Research on Parameters Estimation of Signals Based on Fractal-Box Dimension
- 14 Downloads
Fractal theory is a new scientific method and theory that can describe the complexity and irregularity of nature. Aiming at the problem that the frequency modulation slope obtained by the traditional linear frequency modulation (LFM) signal parameter estimation algorithm has high complexity with poor real-time performance and small SNR adaptation range, the LFM signal frequency modulation slope estimation method based on the fractal-box dimension is proposed. The proposed method is utilized to evaluate the frequency modulation slope of the LFM signal, and the affect of signal amplitude and phase on the fractal-box dimension of the signal is taken into account. The estimation error at different SNRs is analyzed, and the relationship graph of the pulse width, FM bandwidth and fractal-box dimension is drawn. The simulation results demonstrate that the proposed method can accurately estimate the parameters of LFM signals under varying SNR environment. Compared with the traditional linear frequency modulation (LFM) signal parameter estimation algorithms, the anti-noise performance of the proposed algorithm is stronger, and the proposed algorithm is relatively simple and has good application value.
KeywordsChirp signal Parameter estimation Fractal-box dimension Frequency modulation slope
The authors would like to thank State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Director Fund (CEMEE2019Z0105B).
- 4.Jia M, Gao Z, Guo Q et al (2019) Sparse Feature Learning for Correlation Filter Tracking Toward 5G-Enabled Tactile Internet. IEEE Transactions on Industrial Informatics 1:1): 1–1): 1Google Scholar
- 6.Tu Y, Lin Y, Wang J et al (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Contin 55(2):243–254Google Scholar
- 8.Liu S, Pan Z, Cheng X (2017) A Novel Fast Fractal Image Compression Method based on Distance Clustering in High Dimensional Sphere Surface. Fractals 25(4):174000Google Scholar
- 10.Dou Z, Si G, Lin Y et al (2019) An Adaptive Resource Allocation Model with Anti-jamming in IoT Network. IEEE Access 1:1): 1–1): 1Google Scholar
- 14.Yin B, Zhou S, Zhang S, Gu K, Yu F (2017) On Efficient Processing of Continuous Reverse Skyline Queries in Wireless Sensor Networks. TIIS 11(4):1931–1953Google Scholar
- 15.Wang J, Ju C, Gao Y et al (2018) A PSO based energy efficient coverage control algorithm for wireless sensor networks. Comput Mater Contin 56(3):433–446Google Scholar
- 17.Jiang S, Fu C, Wu Z (2010) Intelligent Data-Fusion Model Using Correlation Fractal Dimension for Structural Damage Identification. Smart Materials and Intelligent Systems 10(143-144):1300–1304Google Scholar
- 20.Lin Y, Zhu X, Zheng Z et al (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput 75(6):1–18Google Scholar