A parameter estimation method for time‐frequency‐overlapped frequency hopping signals based on sparse linear regression and quadratic envelope optimization

Abstract

SummaryThe frequency hopping (FH) signals have well‐documented merits for commercial and military fields due to near‐far resistance and robustness to jamming. Therefore, the parameter estimation of FH signals is an important task for subsequent information acquisition and autonomous electronic countermeasure or attack. However, under the complex electromagnetic environment, there always exist overlaps in the time‐frequency domain among multiple signals, which bring poor signal sparsity and make the estimation more challenging. In this paper, a novel parameter estimation approach is developed for the time‐frequency‐overlapped FH signals under single‐channel reception. The exact solution is mainly composed of the sparse linear regression‐based matrix optimization (SLR‐MO) and quadratic envelope optimization (QEO). SLR‐MO highlights the removal of noise and distortion features for improving the overall sparsity and time‐frequency resolution. QEO further eliminates parts of the interfering signal features and outliers and then extracts and optimizes the average time‐frequency ridge to complete the parameter estimation (hopping instants, period, and carriers). Simulation results demonstrate that the developed estimator outperforms the traditional methods in the scope of application, estimation accuracy, and the robustness under low signal‐to‐noise ratio (SNR).

A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

“…Because (10) and (11) have similar forms, we only consider (10) and minimize it. To minimize (10), we set Figure 16a shows the correct rates of the CSML algorithm and the CSOMP-ASW algorithm for frequency estimation of multiple FH signals with frequency-switching time.…”

“…Because (10) and (11) have similar forms, we only consider (10) and minimize it. To minimize (10), we set Figure 16a shows the correct rates of the CSML algorithm and the CSOMP-ASW algorithm for frequency estimation of multiple FH signals with frequency-switching time. When the SNR is greater than 0 dB, the frequency estimation accuracy of the CSML algorithm is 100%; when the SNR is greater than 10 dB, the frequency estimation accuracy of the CSOMP-ASW algorithm is greater than 98%; when the SNR is greater than 20 dB, the frequency estimation accuracy of the CSOMP-ASW algorithm is equal to 100%.…”

“…Because ( 10) and ( 11) have similar forms, we only consider (10) and minimize it. To minimize (10), we set ∂ϕ n,1 /∂a n,1 = 0. Therefore, the estimated value of a n,1 can be expressed as:…”

“…In order to intercept and jam the frequency-hopping signals of non-cooperative targets, precise knowledge of the parameters of their transmitted FH signals is required [9]. Therefore, parameter estimation of frequency-hopping (FH) signals is an important task for information interception and autonomous electronic countermeasures [10,11]. The battlefield environment is rapidly changing, and the fast estimation of FH signals in the case of unknown frequency, an unknown number of signals or signals with frequency-switching time is of great importance.…”

A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

“…to monitor the UAV frequency band all-weather, analyze the spectrum data, then detect the control signal or the communication signal. Compared with these methods, spectrum monitoring has the advantages of good applicability and less influence by the environment, become an important method for UAV discovery [6][7][8][9][10] [28].…”