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The derivation of low-complexity of recursive least square (RLS) adaptive filtering algorithms is discussed.

The problem of providing simultaneous high data rate communication links to multiple moving railway trains from high-altitude platforms (HAPs) using a 'smart' antenna array is addressed. Solutions are provided subject to system-level constraints: the number and position of interferers (trains), motion of both the trains and the HAP, and link budget for broadband transmission in the 31/28 GHz bands, assuming line-of-sight propagation. Techniques are studied for estimation of the number of trains present, accurate direction-of-arrival (DOA) estimation, and tracking of multiple trains for the purposes of beamforming (BF) and reliable attribution of DOA estimates to trains. A range of train scenarios, including trains crossing and passing through tunnels and shadowed stations, is considered. It is shown that extended Kalman filtering (EKF) applied to DOA estimates, in addition to providing improved accuracy of positional estimation of trains, also provides much more reliable attribution of DOA estimates to trains compared to DOA estimation alone--particularly when trains cross or pass closely. Methods for adapting EKF for tracking trains passing through tunnels and shadowed stations are proposed, and it is shown that tracking can be initialized to cope with both slow variations in train velocity and sudden HAP motion. BF that is based on null steering is shown to be beneficial in HAP-train data communications, even for small number of trains, providing increased stability of signal-to-interference-plus-noise ratio. Based on the analysis, array signal processing techniques are proposed based on an iterative root-MUSIC DOA method, extended Kalman tracking, and Capon BE Index Terms--Array signal processing, high-altitude platforms (HAPs), smart antennas, stratospheric platforms, tracking filters.

The low-complexity delayless acoustic echo cancellation methods are proposed and investigated based on frequency-domain spline-identification. The results have shown that these methods have provided cancellation performance better than that of the fast offline projection (FAP) algorithm, especially in double-talk and noisy environments, with a lower complexity.

Clarke's model of time variations of path amplitudes in multipath fading channels with Doppler scattering is used for the investigation of polynomial spline approximation of stationary random processes. The results reveal that local spline approximation is attractive for implementation, for both low processing delay and small approximation error.

This paper concerns the estimation of a frequency offset of a known (pilot) signal propagated through a slowly fading multipath channel, such that channel parameters are considered to be constant over the observation interval. We derive a maximum-likelihood (ML) frequency estimation algorithm for additive Gaussian noise and path amplitudes having complex Gaussian distribution when covariance matrices of the fading and noise are known; we consider in detail the algorithm for the white noise and Rayleigh fading, in particular, for independent fading of path amplitudes and pilot signals with diagonal autocorrelation matrices. For the latter scenario, we also derive an ML frequency estimator when the power delay profile is unknown, but the noise variance and bounds for the path amplitude variances are specified; in particular, this algorithm can be used when path delays and amplitude variances are unknown. Finally, we consider frequency estimators which do not use a priori information about the noise variance; these algorithms are also operable without timing synchronization. All the frequency estimators exploit the multipath diversity by combining periodograms of multipath signal components and searching for the maximum of the combined statistic. For implementation of the algorithms, we use a fast Fourier transform-based coarse search and fine dichotomous search. We perform simulations to compare the algorithms. The simulation results demonstrate high accuracy performance of the proposed frequency estimators in wide signal-to-noise ratio and frequency acquisition range. Index Terms--Discrete Fourier transforms, fading channels, fast Fourier transforms, frequency estimation, maximum-likelihood estimation, multipath channels, Rayleigh channels.

The matched-phase coherent broadband matched-field (MF) processor has been previously proposed and shown to outperform other advanced broadband MF processors. It has been previously proposed to search the matched phases using the simulated annealing, which is well known for its ability of solving global optimization problems while having high computational complexity. This prevents simultaneous processing of many frequencies, and thus, limits the processor performance. We propose to use a novel iterative technique, the phase descent search (PDS), for searching the matched phases. This technique is based on coordinate descent optimization which is mainly applicable to solving convex problems. In this work, we investigate its application to the phase search problem, which is a nonconvex problem. We show that the PDS algorithm obtains matched phases similar to that obtained by the simulated annealing, and has significantly lower complexity. Therefore, it enables to search phases for a large number of frequencies and significantly improves the processor performance. The proposed processor is applied to real data from the 1996 Shallow Water Experiment (SWellEx-96) for locating a moving acoustic source at distances between 1 and 9 km with a step of about 150 m. At each distance, one 1-s snapshot with 13 frequencies is enough to provide accurate localization of the source well matched to global positioning system (GPS) measurements. [ABSTRACT FROM PUBLISHER]