Generation of Orthogonal Discrete Frequency Coded Waveform Using Accelerated Particle Swarm Optimization Algorithm for MIMO Radar
Design of orthogonal code sets with correlation properties can effectively improve the radar performance by transmitting specially designed orthogonal Multiple Input Multiple Output (MIMO) radar. A novel particle swarm algorithm is proposed to numerically design orthogonal Discrete Frequency Waveforms and Modified Discrete Frequency Waveforms (DFCWs) with good correlation properties for MIMO radar. We employ Accelerated Particle Swarm Optimization algorithm (ACC_PSO), Particles of a swarm communicate good positions, velocity and accelerations to each other as well as dynamically adjust their own position, velocity and acceleration derived from the best of all particles. The simulation results show that the proposed algorithm is effective for the design of DFCWs signal used in MIMO radar.
KeywordsMultiple Input and Multiple Output (MIMO) Radar Discrete Frequency Coded waveform (DFCW) Accelerated Particle Swarm Optimization Algorithm (ACC_PSO)
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- 1.Fishler, E., Haimovich, A., Blum, R., Cimini, L., Chizhik, D., Valenzuela, R.: MIMO radar: An idea whose time has come. In: Proceedings of IEEE International Radar Conference, Philadelphia, PA (April 2004)Google Scholar
- 2.Liu, B., He, Z., Li, J.: Mitigation of Autocorrelation sidelobe peaks of Orthogonal Discrete Frequency-Coding waveform for MIMO Radar. In: Proceedings of IEEE Radar Conference, China, Chengdu, pp. 1–6 (2008)Google Scholar
- 4.Liu, B., He, Z.: Orthogonal Discrete Frequency-Coding waveform for MIMO Radar. Spinger Link Journal of Electronics (China) 25(4) (July 2008)Google Scholar
- 5.Kobayashi, T., Nakagawa, K., Imae, J., Zhai, G.: Real Time Object tracking on Video Image Sequence using Particle Swarm Optimization. In: International Conference on Control, Automation and Systems, Seoul, Korea, pp. 1773–1778 (2007)Google Scholar
- 6.Deng, Y., Tong, H.: Dynamic Shortest path in stochastic Traffic networks baed on fluid neural network and particle swarm optimization. In: Internal Conference on Natural Computation (ICNC), Yantai Shandong, pp. 2325–2329 (2010)Google Scholar