Instantaneous frequency estimation by using Wigner distribution and Viterbi algorithm

2004 ◽  
Author(s):  
L.J. Stankovic ◽  
I. Djurovic ◽  
A. Ohsumi ◽  
H. Ijima
Keyword(s):  
Instantaneous Frequency ◽  
Viterbi Algorithm ◽  
Wigner Distribution ◽  
Frequency Estimation ◽  
Instantaneous Frequency Estimation

scholarly journals Combination of the viterbi algorithm and cross-wigner distribution for the instantaneous frequency estimation phase signals in high noise environments

2018 ◽  
Vol 69 (3) ◽  
pp. 255-258
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Viterbi Algorithm ◽  
Wigner Distribution ◽  
Frequency Estimation ◽  
Penalty Functions ◽  
Accuracy Improvement ◽  
Instantaneous Frequency Estimation ◽  
High Noise ◽  
Fm Signals ◽  
Improved Accuracy

Abstract In this paper, combination of the cross-Wigner distribution (XWD) and the Viterbi algorithm (VA) for the instantaneous frequency (IF) estimation of frequency modulated (FM) signals in high noise environments is proposed. The favourable properties of the VA, the IF reconstruction based on minimization of the path penalty functions, and the XWD, iterative accuracy improvement of the IF estimation, give hybrid IF estimator with improved accuracy for high noise environments

scholarly journals RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Matching Pursuit ◽  
Frequency Estimation ◽  
Consensus Algorithm ◽  
Multidimensional Space ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Polynomial Phase ◽  
Fm Signals ◽  
Fifth Order

AbstractFrequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

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