Name
Title | ||
|---|---|---|
On the role of the Hilbert transform in boosting the performance of the annihilating filter |
Abstract | ||
|---|---|---|
We consider the problem of parameter estimation from real-valued multi-tone signals. Such problems arise frequently in spectral estimation. More recently, they have gained new importance in finite-rate-of-innovation signal sampling and reconstruction. The annihilating filter is a key tool for parameter estimation in these problems. The standard annihilating filter design has to be modified to result in accurate estimation when dealing with real sinusoids, particularly because the real-valued nature of the sinusoids must be factored into the annihilating filter design. We show that the constraint on the annihilating filter can be relaxed by making use of the Hilbert transform. We refer to this approach as the Hilbert annihilating filter approach. We show that accurate parameter estimation is possible by this approach. In the single-tone case, the mean-square error performance increases by 6 dB for signal-to-noise ratio (SNR) greater than 0 dB. We also present experimental results in the multi-tone case, which show that a significant improvement (about 6dB) is obtained when the parameters are close to 0 or π. In the mid-frequency range, the improvement is about 2 to 3dB. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853916 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
Hilbert transforms,estimation theory,filtering theory,parameter estimation,signal reconstruction,signal sampling,Hilbert transform,SNR,finite-rate-of-innovation signal reconstruction,finite-rate-of-innovation signal sampling,mean-square error performance,noise figure 6 dB,parameter estimation,real-valued multitone signal,signal-to-noise ratio,spectral estimation,standard annihilating filter design,Annihilating filter,discrete Hilbert transform,finite rate of innovation,sampling,spectral estimation | Spectral density estimation,Mathematical optimization,Computer science,Sampling (statistics),Boosting (machine learning),Estimation theory,Hilbert transform,Hilbert spectral analysis,Filter design | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sudarshan Nagesh | 1 | 0 | 0.68 |
| Satish Mulleti | 2 | 12 | 2.99 |
| Chandra Sekhar Seelamantula | 3 | 142 | 37.43 |