Method for estimating jump cycle and take-off time of frequency hopping signal

Abstract

The invention discloses a method for estimating the jump cycle and the take-off time of a frequency hopping signal. The method comprises the following steps: step one: obtaining a data sequence; step two: initializing parameters; step three: calculating the power spectrum of data in the i-th short-time window; step four: estimating the peak frequency and the peak-to-average power ratio of the data in the short-time window through the power spectrum; step five: judging whether the processing for the data of all short-time windows is completed, if the processing is not completed, returning to the third step, and if the processing is completed, returning to the sixth step; step six: estimating the initial moment of each jump of the frequency hopping signal; step seven: utilizing an alpha-TM algorithm to estimate the jump cycle and the take-off time. The method simultaneously utilizes the frequency characteristics and the energy features of the frequency hopping signal, and the robustness is good; through short-time Fourier transform, the calculated amount is small, the engineering practicability is high, and the method is suitable for quick and robust estimation of the parameters of the frequency hopping signal.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a method for estimating the hopping cycle and take-off time of a frequency hopping signal. Background technique [0002] Frequency hopping signals have good anti-interference, low intercept probability, anti-multipath and strong multi-access networking capabilities, and have been widely used in military and civilian communications. In recent years, they have also begun to be used in some underwater communications Occasions that require high confidentiality and reliability. Hop period and take-off time are two basic parameters to describe the frequency hopping signal. Under random background noise and non-cooperative conditions, the rapid and high-precision estimation of these two parameters is the premise to achieve the purpose of reconnaissance and interference to the frequency hopping system. [0003] At present, time-frequency analysis methods are mostly used for parameter e...

AI Technical Summary

Problems solved by technology

Wigner-Ville Distribution (Wigner-Ville Distribution, WVD) is a typical nonlinear time-frequency distribution with the highest theoretical time-frequency resolution. However, for multi-component signals such as frequency hopping signals, WVD has serious cross-term interference. make its application limited

In order to overcome the interference of WVD cross-terms, a large number of improved frequency-hopping signal parameter estimation algorithms based on WVD have emerged: For example, Zhao Jun et al. proposed a method using smoothed pseudo-WVD (Smoothed Pseudo WVD, SPWVD) to estimate frequency-hopping signals Parameters, this method can effectively suppress cross-interference terms, but this method is sensitive to noise, and the parameter estimation performance drops sharply under low Signal to Noise Ratio (SNR), and the calculation amount is very huge (Zhao Jun, Zhang Chaoyang, Lai Lifeng, etc. A method for blind estimation of frequency hopping signal parameters based on time-frequency analysis. Journal of Circuits and Systems, 2003, 8(3):46-50.); Feng Tao and Yuan Chaowei combined SPWVD with wavelet transform, this method Only the estimation method of the hopping period of the frequency hopping signal has been improved, and there is no essential difference with the method of Zhao Jun et al. ):2921-2925.); Guo Yi et al proposed a method to estimate frequency hopping signal parameters based on SPW time-frequency analysis. Compared with SPWVD, SPW has a certain advantage in calculation amount, but under low SNR, the estimation accuracy Greatly reduced (Guo Yi, Zhang Eryang, Shen Rongjun. Frequency-hopping signal time-frequency domain analysis and parameter blind estimation method. Signal Processing, 2007,23(2):210-213.); Chen proposed a parameter estimation based on rearrangement SPWVD method, which improves the focus of the time-frequency distribution, but the amount of calculation is greatly increased (Chen T C. Joint signal parameter estimation of frequency-hopping communications. Communications, IET, 2012, 6(4): 381-389.)

The above-mentioned improved method based on WVD only utilizes one of the time-frequency ridge peak frequency or peak frequency energy feature, which is greatly affected by noise. Occasions for fast and high-precision parameter estimation of frequency-hopping signals

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