scholarly journals Adaptive Sensor Scheduling and Resource Allocation in Netted Collocated MIMO Radar System for Multi-Target Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109976-109988
Author(s):  
Zhengjie Li ◽  
Junwei Xie ◽  
Haowei Zhang ◽  
Houhong Xiang ◽  
Zhaojian Zhang
Keyword(s):  
Resource Allocation ◽  
Target Tracking ◽  
Mimo Radar ◽  
Radar System ◽  
Sensor Scheduling ◽  
Multi Target Tracking

scholarly journals Sensor Scheduling and Resource Allocation in Distributed MIMO Radar for Joint Target Tracking and Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 62387-62400 ◽  
Author(s):  
Haowei Zhang ◽  
Junwei Xie ◽  
Junpeng Shi ◽  
Zhaojian Zhang ◽  
Xiaolong Fu
Keyword(s):  
Resource Allocation ◽  
Target Tracking ◽  
Mimo Radar ◽  
Sensor Scheduling

scholarly journals Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

2021 ◽  
Author(s):  
Khaireddine Zarai ◽  
Adnan Cherif
Keyword(s):  
Monte Carlo ◽  
State Estimation ◽  
Target Tracking ◽  
Data Association ◽  
Mimo Radar ◽  
Radar System ◽  
Complex Data ◽  
Interference Phenomenon ◽  
Experimental Database ◽  
Multi Target Tracking

This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework.

Bi-Station Multi-Target Tracking Using Range and Doppler Measurements

2013 ◽  
Vol 329 ◽  
pp. 338-343
Author(s):  
Tian Jiao Fu ◽  
Li Guo Zhang ◽  
Jian Yue Ren
Keyword(s):  
Target Tracking ◽  
High Frequency ◽  
Radar System ◽  
Tracking Algorithm ◽  
Range Finding ◽  
Wave Radar ◽  
Multi Target Tracking ◽  
Actual Environment ◽  
Simulation Results ◽  
Ground Wave

The azimuthal measurements of the high frequency ground wave radar are poor in an actual environment, which can cause the plots highly decentralized and damage the formation of the over-the-horizon tracks. To solve the problem, a new radar system is proposed to triangulate target tracks using range and Doppler measurements only. On the basis of the analysis of the characteristics of the range-finding location, a multi-target tracking algorithm under non-clutter condition is given in this paper, which further improves the tracking algorithm of this system. Simulation results show the effectiveness of this method.

scholarly journals Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 981 ◽  
Author(s):  
Qinghua Han ◽  
Minghai Pan ◽  
Weijun Long ◽  
Zhiheng Liang ◽  
Chenggang Shan
Keyword(s):  
Resource Allocation ◽  
Target Tracking ◽  
Power Allocation ◽  
Adaptive Sampling ◽  
Sampling Interval ◽  
Radar System ◽  
Chance Constraint ◽  
Maneuvering Target Tracking ◽  
Time Step ◽  
Maneuvering Target

In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.

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