In this study, the authors propose a closed-form time-of-arrival source localisation method and justify the employment of the invariance property of the maximum likelihood (ML) estimator in the source localisation con...
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In this study, the authors propose a closed-form time-of-arrival source localisation method and justify the employment of the invariance property of the maximum likelihood (ML) estimator in the source localisation context with multiple samples. The magnitude of the bias of the proposed sample vector function (the statistic that consists of the multiple observations set) using the invariance property of the ML estimator is smaller than that based on the sample mean. Therefore, the meansquarederror (MSE) of the weighted least squares estimate using the proposed sample vector function is smaller than that based on the sample mean when the variances of both sample vector functions are the same. Furthermore, the authors investigate a situation in which sensors have erroneous position information. The simulation results show that the averaged MSE performance of the proposed method is superior to that of the existing methods irrespective of the number of samples.
Compressed sensing is a topic that has recently gained much attention in high-speed underwater acoustic (UWA) communications. Combined the MIMO communication system with OFDM technology, they can realize high-speed da...
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ISBN:
(纸本)9781479958368
Compressed sensing is a topic that has recently gained much attention in high-speed underwater acoustic (UWA) communications. Combined the MIMO communication system with OFDM technology, they can realize high-speed data communication over UWA channels and overcome the fading problem efficiently. In this paper performance analysis of compressed sensing (CS) channel estimation algorithms for underwater acoustic communication is investigated. The estimation of UWA channel is mainly based on Least Square (LS), Orthogonal Matching Pursuit (OMP) and Compressed Sensing Matching Pursuit (CS-MP) channel estimation algorithms. We have compared the performance of channel estimation algorithms by measuring meansquarederror (MSE) vs. SNR and calculation time vs. SNR. On the basis of the simulation results, the CS-MP channel estimation indicates to perform better performance than LS and OMP, which can lead to accurate channel state information (CSI) with fewer pilots so as to improve the frequency spectrum efficiency. CS provides some new idea about channel estimation for UWA communications.
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