Source localization using acoustic sensor networks has been drawing a lot of research interest recently. In a sensor network, there are a large number of inexpensive sensors which are densely deployed in a region of i...
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ISBN:
(纸本)9781457720536
Source localization using acoustic sensor networks has been drawing a lot of research interest recently. In a sensor network, there are a large number of inexpensive sensors which are densely deployed in a region of interest (ROI). This dense deployment enables accurate intensity (energy) based target localization. The maximum-likelihood is the predominant objective which leads to a variety of source localization approaches. However, the investigation on the energy-based localization for multiple sources has been very rare. The corresponding robust and efficient algorithms are still being pursuit by researchers nowadays. In this paper, we would like to combat the energy-based multiple-source localization problem. We propose two new algorithms, namely alternating projection (ap) algorithm and expectation maximization (EM) algorithm, which can combat the energy-based localization problem for multiple sources.
Aiming at the nonlinear system identification problem, a parallel recursive affine projection (ap) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identif...
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Aiming at the nonlinear system identification problem, a parallel recursive affine projection (ap) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identifies in parallel the Volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the Volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional ap adaptive algorithm based on Volterra series. Simulation results indicate that the proposed method in this paper is efficient.
The normalized LMS (NLMS) algorithm has been successfully used in many system identification problems. However, the NLMS algorithm is known to exhibit low convergence speed especially when the input data covariance ma...
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ISBN:
(纸本)0780374029
The normalized LMS (NLMS) algorithm has been successfully used in many system identification problems. However, the NLMS algorithm is known to exhibit low convergence speed especially when the input data covariance matrix is ill-conditioned. In this paper, we consider a sub-optimal implementation of the affine projection (ap) algorithm based on a prewithening mechanism, which renders the convergence characteristics less sensitive to the coloring of the input signal spectrum than is the case for the NLMS algorithm. Comparisons with the ap algorithm are given to validate our approach. Implementation details are discussed in the context of hands-free telephony where echo cancelling and speech coding algorithms are integrated on the same DSP board.
In this correspondence, we present a Newton-based alternating projection (ap) algorithm for estimating the parameters of exponential signals in noise, and compare its performance with that of modified forward-backward...
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In this correspondence, we present a Newton-based alternating projection (ap) algorithm for estimating the parameters of exponential signals in noise, and compare its performance with that of modified forward-backward linear prediction/backward linear prediction (FBLP/BLP), total least squares (TLS), and iterative quadratic maximum likelihood (IQML) methods using computer simulations. In the case of undamped sinusoids, the ap algorithm yielded lower threshold SNR than the others, while in the case of damped sinusoids, its threshold SNR is same as that of TLS, but is 1 dB lower than that of MBLP and IQML.
We present an improved initialization procedure for the alternating projection (ap) algorithm which is an efficient iterative algorithm for computing the deterministic maximum likelihood (ML) estimator of the location...
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We present an improved initialization procedure for the alternating projection (ap) algorithm which is an efficient iterative algorithm for computing the deterministic maximum likelihood (ML) estimator of the locations of multiple sources in passive sensor arrays. By utilizing the high resolution property of the sequential MUSIC (MUltiple SIgnal Classification) algorithm based on the sequential estimation technique, the procedure provides fast initial estimates that reduce significantly the number of iterations to convergence. Also these initial estimates improve greatly the possibility of global convergence. Also, we give computer simulation results to compare the ap algorithm using the proposed initialization procedure and the original ap algorithm in terms of the estimation performance and convergence behaviors.
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