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Most available instantaneous frequency (IF) estimation methods for frequency modulated signals in white noise deteriorate dramatically when SNR falls below some threshold. We present a time-varying Prony method for IF estimation from low SNR data. It is an extension of a frequency estimation method for stationary processes which was shown to be less complicated than, yet close in performance to, the best available approaches based on singular value decomposition or the principle of maximum likelihood. First, the time-varying autoregressive order is set higher than that needed for pure signal components so that the extra poles capture part of the noise. Then we choose "signal poles" based on a subset selection procedure. The performance improvement at low SNR over the TVAR method without subset selection is evidenced through simulation experiments.

• Time-Varying Channels
• Time-Varying Prony Method
• Instantaneous Frequency Estimation
• SNR
• Frequency Modulated Signals
• White Noise
• Time-Varying Autoregressive Order
• Signal Poles
• Subset Selection Procedure