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Locating multiple underwater acoustic sources is a problem that can be solved using antenna array beamforming based on the matched field (MF) processing. However, known MF beamforming techniques fail to provide good performance for multiple sources, a high noise power, and/or when the sources are close to each other. This paper proposes an MF technique for solving the localization problem. The proposed technique exploits formulation of the localization problem in terms of sparse representation of a small number of source positions among a much larger number of potential positions. The sparse representation is formulated as the basis pursuit de-noising (BPDN) problem for complex-valued variables. The solution is found as a joint solution to a set of BPDN problems corresponding to the set of source frequencies subject to the joint support. The joint BPDN problem is efficiently solved using the Homotopy approach and coordinate descent search. For further reduction in the complexity, a position grid refinement method is applied. Using simulated and real experimental data, it is shown that the technique can provide accurate source localization for multiple sources. The proposed technique outperforms other MF techniques in resolving sources positioned closely to each other, tolerance to the noise and capability of locating multiple sources. [ABSTRACT FROM PUBLISHER]

We propose a new method of modeling the signal transmission in underwater acoustic communications when the transmitter and receiver are moving. The motion-induced channel time variations can be modeled by sampling the transmitter/receiver trajectory at the signal sampling rate and calculating, for each position, the channel impulse response from the acoustic-field computation. This approach, however, would result in high complexity. To reduce the complexity, the channel impulse response is calculated for fewer (waymark ) positions and then interpolated by local splines to recover it at the signal sampling rate. To allow higher distances between waymarks and, thus, further reduction in the complexity, the multipath delays are appropriately adjusted before the interpolation. Because, for every time instant, this method only requires local information from the trajectory, the impulse response can recursively be computed, and therefore, the signal transmission can be modeled for arbitrarily long trajectories. An approach for setting the waymark sampling interval is suggested and investigated. The proposed method is verified by comparing the simulated data with data from real ocean experiments. For a low-frequency shallow-water experiment with a moving source that transmits a tone set, we show that the Doppler spectrum of the received tones is similar in the simulation and experiment. For a higher frequency deep-water experiment with a fast-moving source that transmits orthogonal frequency-division multiplexing (OFDM) communication signals, we investigate the detection performance of a receiver and show that it is similar in the simulation and experiment. [ABSTRACT FROM PUBLISHER]

In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N²) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented. [ABSTRACT FROM AUTHOR]

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 BF. [ABSTRACT FROM AUTHOR]

Low-complexity delayless acoustic echo cancellation techniques based on frequency-domain spline-identification are proposed and investigated. Two methods of approximation of the acoustic frequency response, both using B-splines, are considered: the optimal-spline method and the local-spline method. The optimal-spline method seeks the solution of a least squares problem. The most computationally demanding part of the method, solution of the normal equations, is implemented by using the low-complexity dichotomous coordinate descent algorithm. The local-spline method avoids solving the normal equations, enabling further simplification; this is at the expense of a slight degradation in the cancellation performance. A novel efficient double-talk detector is also proposed, being an inherent feature of the frequency-domain identification. Open-loop. and closed-loop identification schemes with cubic splines are studied by simulation and compared with the fast affine projection (FAP) algorithm. The proposed techniques provide cancellation performance better than that of the FAP algorithm, especially in double-talk and noisy environments, with a lower complexity. [ABSTRACT FROM AUTHOR]

Abs tract-We investigate polynomial spline approximation of stationary random processes on a uniform grid applied to Clarke's model of time variations of path amplitudes in multipath fading channels with Doppler scattering. The integral mean square error (MSE) for optimal and interpolation splines is presented as a series of spectral moments. The optimal splines outperform the interpolation splines; however, as the sampling factor increases, the optimal and interpolation splines of even order tend to provide the same accuracy. To build such splines, the process to be approximated needs to be known for all time, which is impractical. Local splines, on the other hand, may be used where the process is known only over a finite interval. We first consider local spilnes with quasioptimal spline coefficients. Then, we derive optimal spline coefficients and investigate the error for different sets of samples used for calculating the spline coefficients. In practice, approximation with a low processing delay is of interest; we investigate local spline extrapolation with a zero-processing delay. The results of our investigation show that local spline approximation is attractive for implementation from viewpoints of both low processing delay and small approximation error; the error can be very close to the minimum error provided by optimal splines. Thus, local splines can be effectively used for channel estimation in multipath fast fading channels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo cancellation in vehicle hands-free communication systems and video teleconferencing systems, namely the affine-projection Lorentzian (APL) algorithm. By minimizing the Lorentzian norm of the a posteriori error vector with a suitable constraint on the weight vector and providing a dynamic Lorentzian-norm-controlling parameter, the proposed APL algorithm achieves robustness against impulsive disturbances and speeds up convergence for colored input signals. The computational complexity of the APL algorithm is analyzed and a fast recursive filtering method is employed to reduce its complexity. The stability analysis, based on energy-conservation arguments, shows that the APL algorithm converges. Furthermore, its tracking behavior is also investigated and a step size optimizing the tracking performance is derived. Simulation results agree well with the theoretical analysis. Simulation results for channel estimation and in-car echo cancellation scenarios demonstrate that the APL algorithm achieves better performance compared to the maximum-correntropy-criterion, affine-projection-generalized-maximum-correntropy, affine-projection-sign, affine-projection-like M-estimate, and Lorentzian adaptive filtering algorithms in various impulsive interference environments. [ABSTRACT FROM AUTHOR]

