The Parameter Estimation of Non-Cooperative Frequency Hopping Signals Based on the Algorithm of RSPWVD

How to get good time-frequency distribution of FH signals is crucial to detect and to trace the FH signals. The theory about the rearrangement of time-frequency distribution is summarized in the paper, then the basic principle of detecting the signals using the algorithm of rearrangement of the smooth pseudo Wigner-Ville distribution (RSPWVD) is analyzed in detail, and the algorithm expression are given at the same time. The analysis results show that the RSPWVD algorithm not only has more ideal anti-jamming effects, but also can enhance the time-frequency aggregation of the signal, so the signal parameters are estimated more accurately. In the end, the computer simulation is shown to test the feasibility and effectiveness of the algorithm.

Download Full-text

Parameter Estimation of Multi Frequency Hopping Signals Based on Space-Time-Frequency Distribution

Symmetry ◽

10.3390/sym11050648 ◽

2019 ◽

Vol 11 (5) ◽

pp. 648 ◽

Cited By ~ 2

Author(s):

Jian Wan ◽

Dianfei Zhang ◽

Wei Xu ◽

Qiang Guo

Keyword(s):

Parameter Estimation ◽

Frequency Distribution ◽

Signal To Noise Ratio ◽

Frequency Hopping ◽

Space Time ◽

Signal To Noise ◽

Joint Diagonalization ◽

Time Frequency ◽

Noise Ratio ◽

Time Frequency Distribution

Frequency hopping spread spectrum (FHSS) communication is widely used in military and civil communication, and the parameter estimation of frequency hopping (HF) signals is of great significance. In order to estimate the parameters of multiple frequency hopping signals effectively, a blind parameter estimation algorithm based on space-time frequency analysis (STFA) and matrix joint diagonalization (JDM) is proposed. Firstly, the time domain signal received by the linear array is converted to the space-time frequency domain through the space-time frequency transformation, and the space-time frequency distribution (STFD) of the signal is obtained. Then the time-frequency point is extracted from the space-time frequency distribution map, the extraction of the hop is completed by the method of finding an “island”, and the space-time frequency matrix of each hop is constructed, and then the preliminary estimation of each jump frequency, jump time and jump period is completed. Finally, the space-time-frequency matrix of the same hop received by different array elements is jointly diagonalized by the matrix joint diagonalization algorithm, and the diagonalization matrix is obtained. On the basis of the diagonalization matrix, the root-MUSIC algorithm is used to complete the direction of arrival (DOA) estimation of the frequency hopping signal and the separation of the frequency hopping radio. The simulation results show that the proposed algorithm is effective in parameter estimation of multi-hopping signals. It can estimate the parameters of −4 dB signal-to-noise ratio (SNR). The accuracy rate of parameter (hop period, DOA, hop start time, hop end time, frequency hopping frequency set) estimation reaches 73.26%, and the sparse linear regression (SLR) algorithm reaches 70.15%. When the signal-to-noise ratio reaches 5 dB, the accuracy of estimation can reach 94.74%, and the SLR reach 85.64%. It has a good effect on parameter estimation of multi-hopping signals.

Download Full-text

The analysis of frequency-hopping signal acquisition based on Cohen-reassignment joint time-frequency distribution

Asia-Pacific Conference on Environmental Electromagnetics, 2003. CEEM 2003. Proceedings. ◽

10.1109/ceem.2003.238474 ◽

2003 ◽

Cited By ~ 2

Author(s):

Wang Danzhi ◽

Li Shujian ◽

Shao Dingrong

Keyword(s):

Frequency Distribution ◽

Frequency Hopping ◽

Signal Acquisition ◽

Time Frequency ◽

Time Frequency Distribution

Download Full-text

A parameter estimation method for polyphase codes using time-frequency distribution

The 2nd International Conference on Information Science and Engineering ◽

10.1109/icise.2010.5690460 ◽

2010 ◽

Author(s):

Xu Wei ◽

Chen Mao ◽

Zhang Guan Jie

Keyword(s):

Parameter Estimation ◽

Frequency Distribution ◽

Estimation Method ◽

Time Frequency ◽

Parameter Estimation Method ◽

Time Frequency Distribution

Download Full-text

Adaptive radial sinc kernel distribution and its application in mechanical fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215616985 ◽

2016 ◽

Vol 231 (3) ◽

pp. 485-493 ◽

Cited By ~ 2

Author(s):

Zhinong Li ◽

Ming Zhu ◽

Fulei Chu ◽

Xuping He

Keyword(s):

Fault Diagnosis ◽

Frequency Distribution ◽

Kernel Function ◽

Rolling Bearing ◽

Kernel Functions ◽

Frequency Resolution ◽

Good Time ◽

Time Frequency ◽

Fixed Kernel ◽

Time Frequency Distribution

Based on the deficiency of fixed-kernel in the traditional time–frequency distribution, which is lack of adaptability, a new adaptive kernel function, which is named as the adaptive radial sinc kernel, is proposed according to design criteria of adaptive optimal kernel. The definition and algorithm of radial sinc kernel are given, and the proposed method is compared with the tradition time–frequency distribution. The simulation results show that the proposed method is superior to the traditional fixed-kernel functions, such as Wigner–Ville distribution, Choi–Williams distribution, cone-kernel distribution and continuous wavelet transform. The adaptive radial sinc kernel can overcome the deficiency of fixed-kernel function in traditional time–frequency distribution, adopt the optimizing method to filter the cross-terms adaptively according to the signal distribution, obtain good time–frequency resolution and has extensive adaptability for an arbitrary signal. Finally, the proposed method has been applied to the fault diagnosis of rolling bearing, and the experiment result shows that the proposed method is very effective.

Download Full-text