Abstract
It is well known that the estimator-correlator (EC) is a maximum likelihood detector for random signals in Gaussian noise. In this paper we derive a continuous wavelet domain EC processor for the detection of signals that have propagated over stochastic propagation and scattering channels. The derivation shows that the wavelet transforms that are used for the conditional mean estimator (CME) and for the computation of the detection statistic must be defined by using reproducing kernel Hilbert space (RKHS) inner products rather than ordinary Hilbert space inner products. This fact suggests new weighted wavelet (as well as other time-frequency and time-scale) transforms. These new transforms have many applications to optimum signal processing.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1235-1239 |
| Number of pages | 5 |
| Journal | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
| Volume | 2 |
| State | Published - Jan 1 1997 |
| Event | Proceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2) - Pacific Grove, CA, USA Duration: Nov 3 1996 → Nov 6 1996 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Networks and Communications
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Wavelet domain implementation of the estimator-correlator and weighted wavelet transforms. / Sibul, Leon H.; Sidahmed, Stefan T.; Dixon, Teresa L.; Weiss, Lora G.
In: Conference Record of the Asilomar Conference on Signals, Systems and Computers, Vol. 2, 01.01.1997, p. 1235-1239.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Wavelet domain implementation of the estimator-correlator and weighted wavelet transforms
AU - Sibul, Leon H.
AU - Sidahmed, Stefan T.
AU - Dixon, Teresa L.
AU - Weiss, Lora G.
PY - 1997/1/1
Y1 - 1997/1/1
N2 - It is well known that the estimator-correlator (EC) is a maximum likelihood detector for random signals in Gaussian noise. In this paper we derive a continuous wavelet domain EC processor for the detection of signals that have propagated over stochastic propagation and scattering channels. The derivation shows that the wavelet transforms that are used for the conditional mean estimator (CME) and for the computation of the detection statistic must be defined by using reproducing kernel Hilbert space (RKHS) inner products rather than ordinary Hilbert space inner products. This fact suggests new weighted wavelet (as well as other time-frequency and time-scale) transforms. These new transforms have many applications to optimum signal processing.
AB - It is well known that the estimator-correlator (EC) is a maximum likelihood detector for random signals in Gaussian noise. In this paper we derive a continuous wavelet domain EC processor for the detection of signals that have propagated over stochastic propagation and scattering channels. The derivation shows that the wavelet transforms that are used for the conditional mean estimator (CME) and for the computation of the detection statistic must be defined by using reproducing kernel Hilbert space (RKHS) inner products rather than ordinary Hilbert space inner products. This fact suggests new weighted wavelet (as well as other time-frequency and time-scale) transforms. These new transforms have many applications to optimum signal processing.
UR - http://www.scopus.com/inward/record.url?scp=0030647524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030647524&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0030647524
VL - 2
SP - 1235
EP - 1239
JO - Conference Record of the Asilomar Conference on Signals, Systems and Computers
JF - Conference Record of the Asilomar Conference on Signals, Systems and Computers
SN - 1058-6393
ER -