Chair: Ed Titlebaum, University of Rochester (USA)
T.G. Manickam, University of Rhode Island (USA)
R.J. Vaccaro, University of Rhode Island (USA)
We address the problem of multipath time-delay estimation. When the received data is very long compared to the transmitted signal, the data is expected to consist of a large number of paths. Modeling the entire data becomes computationally expensive. We propose a technique to break the data into short segments and model each segment individually without misfitting or truncating any paths at the ends of any segment. By effectively using overlapping segments, the estimates of time-delays are combined to model the entire data record. The method is extended to the case where only basebanded data are available. The proposed technique is demonstrated on an experimental sea-test data.
Yisong Ye, Auburn University (USA)
Jitendra K. Tugnait, Auburn University (USA)
The problem of estimating the difference in arrival times of a non-Gaussian signal at two spatially separated sensors is considered. The signal is assumed to be corrupted by spatially correlated Gaussian (or a class of non-Gaussian) noise of unknown cross-correlation. We analyze the asymptotic performance of some recently proposed differential time-delay estimators which exploit the integrated polyspectrum of the measurements. The proposed estimators are asymptotically maximum-likelihood when attention is confined to the integrated polyspectra of the measurements. Therefore, the performance of the estimators approaches the Cramer-Rao (CR) bound asymptotically. Expressions for the relevant CR bound are derived. Computer simulations are presented comparing actual performance with the CR bounds for a simple example.
H.C. So, The Chinese University of Hong Kong (HONG KONG)
P.C. Ching, The Chinese University of Hong Kong (HONG KONG)
Y.T. Chan, Royal Military College of Canada (CANADA)
The explicit time delay estimator (ETDE) provides an efficient way to estimate the time difference of arrival between signals received at two separated sensors. However, the algorithm is biased for finite filter length and the delay bias increases when the signal-to-noise ratio (SNR) or the number of filter taps decreases. In this paper, we add an adaptive gain control to the ETDE to decouple the effect of changes in the SNR during adaptation. As a result, a smaller delay variance and an unbiased delay estimate for a wide range of filter lengths can be attained. Computer simulations are presented to validate the theoretical derivations of the proposed estimator for static and linearly time-varying delays under both stationary and nonstationary signal/noise power environments.
Donald W. Tufts, University of Rhode Island (USA)
Hongya Ge, University of Rhode Island (USA)
We present an novel method of fast and accurate estimation of frequencies of sinusoids from short data records of wide-band under-sampled data. By introducing properly chosen delay lines, and by using sparse linear prediction [1, 2, 3], our proposed method provides unambiguous frequency estimates using low A/D conversion rates. It provides a new way to implement a digital microwave receiver under these conditions.
Eric Petit, CEPHAG-ENSIEG (FRANCE)
Genevieve Jourdain, CEPHAG-ENSIEG (FRANCE)
The aim of this paper is to propose a receiver which performs fast self-recovering adaptive identification and equalization based on decision feedback equalization and reduced maximum likelihood sequence estimation. It runs on binary-phase-shift keying signals through underwater acoustic channels. Considerations on implementation are discussed. Its performance is demonstrated both on experimental data from long-range deep water channel and on simulated data from two discrete-time channels with severe amplitude distorsion and additive white gaussian noise.
Y. Xiang Yuan, University of Saskatchewan
G. Clifford Carter, NUWCDN
J. Eric Salt, University of Saskatchewan (CANADA)
A generalized covariance expression for time difference of arrival (TDOA) estimators is derived for an M- sensor arbitrary array in a two-path underwater environment. The resulting expression can be reduced to the variance and covariance expressions developed previously. The correlation among the time difference of arrival estimators and its effect on a localization error are investigated for a two-sensor vertical array. The results show that the correlation among the multipath TDOA estimators is significant and the degree of correlation depends on the power spectral density of source signal and that of noise, and only on two multipath TDOAs. Because of this correlation, every TDOA estimate contributes information to localization and therefore a whole set of TDOA estimators should be used.
Mark D. Hahm, University of Rochester (USA)
Edward L. Titlebaum, University of Rochester (USA)
Zoran I. Mitrovski, University of Rochester (USA)
It has been shown previously that, using a deconvolution method such as projection onto convex set (POCS), which relies on some form of inverse filtering, one can increase the resolution of multipath parameter estimation in a Doppler environment beyond that which is achievable with correlation processing alone. This paper shows that the output of an inverse filter in the presence of Doppler can be characterized by two signal-related functions: one of which is free from the range-Doppler coupling effects inherent in correlation processing.
Yingbo Hua, University of Melbourne (AUSTRALIA)
Howard Yang, University of Melbourne (AUSTRALIA)
Mingyong Zhou, University of Melbourne (AUSTRALIA)
This paper presents a study of blind system identification using measurements of multiple sensors. The mathematical model we consider here consists of multiple FIR channels driven by an unknown common source. We first show an orthogonal complement system matrix which is very useful in understanding the multi-channel system and developing efficient identification techniques. We then show a fast maximum likelihood method, its relation to a cross-relation method, and a new method utilizing a minimum noise subspace. We also report a study of strict identifiability of the multi-channel system and its relation to existing concepts.
Xiao-Jiao Tao, Southeast University (PEOPLES REPUBLIC OF CHINA)
Cai-Rong Zou, Southeast University (PEOPLES REPUBLIC OF CHINA)
Zhen-Ya He, Southeast University (PEOPLES REPUBLIC OF CHINA)
Estimation of the trajectory of a target from a passive sonar's bearings and frequency measurements in the presence of multivariate normally distributednoise, with unknown inhomogeneous general covariance, is modelled as a nonlinear multi-response parameter estimation problem. It is shown that Maximum Likelihood Estimation in this case is identical to optimizing a determinant criterion whichhas a concise form and contains no elements of unknown covariance matrix. Aneffective Guass-Newton type algorithm, using only the first-order derivatives of the model function, is presented to implement such estimation. The simulation shows that the proposed approach is superior to the traditional estimation methods especially under the condition of strong inhomogeneity of noise covariance and high correlation between different types of measurement noises.
B. Geller, CEPHAG- ENSIEG (FRANCE)
V. Capellano, CEPHAG- ENSIEG (FRANCE)
G. Jourdain, CEPHAG- ENSIEG (FRANCE)
This paper concerns the equalization problem of an underwater high rate transmission system . As the channel delay spread is large compared with the data duration, we have developed an equalizer in order to minimize the calculus burden. Test on real data is also made.