Parameter Estimation of Polynomial Phase Signal by Unscented Kalman Filtering

Abstract:

The parameter estimation of the Polynomial Phase Signals (PPS) is one of the core issues. In this paper, UKF-based algorithm is proposed to estimate the parameter of PPS embedded in Gaussian noise. The algorithm constructs an adequate state-space model to represent the PPS and the model can also be implied in real radar signal. Unscented Kalman filtering is applied to estimate the signal parameters. The method achieves the lower SNR threshold, the faster convergence speed, the higher accuracy and more stable estimation performance compared with the existing methods. Simulation also verifies the efficiency of the proposed method.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 4253-4256
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.4253
• Online since: September 2014
• Authors: Wan Ge Li*, Jin Feng Hu, Hui Ai, Zhi Rong Lin, Ya Xuan Zhang
• Keywords: Parameter Estimation, Polynomial Phase Signal, State-Space Model, Unscented Kalman Filter

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