Small space target detection based on feature stability algorithm

2011 ◽  
Author(s):  
Jianwei Gao ◽  
Rui Yao ◽  
Lei Jiang ◽  
Jinqiu Sun ◽  
Yanning Zhang
Keyword(s):  
Target Detection ◽  
Small Space ◽  
Space Target ◽  
Feature Stability

Passive Radar Based on LOFAR Radio Telescope for Air and Space Target Detection

2021 ◽  
Author(s):  
Mateusz Malanowski ◽  
Konrad Jedrzejewski ◽  
Jacek Misiurewicz ◽  
Krzysztof Kulpa ◽  
Artur Gromek ◽  
...  
Keyword(s):  
Target Detection ◽  
Radio Telescope ◽  
Passive Radar ◽  
Space Target

Waveband Selection for Space Target Detection and Identification

2009 ◽  
Vol 29 (1) ◽  
pp. 67-71
Author(s):  
李雅男 Li Yanan ◽  
孙晓兵 Sun Xiaobing ◽  
乔延利 Qiao Yanli
Keyword(s):  
Target Detection ◽  
Detection And Identification ◽  
Selection For ◽  
Space Target

scholarly journals Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yijun Chen ◽  
Qun Zhang ◽  
Ying Luo ◽  
Tat Soon Yeo
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Feedback Loop ◽  
Feature Extraction Method ◽  
Feature Parameters ◽  
Detection And Tracking ◽  
Transition Set ◽  
Target Detecting ◽  
Track Before Detect ◽  
Space Target

The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method.

Design of Analysis Software for Measured RCS Data

2013 ◽  
Vol 333-335 ◽  
pp. 172-177
Author(s):  
Bo Lin Chen ◽  
Yun Tao Li
Keyword(s):  
Data Processing ◽  
Target Detection ◽  
Cross Section ◽  
Threat Assessment ◽  
Original Data ◽  
Visual Simulation ◽  
Analysis Software ◽  
Simulation Process ◽  
Space Target ◽  
Extract Information

Radar cross section is an extremely important parameter in narrowband radar application for space target detection, tracking, and identification, as well as for the purpose of threat assessment. An analysis algorism, together with related software for measured RCS data has been developed to extract information from the original data. Some analyzing methods for measured RCS data were introduced. The playback visual simulation process and data processing were elaborated, so as to handle the processing of software program. Finally the software running results were analyzed. The experiment results showed that the software could be used for the issue of analysis of measured RCS data in a reliable and systematical way. As a result, it provides us with a strong guarantee for space surveillance.

A Robust Space Target Detection Algorithm Based on Target Characteristics

2021 ◽  
pp. 1-5
Author(s):  
Bin Lin ◽  
Xia Yang ◽  
Jie Wang ◽  
Yangyang Wang ◽  
Kunpeng Wang ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Target Characteristics ◽  
Space Target
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