Robust Multi-Frame Joint Frequency Hopping Radar Waveform Parameters Estimation Under Low Signal-Noise-Ratio

Abstract

Robust estimation of frequency hopping radar signal parameters is the key to achieving stable jam in frequency hopping communication. In order to deal with the poor noise resistance performance of conventional time-frequency analysis based parameter estimation method under low signal-noise-ratio conditions. A robust method is proposed in this paper for frequency hopping radar waveform parameters estimation. The method is performed in time domain. Firstly, the pulse repetition interval of the received radar signal is extracted by matching filtering, which is used as the window length to divide the signal into multiple frames. The coherent integration process is performed on the multi-frame signal to increase the signal-noise-ratio, which effectively ensures the robustness of the algorithm under low signal-noiseratio conditions. Secondly, the short-time Fourier transform is used to realize the rough estimation of the frequency hopping number, which reduces the range of subsequent hopping counts and reduces the amount of algorithm calculation. Finally, an accurate estimation of the waveform parameters including frequency hopping sequence, hop duration, frequency hopping interval and carrier frequency is realized. Simulation experiments verify the effectiveness of the algorithm and the robustness under various signal-noise-ratio conditions. INDEX TERMS Hopping frequency waveform, parameter estimation, multiple-frame, short time Fourier transform (STFT).

Parameter estimation of multiple frequency‐hopping signals based on Bayesian compressive sensing and atomic norm soft thresholding with missing observations

“…In particular, two performance measures are defined to evaluate the hopping time and the IF detection performance, respectively. The ratio of the correct hopping time detection is defined as [14, 23] $$\begin{equation}{P_t} \buildrel \Delta \over = \frac{1}{{{M_c}}}\sum_{i = 1}^{{M_c}} {{D_t}} (i),\end{equation}$$where $${M_c}$$ is the number of Monte Carlo trails and $${D_t}(i)$$ is the ratio of correct detections in the i th Monte Carlo trial. If the estimated hopping instant is less than three observations away from the associated true hopping instant, then the estimated hopping instant is declared the correct hopping time detection.…”

“…The ratio of incorrect IF detection is defined as [14, 23] $$\begin{equation}{E_f} \buildrel \Delta \over = 1 - \frac{1}{{{M_c}}}\sum_{i = 1}^{{M_c}} {{D_f}} (i),\end{equation}$$where $${D_f}(i)$$ is the ratio of correct frequency detections in the i th Monte Carlo trial.…”

“…Among many FH spectrum representation and parameter estimation techniques, joint time-frequency (TF) analysis [14][15][16][17], sparse linear regression [18] and compressive sensing (CS) [19] are the most representative methods. Methods suitable for traditional time-frequency analysis are short-time Fourier transform (STFT) [20,21] and Wigner-Ville distribution (WVD) [22].…”

Measurement based FHSS–type Drone Controller Detection at 2.4GHz: An STFT Approach

“…Drone controller signals might be FH signals that have temporal statistical characteristics and they can be written as [6],…”

“…In literature, most well known FHSS signal detection methods adopt the time-frequency analysis and wavelet analysis [4][5][6][7]. It is shown that Wigner Ville distribution, Wavelet transform method, and array signal processing methods can be used to identify FHSS signals with the burden of heavy computational complexity which make them hard to satisfy the real-time implementation.…”