targets become much dimmer and are screened by the intricate texture of background, the experimental detection results degrade dramatically. In order to solve this problem, a novel method via IRNN (iteratively reweighted nuclear norm) is proposed in this paper. Experimental results show that under different complicated backgrounds, targets with higher SCRG (signal-to-clutter ratio gain) values and BSF (background suppression factor) values can be acquired through IRNN algorithm compared with the APG algorithm, which means that our method performs better.

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