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| Citation: | ZHU Peikun, LIANG Jing, LUO Zihan, et al. Waveform selection method of cognitive radar target tracking based on reinforcement learning[J]. Journal of Radars, 2023, 12(2): 412–424. doi: 10.12000/JR22239
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Waveform Selection Method of Cognitive Radar Target Tracking Based on Reinforcement Learning
doi: 10.12000/JR22239
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Abstract
Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience, cognitive radar continuously adjusts its waveform, parameters, and illumination strategies to achieve robust target tracking in complex and changing scenarios. Its waveform design has been receiving attention to improve tracking performance. In this paper, we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets. The framework considers the combination of Constant Velocity (CV), Constant Acceleration (CA), and Coordinate Turn (CT) motions. We also design Criterion-Based Optimization (CBO) and Entropy Reward Q-Learning (ERQL) methods to perform waveform selection based on this framework. To provide the optimum target tracking performance, it merges the radar and target into a closed loop, updating the broadcast waveform in real-time as the target state changes. The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO, according to numerical results. The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach. -
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References
[1] YUAN Ye, YI Wei, HOSEINNEZHAD R, et al. Robust power allocation for resource-aware multi-target tracking with colocated MIMO radars[J]. IEEE Transactions on Signal Processing, 2021, 69: 443–458. doi: 10.1109/TSP.2020.3047519[2] SUN Zhichao, YEN G G, WU Junjie, et al. Mission planning for energy-efficient passive UAV radar imaging system based on substage division collaborative search[J]. IEEE Transactions on Cybernetics, 2023, 53(1): 275–288. doi: 10.1109/TCYB.2021.3090662[3] LIANG Jing and LIANG Qilian. Design and analysis of distributed radar sensor networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2011, 22(11): 1926–1933. doi: 10.1109/TPDS.2011.45[4] HAYKIN S. Cognitive radar: A way of the future[J]. IEEE Signal Processing Magazine, 2006, 23(1): 30–40. doi: 10.1109/MSP.2006.1593335[5] LUO Zihan, LIANG Jing, and XU Zekai. Intelligent waveform optimization for target tracking in radar sensor networks[C]. 10th International Conference on Communications, Signal Processing, and Systems (CSPS), Changbaishan, China, 2021: 165–172.[6] HAYKIN S. Cognition is the key to the next generation of radar systems[C]. 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, Marco Island, USA, 2009: 463–467.[7] HAYKIN S, ZIA A, ARASARATNAM I, et al. Cognitive tracking radar[C]. 2010 IEEE Radar Conference, Arlington, USA, 2010: 1467–1470.[8] GUERCI J R. Cognitive radar: A knowledge-aided fully adaptive approach[C]. 2010 IEEE Radar Conference, Arlington, USA, 2010: 1365–1370.[9] GUERCI J R, GUERCI R M, RANAGASWAMY M, et al. CoFAR: Cognitive fully adaptive radar[C]. 2014 IEEE Radar Conference, Cincinnati, USA, 2014: 984–989.[10] GUERCI J R. Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach[M]. 2nd ed. Norwood, USA: Artech House, 2020.[11] BELL K L, BAKER C J, SMITH G E, et al. Cognitive radar framework for target detection and tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1427–1439. doi: 10.1109/JSTSP.2015.2465304[12] SMITH G E, CAMMENGA Z, MITCHELL A, et al. Experiments with cognitive radar[C]. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, 2015: 293–296.[13] ZHANG Lingzhao and JIANG Min. Cognitive radar target tracking algorithm based on waveform selection[C]. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021: 1506–1510.