Genetic algorithm and M-estimator based robust sequential estimation of parameters of nonlinear sinusoidal signals |
| |
| Institution: | 1. Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai 201620, China;2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton AB T6G 2G6, Canada;1. School of Mathematical Science, University of the Chinese Academy of Sciences, Beijing 100049, China;2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China;3. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA;4. Department of Genetics, University of North Carolina at Chapel Hill, NC 27599, USA;5. Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599, USA;6. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, NC 27599, USA;7. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC 27599, USA;1. Department of Information Engineering, I-Shou University, Kaohsiung 84001, Taiwan;2. Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan;3. Novatek Microelectronics Corp., Hsinchu Science Park, Hsinchu 30076, Taiwan |
| |
| Abstract: | Estimation of parameters of nonlinear superimposed sinusoidal signals is an important problem in digital signal processing. In this paper, we consider the problem of estimation of parameters of real valued sinusoidal signals. We propose a real-coded genetic algorithm based robust sequential estimation procedure for estimation of signal parameters. The proposed sequential method is based on elitist generational genetic algorithm and robust M-estimation techniques. The method is particularly useful when there is a large number of superimposed sinusoidal components present in the observed signal and is robust with respect to presence of outliers in the data and impulsive heavy tail noise distributions. Simulations studies and real life signal analysis are performed to ascertain the performance of the proposed sequential procedure. It is observed that the proposed methods perform better than the usual non-robust methods of estimation. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
