Chair: Dimitris G. Manolakis, Boston College (USA)
Alex B. Gershman, Institute of Applied Physics (RUSSIA)
Alexander L. Matveyev, Institute of Applied Physics (RUSSIA)
A simple approximate maximum likelihood (AML) estimator is derived for estimating a power of a single signal with rank-one spatial covariance matrix known a priori except for a scaling. The noises are assumed to have different and unknown powers in each array sensor. The variance of the introduced AML estimator is compared with the exact Cramer-Rao bound (CRB) of this estimation problem analytically and by computer simulations. It is shown analytically that the AML estimator achieves CRB in the majority of practically important cases. Computer simulations have been performed showing that the estimation errors of the AML estimator are very close to CRB for a wide SNR range.
Thomas E. Biedka, E-Systems (USA)
The existing self coherence restoral (SCORE) beamforming techniques have been shown to be capable of blindly extracting a desired signal in the presence of unknown noise and interference by exploiting the cyclostationarity of the signal of interest. The versions of SCORE which offer the best convergence properties require computation of the observed data cyclic correlation matrix. This can be a large computational burden, particularly if the number of antennas in the array is large. This paper introduces a method which requires only a column-wise subset of the cyclic correlation matrix. It is shown that in many cases the new method performs as well as existing SCORE methods yet requires many fewer computations.
John N. Kalamatianos, Northeastern University (USA)
Elias S. Manolakos, Northeastern University (USA)
The design of efficient parallel processing implementations for speeding up the computationally intensive estimation of Higher-Order Statistics (HOS) has been recognized as an important task by the signal processing community. In this paper we report on the synthesis of minimum running time (latency) data-parallel algorithms that can be employed to compute all moment lags, up to the 3rd or 4th-order, on the MasPar-1 Single Instruction Multiple Data (SIMD) parallel system. By construction the synthesized SIMD algorithms require constant memory per processing element (PE), thus allowing the processing of 1-D input data sequences with as many as M=1024 data samples. Simulation results are presented showing the gain in speedup and execution times, as compared to optimized versions of the serial estimation algorithm running in powerful workstations.
Mingui Sun, University of Pittsburgh (USA)
Ching-Chung Li, University of Pittsburgh (USA)
Robert J. Sclabassi, University of Pittsburgh (USA)
In this paper the computational issues for symmetric wavelet transforms are investigated. We present a novel frequency-domain algorithm using the discrete cosine transforms (DCTs) and discrete sine transforms (DSTs). A high efficiency is achieved when this algorithm is applied to signals of finite duration, especially images.
George A. Tsihrintzis, University of Virginia
Chrysostomos L. Nikias, University of Southern California (USA)
We address the problem of estimation of the parameters of the recently proposed symmetric, alpha--stable model for impulsive interference. We propose new estimators based on asymptotic extreme value theory, order statistics, and fractional lower--order moments, which can be computed fast and are, therefore, suitable for the design of real--time signal processing algorithms. The performance of the new estimators is evaluated theoretically and via Monte--Carlo simulation. Key words: Impulsive Interference, Stable Distribution, Asymptotic Extreme Value Theory, Order Statistic, Fractional Lower--Order Moment.