Target Location Estimation in Sensor Networks With Quantized Data

Niu, Ruixin; Varshney, Pramod K.

doi:10.1109/tsp.2006.882082

Cited by 252 publications

(260 citation statements)

References 11 publications

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“…MLE is used to solve discovery problems; to obtain accurate distance estimations [67][68][69]. Maximum A Posteriori (MAP) is based on Bayesian theory, when a parameter x to be discovered is based on the outcome of a random variable with known pdf p(s).…”

Section: Pr(b|a)mentioning

confidence: 99%

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Borges

2016

Open Computer Science

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Internet-of-Things (IoT) is the latest buzzword, havings its origins in the erstwhile Sensor Networks. Sensor Networks produce a large amount of data. According to the needs this data requires to be processed, delivered and accessed. This processed data when made available with the physical device location, user preferences, time constraints; generically called as context-awareness; is widely referred to as the core function for ubiquitous systems. To our best knowledge there is lack of analysis of context information fusion for ubiquitous sensor networks. Adopting appropriate information fusion techniques can help in screening noisy measurements, control data in the network and take necessary inferences that can help in contextual computing. In this paper we try and explore di erent context information fusion techniques by comparing a large number of solutions, their methods, architectures and models. All the surveyed techniques can be adapted to the IoT framework.

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Section: Pr(b|a)mentioning

confidence: 99%

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Borges

2016

Open Computer Science

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“…Position estimates obtained using localization can also be used in tracking applications. One promising approach for target localization is a method using binary sensor data for which a maximum likelihood (ML) estimator and its Cramer-Rao lower bound have been derived [19]. In this approach, each sensor makes a binary decision about a target's presence by comparing the measured signal strength to a threshold, and communicates a one-bit message to a fusion center.…”

Section: Maximum Likelihood Target Localization Using Binary Sensor Datamentioning

confidence: 99%

“…The fusion center uses the binary information received from all the sensors, along with a priori information about the positions of the sensors, to localize the target through nonlinear optimization of a highly complex multimodal function. Recently, this approach was extended for localization based on quantized data [20]. These methods are attractive because they facilitate accurate target localization based on the transmission of binary or multibit quantized data, which requires limited communication bandwidth.…”

Section: Maximum Likelihood Target Localization Using Binary Sensor Datamentioning

confidence: 99%

“…These methods are attractive because they facilitate accurate target localization based on the transmission of binary or multibit quantized data, which requires limited communication bandwidth. The ML estimation framework for target localization using binary data presented in [19] and reviewed in the following was utilized in this work.…”

Section: Maximum Likelihood Target Localization Using Binary Sensor Datamentioning

confidence: 99%

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Intelligent Control for Future Autonomous Distributed Sensor Systems

Jannett¹

2007

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Key distributed coordination and control technologies needed for future deployable autonomous distributed systems were investigated. Specific tasks accomplished include: 1) identification and exploration of alternative distributed intelligent system architectures, methods for distributed data fusion, control, and coordination, and strategies that will increase the ability of the field to survive attacks, failures, and accidents, 2) development of new intelligent software agents that facilitate distributed coordination and control, end 3) creation of computer simulations for developing and testing alternative algorithms, considering tradeoffs, and evaluating the ability of the distributed system to achieve its goals when presented with a set of operational challenges. This approach allowed design alternatives to be explored, allowed trade-offs to be exposed, provided insight into the parameters that impact overall system performance, and facilitated the identification of requirements for further development. factual summary of the most significant information. PLEASE DO NOT5e. TASK NUMBER. Enter all task numbers as they 15. SUBJECT TERMS. Key words or phrases appear in the report, e.g. 05; RF0330201; T4112.identifying major concepts in the report.5f. WORK UNIT NUMBER. Enter all work unit numbers as they appear in the report, e.g. 001; 16. SECURITY CLASSIFICATION. Enter security AFAPL30480105.classification in accordance with security classification regulations, e.g. U, C, S, etc. If this form contains 6. AUTHOR(S). Enter name(s) of person(s) classified information, stamp classification level on the responsible for writing the report, performing the top and bottom of this page. research, or credited with the content of the report. The form of entry is the last name, first name, middle 17. LIMITATION OF ABSTRACT. This block must be initial, and additional qualifiers separated by commas, completed to assign a distribution limitation to the e.g. Smith, Richard, J, Jr.abstract. Enter UU (Unclassified Unlimited) or SAR (Same as Report). An entry in this block is necessary if 7. PERFORMING ORGANIZATION NAME(S) AND teasrc st elmtd ADDRESS(ES ABSTRACTKey distributed coordi...

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“…In a simple distributed framework where a parameter of interest is directly estimated at each sensor, distributed estimators based on quantized data were derived [19]; these results rely on the availability of measurement noise statistics. A source localization system where each sensor measures the signal energy, and sends the quantized sensor reading to a fusion node is considered [15]. In this framework, the maximum likelihood (ML) estimation problem based on quantized data was addressed and the Cramer-Rao bound (CRB) was derived for comparison.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Distributed Estimation Using Quantized Data

Kim

2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators. key words: distributed estimation, maximum a posteriori (MAP), quantization, source localization, sensor networks

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Target Location Estimation in Sensor Networks With Quantized Data

Cited by 252 publications

References 11 publications

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Intelligent Control for Future Autonomous Distributed Sensor Systems

Region-Based Distributed Estimation Using Quantized Data

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