Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function

Biyun MA; Dapeng YUAN; Jiaojiao LIU

doi:10.11999/JEIT200044

Volume 43 Issue 5

May 2021

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Biyun MA, Dapeng YUAN, Jiaojiao LIU. Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044

Citation:

Biyun MA, Dapeng YUAN, Jiaojiao LIU. Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044

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Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function

doi: 10.11999/JEIT200044

Biyun MA^{1, 2},
Dapeng YUAN¹,
Jiaojiao LIU^{1
,
,}

1.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
2.
Key Laboratory of Marine Environment Survey Technology and Application, Ministry of Natural Resource, Guangzhou 510300, China

Funds: The Key Program of Marine Economy Development, Department of Natural Resource of Guangdong Province under Grant (YZRZH[2020]009), The National Natural Science Foundation of China (61302056, 61401158), The Basic Research Business Expenses of Central Universities of South China University of Technology (2017MS047)

Received Date: 2020-01-13
Rev Recd Date: 2020-08-01

Available Online: 2020-08-24

Publish Date: 2021-05-18

Abstract

Abstract

Compared with Linear Frequency Modulation (LFM) signals, Hyperbolic Frequency Modulation (HFM) signals, which have good performance of the pulse compression and the Doppler invariance, are widely used in scenes with severe Doppler effects such as radar detection and underwater acoustic detection, and among them, the parameter estimation problem of HFM signals is particularly important. In view of this, this paper proposes a Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function. Firstly, the Cramer-Rao lower bound of the HFM signal is derived as the performance evaluation standard for parameter estimation; Then based on the Gaussian random noise, the likelihood function of the HFM signal is constructed, and an improved fitness function is proposed in combination with the characteristics of data vectorization, then the Global best guided Artificial Bee Colony (GABC) algorithm is used to optimize the fitness function to realize the parameter estimation of the HFM signal. Finally, Monte Carlo simulation results show that, compared to the method before the improvement, the mean square error of the parameter estimation result of the HFM signal is closer to the Cramer-Rao lower bound when the signal-to-noise ratio is more the 3 dB, and the amount of calculation is about one-third of the method before improvement, which improves the algorithm convergence speed while ensuring the estimation accuracy.
- Signal processing,
- Hyperbolic Frequency Modulation(HFM),
- Parameter estimation,
- Likelihood function

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References(14)

References

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