Improved Maximum Likelihood Estimation of Target Position in Wireless Sensor Networks using Particle Swarm Optimization

Noel, Mathew Mithra; Joshi, P.P.; Jannett, T.C.

doi:10.1109/itng.2006.72

Cited by 29 publications

(24 citation statements)

References 11 publications

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“…As an example, consider that in an indoor transmission, the received node identified RSSI to be −93dBm. By looking this up on a pre-calculated table, the mote can determine the distance of the transmitting mote to be [10,13,36,37]. In fact, if the mote relied only on the RSSI value, considering the number of unique distances that each of the RSSI value would provide, we get a histogram similar to the one seen in Fig.…”

Section: Analysis Of Measurements and Curve Fittingmentioning

confidence: 99%

“…Additional environmental factors such as temperature and humidity have been shown to interfere with RSSI readings as well [11]. So due to a strong non-linear characteristic of RSSI, any localization or distance estimation method that relies on previously measured RSSI fingerprint levels, may fail even for approaches that apply filters or signal processing [12][13][14]. Comprehensive studies of indoor RSSI have been conducted in [10,15] where the authors show that the only way to improve the accuracy would be through a more complex model of the RSSI behavior.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

Oliveira¹,

Raju²,

Agrawal³

2012

NPA

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The received signal strength indicator (RSSI) and the link quality indicator (LQI) are the metrics that are commonly available in commercial off-the-shelf (COTS) sensor hardware. The former has been widely regarded as the main source for distance estimation and node localization. However, experimentally RSSI has been shown to behave in an inconsistent manner, even in ideal scenarios, and serve at best as bounds for distances. The latter is effectively a measure of chip error rate, and can be used to identify higher quality transmissions, and the combination RSSI/LQI can be expected to make more precise estimates with the tradeoff of increased delay and estimation cost. In this paper, we describe our distance estimation system that uses these two metrics and test our hypothesis purely through experimental measurements using sensor nodes. Results indicate that such a combination of metrics can be used to provide a tighter bound on the range of estimated distances. We then quantify the improvement in distance estimation by relying on these two metrics. Through a unique classification using fuzzy logic and TBM, we developed an algorithm that is capable of precise distance estimation within the range of 100cm to 400cm, on at least 80% of the times while reaching accuracy as high as 100%.

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Section: Analysis Of Measurements and Curve Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

Oliveira¹,

Raju²,

Agrawal³

2012

NPA

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“…The literature on these problems goes back to radar systems (see Abdel-Samad and Tewfik, 1999), where localization was mostly performed via beam-forming methods. Existing localization algorithms for WSNs can be divided into two general classes: those based on energy readings E i (Kaplan et al, 2001;Li et al, 2002;Sheng and Hu, 2003;Blatt and Hero, 2006) and those based on binary decisions Y i (Niu and Varshney, 2004;Noel et al, 2006;Ermis and Saligrama, 2006). Li et al (2002) used non-linear least squares to localize the target, assuming an isotropic exponentially decaying signal model.…”

Section: Related Workmentioning

confidence: 99%

Robust Target Localization From Binary Decisions in Wireless Sensor Networks

Katenka

Levina²,

Michailidis³

2008

Technometrics

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Wireless sensor networks (WSN) are becoming an important tool in a variety of tasks, including monitoring and tracking of spatially occurring phenomena. These networks offer the capability of densely covering a large area, but at the same time are constrained by the limiting sensing, processing and power capabilities of their sensors. In order to complete the task at hand, the information collected by the sensor nodes needs to be appropriately fused. In this paper, we study the problems of estimating the location of a target and estimating its signal intensity. The proposed algorithms are based on the local vote decision fusion (LVDF) mechanism, where sensors first correct their original decisions using decisions of neighboring sensors. These corrected decisions are more accurate, robust, and improve detection; however, they are correlated, which renders maximum likelihood estimation intractable. We adopt a pseudo-likelihood formulation and examine several variants of localization and signal estimation algorithms based on original and corrected decisions using direct optimization methods as well as an EM approach. Uncertainty assessments about the parameters of interest are provided using a parametric bootstrap technique. An extensive simulation study of the developed algorithms along with several benchmarks establishes the overall superior performance of the LVDF based algorithms, especially in low signal-to-noise ratio environments. Extensions to tracking moving targets and localizing multiple targets are also considered.

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“…Thus, hybrid optimization schemes that combine both stochastic and deterministic schemes to achieve high convergence rates and avoid local traps are of interest. The GPSO algorithm combines a PSO algorithm used for global exploration with a gradient-based scheme used for accurate local exploration [23]. The PSO algorithm is used to go near the vicinity of a good local minimum and then the gradientbased local search scheme is used to find the local minimum accurately.…”

Section: L+ad(mentioning

confidence: 99%

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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Improved Maximum Likelihood Estimation of Target Position in Wireless Sensor Networks using Particle Swarm Optimization

Cited by 29 publications

References 11 publications

Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

Accurate Distance Estimation Using Fuzzy based combined RSSI/LQI Values in an Indoor Scenario: Experimental Verification

Robust Target Localization From Binary Decisions in Wireless Sensor Networks

Intelligent Control for Future Autonomous Distributed Sensor Systems

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