[14] HULEIHEL W, TABRIKIAN J, and SHAVIT R. Optimal adaptive waveform design for cognitive MIMO radar[J]. IEEE Transactions on Signal Processing, 2013, 61(20): 5075–5089. doi: 10.1109/TSP.2013.2269045[15] ALDAYEL O, MONGA V, and RANGASWAMY M. Successive QCQP refinement for MIMO radar waveform design under practical constraints[J]. IEEE Transactions on Signal Processing, 2016, 64(14): 3760–3774. doi: 10.1109/TSP.2016.2552501[16] FENG Shuo and HAYKIN S. Cognitive risk control for transmit-waveform selection in vehicular radar systems[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10): 9542–9556. doi: 10.1109/TVT.2018.2857718[17] SAVAGE C O and MORAN B. Waveform selection for maneuvering targets within an IMM framework[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3): 1205–1214. doi: 10.1109/TAES.2007.4383612[18] CLEMENTE C, SHOROKHOV I, PROUDLER I, et al. Radar waveform libraries using fractional Fourier transform[C]. 2014 IEEE Radar Conference, Cincinnati, USA, 2014: 855–858.[19] ZHAO Dehua, WEI Yinsheng, and LIU Yongtan. Real-time waveform adaption in spectral crowed environment using a sub-waveforms-based library[C]. 2016 CIE International Conference on Radar, Guangzhou, China, 2016: 1–5.[20] NGUYEN N H, DOGANCAY K, and DAVIS L M. Adaptive waveform selection for multistatic target tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 688–701. doi: 10.1109/TAES.2014.130723[21] ROMAN J. R., GARNHAM J. W. and ANTONIK P., Information Theoretic Criterion for Waveform Selection. Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006., Waltham, MA, USA, 2006, 444-448, doi: 10.1109/SAM.2006.1706172.[22] CAO Xin, ZHENG Zhe, and AN Di. Adaptive waveform selection algorithm based on reinforcement learning for cognitive radar[C]. 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China, 2019: 208–213.[23] HAN Bo, HUANG Hanqiao, LEI Lei, et al. An improved IMM algorithm based on STSRCKF for maneuvering target tracking[J]. IEEE Access, 2019, 7: 57795–57804. doi: 10.1109/ACCESS.2019.2912983[24] BLACKMAN S S, DEMPSTER R J, BUSCH M T, et al. IMM/MHT solution to radar benchmark tracking problem[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 730–738. doi: 10.1109/7.766953[25] KERSHAW D J and EVANS R J. Optimal waveform selection for tracking systems[J]. IEEE Transactions on Information Theory, 1994, 40(5): 1536–1550. doi: 10.1109/18.333866[26] SIRA S P, PAPANDREOU-SUPPAPPOLA A, and MORRELL D. Advances in Waveform-Agile Sensing for Tracking[M]. Cham: Springer, 2009: 59–60.[27] WILLIAMS J L. Information theoretic sensor management[D]. [Ph. D. dissertation], Massachusetts Institute of Technology, 2007: 41–42.[28] ATHANS M and TSE E. A direct derivation of the optimal linear filter using the maximum principle[J]. IEEE Transactions on Automatic Control, 1967, 12(6): 690–698. doi: 10.1109/TAC.1967.1098732[29] THORNTON C E, KOZY M A, BUEHRER R M, et al. Deep reinforcement learning control for radar detection and tracking in congested spectral environments[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(4): 1335–1349. doi: 10.1109/TCCN.2020.3019605[30] WANG Qing, QIAO Yanming, and GAO Lirong. A cognitive radar waveform optimization approach based on deep reinforcement learning[C]. 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China, 2019: 1–6. -
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- Figure 1. Cognitive radar waveform selection framework
- Figure 2. IMM flow chart based on CV, CA and CT models
- Figure 3. Waveform selection block diagram
- Figure 4. Trajectory of maneuvering target
- Figure 5. Probability of each motion model being selected in different motion stages
- Figure 6. Target position tracking RMSE curve (X axis)
- Figure 7. Target velocity tracking RMSE curve (X axis)
- Figure 8. Target tracking pulse duration variation curve
- Figure 9. Target tracking frequency modulation slope variation curve
- Figure 10. Target tracking entropy state variation curve
- Figure 11. The average time-consuming results of each waveform parameter selection algorithm