A simple and efficient suboptimal multilevel quantization approach in geographically distributed sensor systems

Aziz, Ashraf M.

doi:10.1016/j.sigpro.2008.01.006

Cited by 43 publications

(17 citation statements)

References 26 publications

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“…In addition, it is not guaranteed to converge to the optimum quantization levels. A suboptimum multilevel quantization scheme with improved computational efficiency is proposed by using fuzzy techniques in [23]. Nevertheless, as stated in [23], the disadvantage of this method is that it is generally unknown how to map the local likelihood ratio to a fuzzy set to provide the best detection performance for different scenarios.…”

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confidence: 99%

“…A suboptimum multilevel quantization scheme with improved computational efficiency is proposed by using fuzzy techniques in [23]. Nevertheless, as stated in [23], the disadvantage of this method is that it is generally unknown how to map the local likelihood ratio to a fuzzy set to provide the best detection performance for different scenarios. The authors in [24] consider the distributed detection problem with multilevel quantization in each local sensor.…”

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confidence: 99%

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Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Gao

Guo

et al. 2015

IEEE Trans. Wireless Commun.

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We consider the problem of distributed detection of a mean parameter corrupted by Gaussian noise in wireless sensor networks, where a large number of sensor nodes jointly detect the presence of a weak unknown signal. To circumvent power/bandwidth constraints, a multilevel quantizer is employed in each sensor to quantize the original observation. The quantized data are transmitted through binary symmetric channels to a fusion center where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. The asymptotic performance analysis of the multibit GLRT detector is provided, showing that the detection probability is monotonically increasing with respect to the Fisher information (FI) of the unknown signal parameter. We propose a quantizer design approach by maximizing the FI with respect to the quantization thresholds. Since the FI is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for FI maximization. Numerical results demonstrate that with 2-or 3-bit quantization, the GLRT detector can provide detection performance very close to that of the unquantized GLRT detector, which uses the original observations without quantization.Index Terms-Wireless sensor networks, multilevel quantization, distributed detection, particle swarm optimization algorithm (PSOA).

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confidence: 99%

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Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Gao

Guo

et al. 2015

IEEE Trans. Wireless Commun.

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“…For decentralized target detection by a WSN, one of the key problems is how to quantize the sensor observations. Some one-bit quantization [7] and multi-level quantization schemes [8,9] have been studied for decentralized detection. However, these quantization schemes only work under the assumptions of Gaussian noise and a single unknown parameter.…”

Section: Introductionmentioning

confidence: 99%

Aspect-Aware Target Detection and Localization by Wireless Sensor Networks

Wang

Er-yang

2018

Sensors

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This paper considers the active detection of a stealth target with aspect dependent reflection (e.g., submarine, aircraft, etc.) using wireless sensor networks (WSNs). When the target is detected, its localization is also of interest. Due to stringent bandwidth and energy constraints, sensor observations are quantized into few-bit data individually and then transmitted to a fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to achieve target detection and maximum likelihood estimation of the target location simultaneously. In this context, we first develop a GLRT detector using one-bit quantized data which is shown to outperform the typical counting rule and the detection scheme based on the scan statistic. We further propose a GLRT detector based on adaptive multi-bit quantization, where the sensor observations are more precisely quantized, and the quantized data can be efficiently transmitted to the FC. The Cramer-Rao lower bound (CRLB) of the estimate of target location is also derived for the GLRT detector. The simulation results show that the proposed GLRT detector with adaptive 2-bit quantization achieves much better performance than the GLRT based on one-bit quantization, at the cost of only a minor increase in communication overhead.

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“…Some of these applications are diversity communication systems (El-Ansary et al, 2013;Aziz, 2011a), target detection (Aziz, 2010;El-Ayadi et al, 1996), distributed radar surveillance networks (Aziz, 2014c;Aziz, 2008), wireless sensor networks (Aziz et al, 2011;Aziz, 2011b), biomedical applications (El-Badawy et al, 2014;2013) and target tracking (Aziz, 2013;2011c). An association technique is essential processing in multisensor data fusion systems (Hall, 1992).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Multipath Clustering for Over-the-Horizon Radar

Aziz¹,

Abdelrahman²,

Abouelatta³

2016

American Journal of Applied Sciences

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Over-the-Horizon Radar (OTHR) exploits the refraction of high frequency radiation through the ionosphere layers to detect targets beyond the line-of-sight horizon. Multipath propagation between the radar and the detected targets may results in multiple spatially separated tracks for a single target to be observed at the receiver site. Consequently there is a heavy traffic, especially in case of multiple targets, to be associated and combined if there are tracks represent the same target. In this study, a new method for multipath clustering for OTHR is proposed. The proposed method describes the similarities between all tracks as fuzzy degrees of membership. This method can operate in real-time and can perform clustering and fusion of OTHR tracks with tracks from other sources such as targets reporting global positioning systems and microwave radars. The proposed method has the advantages of less computations and high efficiency compared to conventional fuzzy logic clustering techniques. It has also the advantage of treating all the tracks data at once rather than pairwise. The efficiency of the proposed method is demonstrated using simulated examples.

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A simple and efficient suboptimal multilevel quantization approach in geographically distributed sensor systems

Cited by 43 publications

References 26 publications

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Aspect-Aware Target Detection and Localization by Wireless Sensor Networks

A New Method for Multipath Clustering for Over-the-Horizon Radar

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