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.

scholarly journals Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yijun Chen ◽  
Qun Zhang ◽  
Ying Luo ◽  
Tat Soon Yeo
Keyword(s):  
Resource Allocation ◽  
Feature Extraction ◽  
Extraction Method ◽  
Adaptive Resource Allocation ◽  
Feature Extraction Method ◽  
Single Beam ◽  
Resource Allocation Scheme ◽  
Target Detecting ◽  
Track Before Detect ◽  
Adaptive Resource

The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations.

scholarly journals A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1577 ◽  
Author(s):  
Bo Yan ◽  
Xu Yang Zhao ◽  
Na Xu ◽  
Yu Chen ◽  
Wen Bo Zhao
Keyword(s):  
Target Detection ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Real Data ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Detection And Tracking ◽  
Extended Target ◽  
Air Surveillance ◽  
Track Before Detect

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

scholarly journals A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Junjun Chen ◽  
Bing Xu ◽  
Xin Zhang
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Time Domain ◽  
Mahalanobis Distance ◽  
Extraction Method ◽  
Vibration Signal ◽  
Feature Extraction Method ◽  
Feature Parameters ◽  
Dimensional Parameters ◽  
Failure Types

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

scholarly journals The IR Characteristics Modeling and Simulation of the HEO Target

2018 ◽  
Vol 179 ◽  
pp. 01024
Author(s):  
Tao Dongxing ◽  
Lin Boying ◽  
Du Peng ◽  
Bi Yanqiang ◽  
Shang Yonghong ◽  
...  
Keyword(s):  
Target Detection ◽  
Design Space ◽  
Simulation Data ◽  
Detection And Tracking ◽  
Calculation Results ◽  
Ground Test ◽  
Processing Algorithms ◽  
Imaging Sensor ◽  
Space Target ◽  
Data Processing Algorithms

Space target infrared (IR) characteristics model can be used to design space target detection sensor, generating simulation data to validate the data processing algorithms, such as target detection and tracking. In the work, a HEO target IR characteristics model is built. The model consists of the geometry module and the IR radiometric module. Unlike the traditional space IR model, the temperature of cabin inner is used as the simulation origin. Using the model, the irradiance spectra of HEO target are calculated for different target temperature. With the calculation results, the main wavelength range for HEO detection is analyzed. Using the equvalent temperature, the work also designs a simulative target, which has the similar IR characteristics of a HEO target. The simulator can be used in the ground test for the imaging sensor or target decoy.

scholarly journals Lifting Scheme-Based Sparse Density Feature Extraction for Remote Sensing Target Detection

2021 ◽  
Vol 13 (9) ◽  
pp. 1862
Author(s):  
Ling Tian ◽  
Yu Cao ◽  
Zishan Shi ◽  
Bokun He ◽  
Chu He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Computational Complexity ◽  
Target Detection ◽  
Lifting Scheme ◽  
Feature Points ◽  
Network Efficiency ◽  
Feature Extraction Method ◽  
The Relationship ◽  
Target Detection Task

The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency.

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