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.

A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

2021 ◽  
Vol 13 (23) ◽  
pp. 4942
Author(s):  
Bo Yan ◽  
Hua Zhang ◽  
Luping Xu ◽  
Yu Chen ◽  
Hongmin Lu
Keyword(s):  
Target Tracking ◽  
Target Detection ◽  
Detection Rate ◽  
Synthetic Data ◽  
Positional Information ◽  
High Dimensions ◽  
Extended Target ◽  
Background Clutter ◽  
Track Before Detect ◽  
Weak Target

A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists.

scholarly journals A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongli Liu ◽  
Zhonghui Wang ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Opposition Based Learning ◽  
Fuzzy Clustering Algorithm ◽  
The Impact

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.

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.

An efficient extended target detection method based on region growing and contour tracking algorithm

2017 ◽  
Vol 232 (5) ◽  
pp. 825-836 ◽  
Author(s):  
Bo Yan ◽  
LP Xu ◽  
JZH Yan ◽  
Cong Li
Keyword(s):  
Target Detection ◽  
Simulated Data ◽  
Region Growing ◽  
Real Data ◽  
Tracking Algorithm ◽  
Contour Tracking ◽  
Optimal Parameters ◽  
Extended Target ◽  
Proposed Model ◽  
Resolution Cell

Extended target detection in the presence of K-distributed clutter has gained a special interest in recent years. High-resolution radars allow a target to be found in several resolution cells. Therefore, the detection rate and the false alarm rate of an extended target should be analyzed by these cells jointly rather than in one single resolution cell. A detector in which all the cells whose magnitude affected by one target are considered jointly is present. The performance and optimal parameters of the detector are analyzed in detail. Meanwhile, the large amount of calculation caused by enormous raw data is also considered. Then, an efficient method based on region growing algorithm and contour tracking algorithm is proposed. Only part of the resolution cells is scanned once with the proposed method, while all the cells are scanned at least one times in the existing methods. Therefore, considerable calculations are saved. Furthermore, the proposed method and several existing methods are performed with real data and simulated data, and the results show that the proposed model is practical and efficient to detect the extended targets in K-distributed clutters.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Optimal Conductor Selection Using Grey Wolf Optimization

2016 ◽  
Vol 4 (3) ◽  
pp. 39
Author(s):  
Ramanaiah M. LAXMIDEVI ◽  
REDDY M. DAMODAR ◽  
◽  
Keyword(s):  
Grey Wolf ◽  
Grey Wolf Optimization
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