Time-Frequency Analysis And Synthesis Of Non-Stationary Signals

1989 ◽  
Author(s):  
L. Auslander ◽  
I. Gertner ◽  
R. Tolimieri ◽  
G. Eichmann
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis And Synthesis ◽  
Stationary Signals

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
W. L. Lu ◽  
J. W. Xie ◽  
H. M. Wang ◽  
C. Sheng
Keyword(s):  
Frequency Analysis ◽  
Simulated Data ◽  
Radar Signal ◽  
Electronic Warfare ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Radar Signals ◽  
Electronic Intelligence

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

2012 ◽  
Vol 198-199 ◽  
pp. 803-807
Author(s):  
Feng Li Wang ◽  
Shu Lin Duan ◽  
Hong Tao Gao
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Local Energy ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Process Systems ◽  
Frequency Space ◽  
Time Frequency ◽  
Stationary Signals ◽  
Local Wave

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

Evaluation of Gabor Transform Filter Threshold Identified by Initial Highest Inter-Cluster Distance Probability

2011 ◽  
Vol 204-210 ◽  
pp. 1166-1169
Author(s):  
Di Fan ◽  
Chang Zhi Lv ◽  
Mao Yong Cao
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Experimental Results ◽  
Gabor Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Signals ◽  
Key Factor ◽  
Cluster Distance ◽  
Better Than

Gabor transform is very suitable for time-frequency analysis and good for filtering non-stationary signals. The threshold of the Gabor transform filter is a key factor for the filter’s effectiveness. A novel threshold based on initial highest inter-cluster distance probability (IH-ICDP) is described in this paper and it can make the filter more efficient. Some experiments have been carried out under several conditions to evaluate the new threshold’s characteristics. The experimental results show that Gabor transform filter with this proposed threshold works better than wavelet transform filter, especially when the signal’s SNR is very low. From the evaluation results, it is possible to consider that the threshold presented is optimal or nearly optimal.

scholarly journals Time-frequency analysis of spread spectrum based communication and audio watermarking systems

2021 ◽  
Author(s):  
Serhat Erküçük
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Communication Systems ◽  
Estimation Algorithm ◽  
Audio Watermarking ◽  
Spread Spectrum Communication ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Symbol Detection ◽  
Stationary Signals

In this study, we present novel applications of time-frequency analysis to spread spectrum based communication and audio watermarking systems. Our objective is to detect and estimate non-stationary signals, such as chirps, that are characterized by directional elements in the time-frequency plane. Towards this goal, we model non-stationary signals using the matching pursuit decomposition algorithm, generate a positive time-frequency representation of the signal model using the Wigner-Ville distribution and estimate the energy varying directional elements using a line detection algorithm based on the Hough-Radon transform. Spread spectrum communication systems frequently encounter nonstationary signals with energy varying directional elements as hostile jamming signals. In this thesis, we develop a new interference excision algorithm for spread spectrum communication systems based on the directional element estimation algorithm. At the receiver, we first excise the interference from the spread spectrum signal before despreading and data symbol detection. The new algorithm can excise single and multicomponent interferences such that the spread spectrum system can reliably detect the transmitted message symbols even, when the interference power exceeds the jamming margin of the system. We verify the effectiveness of the interference excision algorithm using simulation studies. Watermarking is the process of embedding imperceptible data into the host signal for marking the copyright ownership. The embedded data should be extractable to prove ownership. Watermarking systems face problems similar to those in spread spectrum communication systems, namely, intentional attacks by the adversaries. In watermarking, the adversaries try to obliterate the embedded watermark in order to prevent its detection by authorized parties. In this thesis, we develop a spread spectrum audio watermarking scheme, where we embed perceptually shaped linear chirps as watermark messages. The directional elements of the chirp signals represent different watermark messages. We extract the watermark by first detecting the transmitted message symbols in the spread spectrum signal. We then use the directional element estimation algorithm based on the time-frequency analysis as a post-processing tool to minimize the effects of hostile attacks on the extractability of the embedded watermark. We demonstrate the robustness of the algorithm by extracting the watermark correctly after common signal processing operations representing hostile attacks by adversaries.

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