For underwater acoustic (UWA) communication in sensor networks, the sensing information can only be interpreted meaningfully when the location of the sensor node is known. However, node localization is a challenging problem. Global Navigation Satellite Systems (GNSS) used in terrestrial applications do not work underwater. In this paper, we propose and investigate techniques based on matched field processing for localization of a single-antenna UWA communication receiver relative to one or more transmit antennas. Firstly, we demonstrate that a non-coherent ambiguity function (AF) allows significant improvement in the localization performance compared to the coherent AF previously used for this purpose, especially at high frequencies typically used in communication systems. Secondly, we propose a two-step (coarse-to-fine) localization technique. The second step provides a refined spatial sampling of the AF in the vicinity of its maximum found on the coarse space grid covering an area of interest (in range and depth), computed at the first step. This technique allows high localization accuracy and reduction in complexity and memory storage, compared to single step localization. Thirdly, we propose a joint refinement of the AF around several maxima to reduce outliers. Numerical experiments are run for validation of the proposed techniques. [ABSTRACT FROM AUTHOR]

• We have proposed kernel least lncosh (KLL) and quantized kernel least lncosh (QKLL) algorithms in the reproducing kernel Hilbert space under non-Gaussian environment. • The online vector quantization (VQ) is used to quantize the input space to construct a robust adaptive filtering algorithm. • The mean square convergence analysis of the QKLL algorithm has been conducted. • The performance of the KLL and QKLL algorithms is demonstrated by the Mackey–Glass (MG) time series prediction and nonlinear channel equalization. This paper introduces the kernel least lncosh (KLL) algorithm, in which the lncosh (logarithm of hyperbolic cosine) cost function is successfully applied in the reproducing-kernel-Hilbert space. The online vector quantization (VQ) is then used to quantize the input space to construct the algorithm which can curb the growth of network size. As a result, the quantized kernel least lncosh (QKLL) algorithm is developed, which is robust in non-Gaussian environments. The sufficient condition for mean-square convergence of the QKLL algorithm has been conducted. The performance of the KLL and QKLL algorithms is demonstrated by the short-term chaotic time-series prediction and non-linear channel-equalization (NCE). [ABSTRACT FROM AUTHOR]

• We have proposed a new constrained least lncosh (CLL) adaptive filtering algorithm for combating different impulsive noises. • The proposed CLL algorithm is developed via incorporating an lncosh function into constrained optimization problem under non-Gaussian noise environment. • The performance of the CLL algorithm has been analyzed for identifying systems and its convergence behavior and theoretical steady-state mean-square-deviation are presented. • The theoretical analysis agrees well with simulation results and these results verify that the CLL algorithm possesses superior performance and higher robustness than other CAF algorithms under various non-Gaussian impulsive noises. We propose a constrained least lncosh (CLL) adaptive filtering algorithm, which, as we show, provides better performance than other algorithms in impulsive noise environment. The proposed CLL algorithm is derived via incorporating a lncosh function in a constrained optimization problem under non-Gaussian noise environment. The lncosh cost function is a natural logarithm of a hyperbolic cosine function, and it can be considered as a combination of mean-square error and mean-absolute-error criteria. The theoretical analysis of convergence and steady-state mean-squared-deviation of the CLL algorithm in identification scenarios is presented. The theoretical analysis agrees well with simulation results and these results verify that the CLL algorithm possesses superior performance and higher robustness than other CAF algorithms under various non-Gaussian impulsive noises. [ABSTRACT FROM AUTHOR]

We develop adaptive beamforming algorithms that are robust against sensor failure and ill-conditioning in the autocorrelation matrix (common in low-rank interference scenarios). Both goals are achieved simultaneously through the use of l1 regularization. The algorithms are based on the complex adaptive reweighting homotopy technique. We also develop iterative versions of the algorithms that take advantage of properties of homotopy l1 solvers and dichotomous coordinate iterations to reduce considerably the computational complexity, compared with other regularization methods. [ABSTRACT FROM PUBLISHER]