Dr. Thuraiappah Sathyan Profile

Dr. Thuraiappah Sathyan

Post Doctoral Fellow - Wireless Localization at CSIRO

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Publications (8)

Proceedings Article | 16 April 2008 Paper

Efficient data association for move-stop-move target tracking

T. Sathyan, Mike McDonald, T. Kirubarajan

Proceedings Volume 6969, 696917 (2008) https://doi.org/10.1117/12.779403

KEYWORDS: Detection and tracking algorithms, Target detection, Radar, Motion models, Algorithm development, Monte Carlo methods, Computer simulations, Motion measurement, Binary data, Environmental sensing

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Proceedings Article | 25 September 2007 Paper

Computationally efficient assignment-based algorithms for data association for tracking with angle-only sensors

T. Sathyan, A. Sinha, T. Kirubarajan

Proceedings Volume 6699, 66990K (2007) https://doi.org/10.1117/12.734360

KEYWORDS: Sensors, Detection and tracking algorithms, Darmstadtium, Picosecond phenomena, Target detection, Computer simulations, Filtering (signal processing), Monte Carlo methods, Computing systems, Passive sensors

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In this paper we describe computationally efficient assignment-based algorithms to solve the data association problem in synchronous passive multisensor tracking systems. A traditional assignment-based solution to this problem is to solve the measurement-to-measurement association using multidimensional (S-dimensional or SD with S sensors) assignment formulation and the measurement-to-track association using two-dimensional assignment formulation. Even though this solution has been proven to be effective, it is computationally very expensive. One of the reasons is that in calculating the assignment cost of each possible candidate association one requires to find the maximum likelihood (ML) estimate of the unknown target state. The algorithms proposed in this paper use prior information of the targets that are being tracked to reduce the requirement for the costly ML estimation. The first algorithm is similar to the traditional two step technique except that it uses the predicted track information to avoid building the whole assignment tree in the measurement-to-measurement association. In particular, based on the predicted track information first validation gates are constructed for every target. Then, when forming the assignment tree, only the branches connecting measurements that satisfy the validation gate requirement are constructed. The second algorithm is a one-step algorithm in that it directly assigns the measurements to the tracks. We pose the data association problem as an (S + 1)-D assignment with the first dimension being the predicted state information of the tracks, and the rest of the S dimensions are the lists of measurements from the sensors. The costs of each possible (S + 1)-tuple are calculated based on the predicted track information, hence, the requirement for an ML estimate is eliminated. Further, we show that when the target maneuvers are not very high, and when the sensor measurements are uncorrelated the (S+1)-D assignment approximately decomposes into S individual 2-D assignments, resulting in huge computational savings.

Proceedings Article | 7 May 2007 Paper

Two solutions to the localization using time difference of arrival problem

T. Sathyan, T. Kirubarajan

Proceedings Volume 6567, 656704 (2007) https://doi.org/10.1117/12.719068

KEYWORDS: Sensors, Monte Carlo methods, Chemical elements, Receivers, Global Positioning System, Unmanned aerial vehicles, Intelligence systems, Optimization (mathematics), MATLAB, Computer engineering

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Proceedings Article | 25 May 2006 Paper

Issues in resource management with applications to real-world problems

David Castañón, Ron Mahler, Kenneth Hintz, Jim Reich, Ivan Kadar, M. Farooq, T. Kirubarajan, R. Tharmarasa, T. Sathyan, A. Sinha

Proceedings Volume 6235, 62351O (2006) https://doi.org/10.1117/12.694899

KEYWORDS: Sensors, Data fusion, Sensor networks, Cameras, Sensing systems, Algorithm development, Computing systems, Detection and tracking algorithms, Information fusion, Error analysis

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Proceedings Article | 19 May 2006 Paper

Weiss-Weinstein lower bound for maneuvering target tracking

T. Sathyan, M. Hernandez, A. Sinha, T. Kirubarajan

Proceedings Volume 6236, 62360G (2006) https://doi.org/10.1117/12.667792

KEYWORDS: Motion models, Matrices, Process modeling, Sensors, Switching, Astatine, Switches, Computer engineering, Detection and tracking algorithms, Radar

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Proceedings Article | 10 May 2006 Paper

Intelligence-aided multitarget tracking for urban operations - a case study: counter terrorism

T. Sathyan, K. Bharadwaj, A. Sinha, T. Kirubarajan

Proceedings Volume 6201, 62010M (2006) https://doi.org/10.1117/12.669409

KEYWORDS: Roads, Kinematics, Detection and tracking algorithms, Strategic intelligence, Motion models, Surveillance, Buildings, Sensors, Chromium, Image segmentation

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Proceedings Article | 25 May 2005 Paper

Passive geolocation and tracking of an unknown number of emitters

T. Sathyan, A. Sinha, T. Kirubarajan

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.601373

KEYWORDS: Sensors, Detection and tracking algorithms, Unmanned aerial vehicles, Darmstadtium, Monte Carlo methods, Surveillance, Algorithm development, Filtering (signal processing), Receivers, Target recognition

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Proceedings Article | 9 August 2004 Paper

Geolocation of multiple emitters in the presence of clutter

Thuraiappah Sathyan, Thiagalingam Kirubarajan, Abhijit Sinha

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.542259

KEYWORDS: Sensors, Unmanned aerial vehicles, Motion models, Detection and tracking algorithms, Monte Carlo methods, Surveillance, Filtering (signal processing), Motion estimation, Electronic filtering, Electromagnetism

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Showing 5 of 8 publications

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