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

The resource-aware design is of great importance for the distributed multiple-input multiple-output (MIMO) radar in military applications, where multiple missions need to be fully and simultaneously performed constrained by the resource budget. Aiming at the joint of tracking existing targets and detecting new threats, a sensor scheduling integrated with power and bandwidth allocation strategy is put forward. The predicted posterior Cramer-Rao lower bound (PCRLB) in the worst case and the probability of detection are integrated as the optimization metric. Since such a problem is NP-hard, a modified particle swarm optimization (MPSO) for the sensor selection; embed with the greedy idea for the power and bandwidth allocation, is proposed for the solution exploration. The numerical simulations demonstrate that the MPSO is capable of providing close performance to the exhaustive search based method. More importantly, it possesses a lower computational burden and achieves better results compared with multi-start local search (MSLS)-based method.

[1]  Lajos Hanzo,et al.  Decomposition Optimization Algorithms for Distributed Radar Systems , 2016, IEEE Transactions on Signal Processing.

[2]  Junpeng Shi,et al.  Optimization model and online task interleaving scheduling algorithm for MIMO radar , 2019, Comput. Ind. Eng..

[3]  Alexander M. Haimovich,et al.  Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance , 2010, IEEE Transactions on Signal Processing.

[4]  Alexander M. Haimovich,et al.  Target Localization Accuracy Gain in MIMO Radar-Based Systems , 2008, IEEE Transactions on Information Theory.

[5]  Mojtaba Radmard,et al.  Antenna placement and power allocation optimization in MIMO detection , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Wei Yi,et al.  Joint Node Selection and Power Allocation Strategy for Multitarget Tracking in Decentralized Radar Networks , 2018, IEEE Transactions on Signal Processing.

[7]  Kristine L. Bell,et al.  Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking , 2007 .

[8]  Zheng Bao,et al.  Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking , 2015, IEEE Transactions on Signal Processing.

[9]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[10]  Alexander M. Haimovich,et al.  Spatial Diversity in Radars—Models and Detection Performance , 2006, IEEE Transactions on Signal Processing.

[11]  Jun Zhang,et al.  Dynamic waveform design for target tracking using MIMO radar , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[12]  S. Haykin,et al.  Cognitive radar: a way of the future , 2006, IEEE Signal Processing Magazine.

[13]  Alexander M. Haimovich,et al.  Target Velocity Estimation and Antenna Placement for MIMO Radar With Widely Separated Antennas , 2010, IEEE Journal of Selected Topics in Signal Processing.

[14]  Edward Ott,et al.  Controlling chaos , 2006, Scholarpedia.

[15]  Lei Shao,et al.  A hybrid DPSO with Levy flight for scheduling MIMO radar tasks , 2018, Appl. Soft Comput..

[16]  Jun-wei Xie,et al.  An Entropy-based PSO for DAR task scheduling problem , 2018, Appl. Soft Comput..

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Steven Li,et al.  Improved global-best-guided particle swarm optimization with learning operation for global optimization problems , 2017, Appl. Soft Comput..

[19]  H. Vincent Poor,et al.  Resource allocation schemes for target localization in distributed multiple radar architectures , 2010, 2010 18th European Signal Processing Conference.

[20]  Bin Sun,et al.  Cooperative Game Approach to Power Allocation for Target Tracking in Distributed MIMO Radar Sensor Networks , 2015, IEEE Sensors Journal.

[21]  Sheng Chuan,et al.  Scheduling method for phased array radar over chaos adaptively genetic algorithm , 2016, 2016 Sixth International Conference on Information Science and Technology (ICIST).

[22]  Simon Haykin,et al.  Control theoretic approach to tracking radar: First step towards cognition , 2011, Digit. Signal Process..

[23]  Moe Z. Win,et al.  Optimal power allocation for active and passive localization , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[24]  Marco Lops,et al.  Resource allocation in radar networks for non-coherent localization , 2012 .

[25]  Xiyu Song,et al.  Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems , 2017 .

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[27]  H. Vincent Poor,et al.  A combinatorial optimization framework for subset selection in distributed multiple-radar architectures , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[28]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[29]  H. Vincent Poor,et al.  Power allocation schemes for target localization in widely distributed MIMO radar systems , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[30]  Siba Sankar Mahapatra,et al.  A quantum behaved particle swarm optimization for flexible job shop scheduling , 2016, Comput. Ind. Eng..

[31]  Rubén González Crespo,et al.  MOVPSO: Vortex Multi-Objective Particle Swarm Optimization , 2017, Appl. Soft Comput..

[32]  Alexander M. Haimovich,et al.  Resource Allocation in MIMO Radar With Multiple Targets for Non-Coherent Localization , 2013, IEEE Transactions on Signal Processing.

[33]  Jingzhi Zhang,et al.  Power allocation for multiple targets range-only tracking in widely distributed MIMO radar systems , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[34]  Junpeng Shi,et al.  Joint beam and waveform selection for the MIMO radar target tracking , 2019, Signal Process..

[35]  H. Vincent Poor,et al.  Sensor Selection in Distributed Multiple-Radar Architectures for Localization: A Knapsack Problem Formulation , 2012, IEEE Transactions on Signal Processing.

[36]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[37]  Jun-wei Xie,et al.  A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar , 2017, Frontiers of Information Technology & Electronic Engineering.

[38]  L.J. Cimini,et al.  MIMO Radar with Widely Separated Antennas , 2008, IEEE Signal Processing Magazine.

[39]  Alexander M. Haimovich,et al.  Cramer Rao bound on target localization estimation in MIMO radar systems , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[40]  Phani Chavali,et al.  Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking , 2012, IEEE Transactions on Signal Processing.

[41]  L.M. Kaplan,et al.  Global node selection for localization in a distributed sensor network , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[42]  Junwei Xie,et al.  Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking , 2018, IEEE Access.

[43]  Jun-wei Xie,et al.  A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar , 2019, Eur. J. Oper. Res..

[44]  Alexander M. Haimovich,et al.  Target tracking in MIMO radar systems: Techniques and performance analysis , 2010, 2010 IEEE Radar Conference.

[45]  Cheng Wang,et al.  A novel improved particle swarm optimization algorithm based on individual difference evolution , 2017, Appl. Soft Comput..

[46]  H. Vincent Poor,et al.  Cluster allocation schemes for target tracking in multiple radar architecture , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[47]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[48]  Hsing-Chih Tsai,et al.  Unified particle swarm delivers high efficiency to particle swarm optimization , 2017, Appl. Soft Comput..

[49]  Geert Leus,et al.  Sparsity-Aware Sensor Selection: Centralized and Distributed Algorithms , 2014, IEEE Signal Processing Letters.

[50]  H. Vincent Poor,et al.  Power Allocation Strategies for Target Localization in Distributed Multiple-Radar Architectures , 2011, IEEE Transactions on Signal Processing.