Parameters Of Frequency Hopping Signals Research Articles

Parameter estimation of frequency hopping signal based on MWC–MSBL reconstruction

Aiming at the problem that single-network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi-measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time-hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal-to-noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti-noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware.

Parameter estimation of frequency hopping signals based on analogue information converter

Aiming at the shortcomings of the existing frequency hopping signal parameter estimation algorithms based on compressed sensing theory, such as weak anti-noise performance, high data transmission and processing, and high hardware implementation cost, this study proposes an improved frequency hopping signal parameter estimation algorithm based on analogue information converter called Baseband Parameter Estimation (BPE) method. BPE does not reconstruct the frequency hopping signal into the Nyquist spectrum, and after obtaining the support set of the signal, the parameter estimation of the frequency hopping signal is performed directly at the baseband. The processing of baseband spectral slices can convert unreconstructed signal parameter estimates into parameter estimates for certain support sets, which makes BPE possible. Simulation experiments show that compared with the existing parameter estimation algorithm, BPE can not only greatly reduce the bandwidth and storage pressure required for subsequent transmission, but also has good anti-noise performance.

Parameter Blind Estimation of Frequency-Hopping Signal Based on Time–Frequency Diagram Modification

To effectively estimate the parameters of the multiple frequency-hopping signals, a blind parameter estimation method based on time–frequency diagram modification is proposed. Firstly, the observed signal is transformed to the time–frequency domain, using short time Fourier transform with overlapping windows. Then an energy detection method based on adaptive threshold is used to modify the time–frequency diagram, and the parameters of the frequency-hopping signals are finally obtained from the modified spectrogram. Theoretical analysis and simulation results show that the method proposed can get a clear time–frequency diagram at low signal-to-noise ratio (SNR), and its accuracy of parameter estimated is higher than that of previous methods. When SNR is −10 dB, estimation errors of frequency, hopping time and hop duration is 0.0002, 0.0008 and 0.0013, respectively, which are about 1–2 orders of magnitude lower over the previous method.

Parameter estimation of frequency hopping signals based on the Robust S-transform algorithms in alpha stable noise environment

In many communication systems the noise is non-Gaussian and usually exhibits impulsive characteristics. The alpha stable distribution, which is the only distribution satisfies the generalized central-limit theorem and characterizes a range of behaviors from Gaussian to extremely impulsive signals, is appropriate to model this type of noise. Since the performance of the S-transform (ST) based frequency hopping (FH) signal parameter estimation methods, which are very effective in Gaussian noise, may deteriorate significantly in the presence of impulsive noise, two robust methods based on the ST, i.e., the fractional lower order ST (FLOST) and the iterative ST (IST), are proposed in this paper. Specifically, the FLOST is the combination of fractional lower order statistics and the ST, whereas the IST is the application of the minimax Huber's robust theory to the ST. Both robust ST algorithms exhibit higher time-frequency concentration in the presence of alpha stable noise. Simulation results show that the proposed algorithms are valid for FH signal parameter estimation, and they distinctively outperform the standard ST in impulsive noise environment.

Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization

A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.

Joint signal parameter estimation of frequency-hopping communications

In the problem of the parameter estimation of frequency-hopping (FH) signals, most of existing works can only provide the unauthorised detection of the FH signals or estimate the part of parameters of FH signals, therefore cannot provide sufficient information to demodulate signals for message deciphering applications in a non-cooperative communications. This study proposes a new method based on reassigned smoothed pseudo Wigner-Ville distribution (SPWVD) and maximum-likelihood estimation for joint signal parameter estimation of FH communications with M -ary frequency-shift-keyed (MFSK) orthogonal modulation. With the good time frequency concentration and restraining cross-term ability of reassigned SPWVD, this algorithm can efficiently estimate the parameters of FH signals which include hopping frequencies, hopping rate, hopping sequence and modulation type without making any assumption about the alphabet of hopping frequencies or the synchronisation. The algorithm has improved estimation accuracy, reduced estimation root-mean-squared error and obtained better performance than that of the approach based on conventional time-frequency distribution. The simulation results are presented to evaluate the performances of the proposed algorithm. The root-mean-squared error of hopping frequency estimation is <;10 -3 for signal-to-noise ratio (SNR) of >-2 dB. The percentage of correct modulation recognition goes to 90% when SNR >3 dB.