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ABSTRACT: A new signal subspace approach for estimating the frequency of a single complex tone in additive white noise is proposed in this correspondence. Our main ideas are to use a matrix without repeated elements to represent the observed signal and exploit the principal singular vectors of this matrix for frequency estimation. It is proved that for small error conditions, the frequency estimate is approximately unbiased and its variance is equal to Cramér-Rao lower bound. Computer simulations are included to compare the proposed approach with the generalized weighted linear predictor, periodogram, and phase-based maximum likelihood estimators in terms of estimation accuracy, computational complexity, and threshold performance.
IEEE Transactions on Signal Processing 03/2011; · 2.63 Impact Factor
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IEICE Transactions. 01/2011; 94-A:823-825.
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IEEE Transactions on Signal Processing. 01/2010; 58:1433-1439.
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IEICE Transactions. 01/2010; 93-A:636-639.
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IEICE Transactions. 01/2010; 93-A:1248-1250.
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IEEE Trans. Mob. Comput. 01/2010; 9:317-332.
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IEEE Trans. Parallel Distrib. Syst. 01/2010; 21:1851-1866.
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IEEE Transactions on Signal Processing. 01/2009; 57:1630-1633.
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IEEE Transactions on Signal Processing. 01/2009; 57:260-269.
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EURASIP J. Adv. Sig. Proc. 01/2009; 2009.
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 19-24 April 2009, Taipei, Taiwan; 01/2009
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IEEE Transactions on Signal Processing. 01/2009; 57:752-763.
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ABSTRACT: In this correspondence, based on an alternative derivation of the Pisarenko harmonic decomposition (PHD) method, a new asymptotically unbiased estimator for the frequency of a single real tone in white noise is devised with the use of novel sample covariance expressions. Furthermore, extension to sample covariances with higher lags for performance enhancement is investigated while a simple and effective scheme is suggested to resolve the corresponding frequency ambiguity problem. The variance of the modified Pisarenko's method is also derived, which is then utilized to find the best estimate among all admissible solutions from various sets of sample covariances. Computer simulations are included to corroborate the theoretical development and to demonstrate that the proposed approach outperforms several existing low-complexity frequency estimators in terms of nearly uniform performance and estimation accuracy.
IEEE Transactions on Signal Processing 08/2008; · 2.63 Impact Factor
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ABSTRACT: Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability of physical reality. This paper explores efficient use of mobile sensors to address the limitations of static WSNs in target detection. We propose a data fusion model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.
Distributed Computing Systems, 2008. ICDCS '08. The 28th International Conference on; 07/2008
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ABSTRACT: Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent QoS requirements including high detection probability, low false alarm rate and bounded detection delay. Although a dense all-static network may initially meet these QoS requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance within a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.
Quality of Service, 2008. IWQoS 2008. 16th International Workshop on; 07/2008
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ABSTRACT: For a positioning system with L sensors, a maximum of L(L-1)/2 distinct time-difference-of-arrival (TDOA) measurements, which are referred to as the full TDOA set, can be obtained. In this paper, closed-form expressions regarding optimum conversion of the full TDOA set to the nonredundant TDOA set, which corresponds to (L-1) TDOA measurements with respect to a common reference receiver, in the case of white signal source and noise, are derived. The most interesting finding is that optimum conversion can be achieved via the standard least squares estimation procedure. Furthermore, the Cramer-Rao lower bound for TDOA-based positioning is produced in closed-form, which will be useful for optimum sensor array design.
IEEE Transactions on Signal Processing 07/2008; · 2.63 Impact Factor
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IEEE Transactions on Information Theory. 01/2008; 54:468-472.
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IEEE Transactions on Signal Processing. 01/2008; 56:2614-2620.
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Signal Processing. 01/2008; 88:1852-1857.
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IEEE Transactions on Signal Processing. 01/2008; 56:3351-3356.
