Target Tracking in Interference Environments Reinforcement Learning and Design for Cognitive Radar Soft Processing

2008 ◽  
Author(s):  
Feng Zhou ◽  
Deyun Zhou ◽  
Geng Yu
Keyword(s):  
Reinforcement Learning ◽  
Target Tracking ◽  
Cognitive Radar ◽  
Soft Processing

Multi-Agent Reinforcement Learning Aided Intelligent UAV Swarm for Target Tracking

2021 ◽  
pp. 1-1
Author(s):  
Zhaoyue Xia ◽  
Jun Du ◽  
Jingjing Wang ◽  
Chunxiao Jiang ◽  
Yong Ren ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Target Tracking ◽  
Uav Swarm ◽  
Multi Agent

scholarly journals Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6595
Author(s):  
Chengzhi Qu ◽  
Yan Zhang ◽  
Xin Zhang ◽  
Yang Yang
Keyword(s):  
Reinforcement Learning ◽  
Target Tracking ◽  
Nearest Neighbor ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Association Method ◽  
Motion Characteristics

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

scholarly journals UAV Maneuvering Target Tracking in Uncertain Environments Based on Deep Reinforcement Learning and Meta-Learning

2020 ◽  
Vol 12 (22) ◽  
pp. 3789
Author(s):  
Bo Li ◽  
Zhigang Gan ◽  
Daqing Chen ◽  
Dyachenko Sergey Aleksandrovich
Keyword(s):  
Reinforcement Learning ◽  
Target Tracking ◽  
Uncertain Environments ◽  
Target Movement ◽  
Maneuvering Target Tracking ◽  
Novel Approach ◽  
Policy Gradient ◽  
Meta Learning ◽  
Experience Replay ◽  
Task Experience

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to realize the control of unmanned aerial vehicle (UAV), allowing a UAV to quickly track a target in an environment where the motion of a target is uncertain. This approach can be applied to a variety of scenarios, such as wildlife protection, emergency aid, and remote sensing. We consider a multi-task experience replay buffer to provide data for the multi-task learning of the DRL algorithm, and we combine meta-learning to develop a multi-task reinforcement learning update method to ensure the generalization capability of reinforcement learning. Compared with the state-of-the-art algorithms, namely the deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), experimental results show that the Meta-TD3 algorithm has achieved a great improvement in terms of both convergence value and convergence rate. In a UAV target tracking problem, Meta-TD3 only requires a few steps to train to enable a UAV to adapt quickly to a new target movement mode more and maintain a better tracking effectiveness.

Cognitive radar for target tracking using a software defined radar system

2015 ◽  
Author(s):  
Kristine L. Bell ◽  
Joel T. Johnson ◽  
Graeme E. Smith ◽  
Christopher J. Baker ◽  
Muralidhar Rangaswamy
Keyword(s):  
Target Tracking ◽  
Radar System ◽  
Cognitive Radar
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