Dr. Richard A. Burne Profile

Dr. Richard A. Burne

Project Leader at Honeywell International Inc

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Author

Publications (5)

Proceedings Article | 27 September 2001 Paper

Self-organizing cooperative sensor network for remote surveillance: target tracking while optimizing the geometry between bearing-reporting sensors and the target

Richard Burne, Ivan Kadar, Anna Buczak

Proceedings Volume 4393, (2001) https://doi.org/10.1117/12.441266

KEYWORDS: Sensors, Surveillance, Error analysis, Acoustics, Unattended ground sensors, Sensor networks, Roads, Magnetic sensors, Optical inspection, Detection and tracking algorithms

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Proceedings Article | 21 February 2001 Paper

Self-organizing cooperative sensor network for remote surveillance: improved target tracking results

Richard Burne, Ivan Kadar, John Whitson, Anna Buczak

Proceedings Volume 4232, (2001) https://doi.org/10.1117/12.417546

KEYWORDS: Sensors, Surveillance, Acoustics, Unattended ground sensors, Kinematics, Sensor networks, Detection and tracking algorithms, Target recognition, Taxonomy, Magnetic sensors

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Proceedings Article | 21 July 2000 Paper

Self-organizing cooperative UGS network for target tracking

Richard Burne, Ivan Kadar, John Whitson, Eitan Eadan

Proceedings Volume 4040, (2000) https://doi.org/10.1117/12.392565

KEYWORDS: Sensors, Acoustics, Unattended ground sensors, Surveillance, Detection and tracking algorithms, Error analysis, Sensor networks, Target detection, Roads, Magnetic sensors

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Proceedings Article | 30 July 1999 Paper

Self-organizing cooperative sensor network for remote surveillance: current results

Richard Burne, Anna Buczak, Yaochu Jin, Vikram Jamalabad, Ivan Kadar, Eitan Eadan

Proceedings Volume 3713, (1999) https://doi.org/10.1117/12.357139

KEYWORDS: Sensors, Surveillance, Target detection, Acoustics, Unattended ground sensors, Error analysis, Sensor networks, Roads, Detection and tracking algorithms, Algorithm development

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Proceedings Article | 7 January 1999 Paper

Self-organizing cooperative sensor network for remote surveillance

Richard Burne, Anna Buczak, Vikram Jamalabad, Ivan Kadar, Eitan Eadan

Proceedings Volume 3577, (1999) https://doi.org/10.1117/12.336957

KEYWORDS: Sensors, Target detection, Surveillance, Unattended ground sensors, Sensor networks, Optimization (mathematics), Relays, Detection and tracking algorithms, Genetic algorithms, Acoustics

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The capabilities of unattended ground sensors (UBSs) have steadily improved and have been shown to be of value in various military missions. Today's UGS are multifunctional, integrated sensor platforms that can detect and locate a wide variety of ground-based and airborne targets. Due primarily to cost and size constraints of these UGS, they have not been widely used for law enforcement surveillance applications. As an alternative to a single, monolithic sensor platform, remote surveillance may be possible with smaller, less obtrusive sensors that work cooperatively together as a network. The objective of this study was to develop algorithms that can optimally organize and adaptively control a network of UGSs in order to achieve a surveillance mission. In the present study, the sensor network, a random distribution of sensors over a surveillance area (emulates airborne sensor deployment), determines an optimal combination of its sensors that will detect multiple targets and consume the lease amount of power. This problem is considered a multiobjective optimization problem to which there is no unique solution. Furthermore, for a linearly increasing number of sensors, the combinatorial search space increases exponentially. To reduce the search space, a novel clustering method was developed based on whether the sensor can sense the target rather than on similarities between the sensors. A genetic algorithm (GA) was used to obtain a quasioptimal solution for the sensor combination problem. To evaluate the effectiveness of the optimization, figures of merit were developed that are applicable to a sensor network tasked with a surveillance problem. Software-simulated data was used to test software implementation of the clustering, optimization and figure of merit functions. The clustering method reduced the search space by an average of ten orders of magnitude. For a sensor population of 100 sensors that was tasked to detect 24 targets, the GA was able to select optimal sets of sensors for detection and minimization of power consumption. The results demonstrate the feasibility of optimally configuring and controlling a network of sensors for remote surveillance applications.

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