Instantaneous Frequency Estimation Algorithm of Non-linear Frequency Modulation Signal in Low SNR Based on Generalized S-transform

2012 ◽  
Vol 6 (17) ◽  
pp. 627-635 ◽  
Author(s):  
Dianwei Wang ◽  
Jiulun Fan ◽  
Yonghua Li
Keyword(s):  
Frequency Modulation ◽  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Estimation Algorithm ◽  
Instantaneous Frequency Estimation ◽  
Low Snr ◽  
S Transform ◽  
Generalized S Transform ◽  
Modulation Signal ◽  
Linear Frequency Modulation Signal

Instantaneous Frequency Estimation Using the ${\rm S}$-Transform

2008 ◽  
Vol 15 ◽  
pp. 309-312 ◽  
Author(s):  
L. Stankovic ◽  
M. Dakovic ◽  
Jin Jiang ◽  
E. Sejdic
Keyword(s):  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Instantaneous Frequency Estimation ◽  
S Transform

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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