Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations.

[1]  Azlan Mohd Zain,et al.  Overview of NSGA-II for Optimizing Machining Process Parameters , 2011 .

[2]  Junqing. Wu,et al.  Research on Phased Array Radar Resource Management in Searching Mode , 2016 .

[3]  Xin Chen,et al.  An intelligent optimization algorithm for joint MCS and resource block allocation in LTE femtocell downlink with QoS guarantees , 2014, The 2014 5th International Conference on Game Theory for Networks.

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

[5]  Qun Zhang,et al.  Measurement Matrix Optimization for ISAR Sparse Imaging Based on Genetic Algorithm , 2016, IEEE Geoscience and Remote Sensing Letters.

[6]  Jianfeng Tao,et al.  Research on Resource Management of Phased-Array Radar in Target Tracking , 2015 .

[7]  Abbas Alimohammadi,et al.  Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  K. Woodbridge,et al.  Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization , 2006, IEEE Signal Processing Magazine.

[9]  Wang Lei,et al.  Multifunction Phased Radar Resource Management via Maximal Pulse Interleaving Technique , 2013 .

[10]  Chris N. Potts,et al.  Scheduling for a multifunction phased array radar system , 1996 .

[11]  Han-Lim Choi,et al.  A time-window-based task scheduling approach for multi-function phased array radars , 2011, 2011 11th International Conference on Control, Automation and Systems.

[12]  Peng Cheng Li,et al.  A New Method of Power System Load Forecasting Based on Intelligent Optimization Algorithm , 2014 .

[13]  Qun Zhang,et al.  Micromotion Feature Extraction of Space Target Based on Track-Before-Detect , 2017, J. Sensors.

[14]  S. Bandyopadhyay,et al.  Solving multi-objective parallel machine scheduling problem by a modified NSGA-II , 2013 .

[15]  Qun Zhang,et al.  An Adaptive ISAR-Imaging-Considered Task Scheduling Algorithm for Multi-Function Phased Array Radars , 2015, IEEE Transactions on Signal Processing.

[16]  Ángel G. Andrade,et al.  Application of NSGA-II algorithm to the spectrum assignment problem in spectrum sharing networks , 2016, Appl. Soft Comput..

[17]  Qun Zhang,et al.  Micromotion feature extraction of radar target using tracking pulses with adaptive pulse repetition frequency adjustment , 2014 .