Cooperative Node Selection and Transmit Resource Allocation for Multi-Target Tracking in Multiple Radars Architecture
This paper puts forward a cooperative node selection and transmit resource allocation (CNS-TRA) strategy for multi-target tracking (MTT) in multiple radars architecture (MRA), whose objective is to improve the low probability of intercept (LPI) performance by jointly coordinating the radar node sche...
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          | Published in | International Conference on Control, Automation and Information Sciences (Online) pp. 94 - 98 | 
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| Main Authors | , , , | 
| Format | Conference Proceeding | 
| Language | English | 
| Published | 
            IEEE
    
        14.10.2021
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 2475-7896 | 
| DOI | 10.1109/ICCAIS52680.2021.9624503 | 
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| Abstract | This paper puts forward a cooperative node selection and transmit resource allocation (CNS-TRA) strategy for multi-target tracking (MTT) in multiple radars architecture (MRA), whose objective is to improve the low probability of intercept (LPI) performance by jointly coordinating the radar node scheduling, dwell time, transmit power and effective bandwidth of MRA subject to predefined target tracking accuracy requirement and several resource budgets. By incorporating the above controllable parameters, the Bayesian Cramér-Rao lower bound (BCRLB) is calculated and used as the accuracy metric for target tracking. Subsequently, this paper develops a fast and effective two-stage-based solution methodology to solve the resulting non-convex and non-linear optimization problem. Simulation results demonstrate that the CNS-TRA strategy has superiority over other existing algorithms and can achieve better LPI performance for MRA. | 
    
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| AbstractList | This paper puts forward a cooperative node selection and transmit resource allocation (CNS-TRA) strategy for multi-target tracking (MTT) in multiple radars architecture (MRA), whose objective is to improve the low probability of intercept (LPI) performance by jointly coordinating the radar node scheduling, dwell time, transmit power and effective bandwidth of MRA subject to predefined target tracking accuracy requirement and several resource budgets. By incorporating the above controllable parameters, the Bayesian Cramér-Rao lower bound (BCRLB) is calculated and used as the accuracy metric for target tracking. Subsequently, this paper develops a fast and effective two-stage-based solution methodology to solve the resulting non-convex and non-linear optimization problem. Simulation results demonstrate that the CNS-TRA strategy has superiority over other existing algorithms and can achieve better LPI performance for MRA. | 
    
| Author | Ding, Lintao Yan, Junkun Zhou, Jianjiang Shi, Chenguang  | 
    
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| Snippet | This paper puts forward a cooperative node selection and transmit resource allocation (CNS-TRA) strategy for multi-target tracking (MTT) in multiple radars... | 
    
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| SubjectTerms | Bandwidth Bayesian Cramér-Ran lower bound (BCRLB) Cooperative node selection and transmit resource allocation (CNS-TRA) Measurement multi-target tracking (MTT) multiple radars architecture (MRA) Radar clutter Radar tracking Resource management Simulation Target tracking  | 
    
| Title | Cooperative Node Selection and Transmit Resource Allocation for Multi-Target Tracking in Multiple Radars Architecture | 
    
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