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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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PSO-optimized Pareto and Nash equilibrium gaming-based power allocation technique for multistatic radar network

  • Received : 2019.10.25
  • Accepted : 2020.04.13
  • Published : 2021.02.01
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Abstract

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.

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References

  1. A. Deligiannis, S. Lambotharan, and J. A. Chambers, Game theoretic analysis for MIMO radars with multiple targets, IEEE Trans. Aerosp. Electron. Syst. 52 (2016), no. 6, 2760-2774. https://doi.org/10.1109/TAES.2016.150699
  2. J. Yan et al., Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system, IEEE Trans. Signal Process. 66 (2018), no. 15, 3946-3957. https://doi.org/10.1109/TSP.2018.2841865
  3. X. Wang et al., An approach to the modulation recognition of MIMO radar signals, EURASIP J. Wirel. Commun. Netw. 2013 (2013), no. 66.
  4. H. Gao et al., Antenna allocation in MIMO radar with widely separated antennas for multi-target detection, Sensors 14 (2014), no. 11, 20165-20187. https://doi.org/10.3390/s141120165
  5. S. Gogineni and A. Nehorai, Game theoretic design for polarimetric MIMO radar target detection, Signal Process. 92 (2012), no. 5, 1281-1289. https://doi.org/10.1016/j.sigpro.2011.11.024
  6. D. Anastasios et al., Game-theoretic power allocation and the Nash equilibrium analysis for a multistatic MIMO radar network, IEEE Trans. Signal Process. 65 (2017), no. 24, 6397-6408. https://doi.org/10.1109/TSP.2017.2755591
  7. X. Song et al., The MIMO radar and jammer games, IEEE Trans. Signal Process. 60 (2012), no. 2, 687-699. https://doi.org/10.1109/TSP.2011.2169251
  8. M. Piezzo et al., Non-cooperative code design in radar networks: a game-theoretic approach, EURASIP J. Adv. Signal Process. 63 (2013), no. 1.
  9. G. Bacci et al., A game-theoretic approach for energy-efficient detection in radar sensor networks, in Proc. IEEE Sensor Array Multichannel Signal Process. Workshop (Hoboken, NJ, USA), June 2012, pp. 157-160.
  10. F. Zhao et al., Game-theoretic beamforming and power allocation in MIMO cognitive radio systems with transmitter antenna correlation, Mobile Inf. Syst. 2015 (2015), 1-7.
  11. H. Chen, S. Ta, and B. Sun, Cooperative game approach to power allocation for target tracking in distributed MIMO radar sensor networks, IEEE Sens. J. 15 (2015), no. 10, 5423-5432. https://doi.org/10.1109/JSEN.2015.2431261
  12. A. Ajorloo, A. Amini, and M. H. Bastani, A compressive sensing-based colocated MIMO radar power allocation and waveform design, IEEE Sens. J. 18 (2018), no. 22, 9420-9429. https://doi.org/10.1109/jsen.2018.2871214
  13. N. Garcia et al., Resource allocation in MIMO radar with multiple targets for non-coherent localization, IEEE Trans. Signal Process. 62 (2014), no. 10, 2656-2666. https://doi.org/10.1109/TSP.2014.2315169
  14. J. Yan et al., Joint beam selection and power allocation for multiple target tracking in netted colocated MIMO radar system, IEEE Trans. Signal Process. 64 (2016), no. 24, 6417-6427. https://doi.org/10.1109/TSP.2016.2607147
  15. Y. Yu et al., Power allocation and waveform design for the compressive sensing based MIMO radar, IEEE Trans. Aerosp. Electron. Syst. 50 (2014), no. 2, 898-909. https://doi.org/10.1109/taes.2014.130088
  16. L. Wang et al., Jamming power allocation strategy for MIMO radar based on MMSE and mutual information, IET Radar Sonar Nav. 11 (2017), no. 7, 1081-1089. https://doi.org/10.1049/iet-rsn.2016.0356
  17. B. Ma et al., A joint scheme of antenna selection and power allocation for localization in MIMO radar sensor networks, IEEE Commun. Lett. 18 (2014), no. 12, 2225-2228. https://doi.org/10.1109/LCOMM.2014.2365206
  18. X. Song et al., A joint resource allocation method for multiple targets tracking in distributed MIMO radar systems, EURASIP J. Adv. Signal Process. 65 (2018).
  19. L. Xing et al., MIMO radar and target Stackelberg game in the presence of clutter, IEEE Sens. J. 15 (2015), no. 12, 6912-6920. https://doi.org/10.1109/JSEN.2015.2466812
  20. A. Panoui, S. Lambotharan, and J. A. Chambers, Game theoretic distributed waveform design for multistatic radar networks, IEEE Trans. Aerosp. Electron. Syst. 52 (2016), no. 4, 1855-1865. https://doi.org/10.1109/TAES.2016.150378
  21. C. Shi et al., Low probability of intercept-based optimal power allocation scheme for an integrated multistatic radar and communication system, IEEE Syst. J. 14 (2019), no. 1, 983-994. https://doi.org/10.1109/jsyst.2019.2931754
  22. C. Shi et al., Joint subcarrier assignment and power allocation strategy for integrated radar and communications system based on power minimization, IEEE Sens. J. 19 (2019), no. 23, 11167-11179. https://doi.org/10.1109/jsen.2019.2935760
  23. C. Shi et al., Nash bargaining game-theoretic framework for power control in distributed multiple-radar architecture underlying wireless communication system, Entropy 20 (2018), 267. https://doi.org/10.3390/e20040267
  24. C. Shi et al., Power control scheme for spectral coexisting multistatic radar and massive MIMO communication systems under uncertainties: A robust Stackelberg game model, Digit. Signal Process. 94 (2019), 146-155. https://doi.org/10.1016/j.dsp.2019.05.007
  25. C. Shi et al., Non-cooperative game theoretic power allocation strategy for distributed multiple-radar architecture in a spectrum sharing environment, IEEE Access 6 (2018), 17787-17800. https://doi.org/10.1109/access.2018.2817625
  26. D. Mishra and G. C. Alexandropoulos, Jointly optimal spatial channel assignment and power allocation for MIMO SWIPT systems, IEEE Wirel. Commun. Lett. 7 (2018), no. 2, 214-217. https://doi.org/10.1109/lwc.2017.2765320
  27. S. K. Injeti, A Pareto optimal approach for allocation of distributed generators in radial distribution systems using improved differential search algorithm, J. Electr. Syst. Inf. Technol. 5 (2017), no. 3, 908-927. https://doi.org/10.1016/j.jesit.2016.12.006
  28. D. Wu, Y. Cai, and M. Guizani, Auction-based relay power allocation: Pareto optimality, fairness, and convergence, IEEE Trans. Commun. 62 (2014), no. 7, 2249-2259. https://doi.org/10.1109/tcomm.2014.2331072
  29. T. Harikala and R. V. S. Satyanarayana, Power efficient technique for MIMO radar using co-operative and non-co-operative game theory in wireless applications, Int. J. Recent Technol. Eng. 7 (2019), 273-278.