Mr. Evans A. Criswell Profile

Evans A. Criswell

at Univ of Alabama in Huntsville

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Author

Publications (3)

Proceedings Article | 17 March 2008 Paper

A heterogeneous sensor network simulation system with integrated terrain data for real-time target detection in 3D space

Hong Lin, Steve Tanner, John Rushing, Sara Graves, Evans Criswell

Proceedings Volume 6974, 69740A (2008) https://doi.org/10.1117/12.784277

KEYWORDS: Sensors, Sensor networks, Computer simulations, Position sensors, Target detection, Binary data, Data fusion, 3D acquisition, Detection and tracking algorithms, System integration

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Large scale sensor networks composed of many low-cost small sensors networked together with a small number of high fidelity position sensors can provide a robust, fast and accurate air defense and warning system. The team has been developing simulations of such large networks, and is now adding terrain data in an effort to provide more realistic analysis of the approach. This work, a heterogeneous sensor network simulation system with integrated terrain data for real-time target detection in a three-dimensional environment is presented. The sensor network can be composed of large numbers of low fidelity binary and bearing-only sensors, and small numbers of high fidelity position sensors, such as radars. The binary and bearing-only sensors are randomly distributed over a large geographic region; while the position sensors are distributed evenly. The elevations of the sensors are determined through the use of DTED Level 0 dataset. The targets are located through fusing measurement information from all types of sensors modeled by the simulation. The network simulation utilizes the same search-based optimization algorithm as in our previous two-dimensional sensor network simulation with some significant modifications. The fusion algorithm is parallelized using spatial decomposition approach: the entire surveillance area is divided into small regions and each region is assigned to one compute node. Each node processes sensor measurements and terrain data only for the assigned sub region. A master process combines the information from all the compute nodes to get the overall network state. The simulation results have indicated that the distributed fusion algorithm is efficient enough so that an optimal solution can be reached before the arrival of the next sensor data with a reasonable time interval, and real-time target detection can be achieved. The simulation was performed on a Linux cluster with communication between nodes facilitated by the Message Passing Interface (MPI). The input target information for the simulations is a set of modified target track data generated from a realistic theater level air combat simulation. The probability of detection (POD), false alarm rate (FAR), and average deviation (AVD) are used in evaluating the network performance.

Proceedings Article | 10 May 2007 Paper

Architecture for an integrated real-time air combat and sensor network simulation

Evans Criswell, John Rushing, Hong Lin, Sara Graves

Proceedings Volume 6564, 65640E (2007) https://doi.org/10.1117/12.719593

KEYWORDS: Sensors, Computer simulations, Sensor networks, Artificial intelligence, Target detection, Computer architecture, Passive sensors, Radar, Binary data, Databases

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Proceedings Article | 9 April 2007 Paper

Real-time target tracking simulations in large disparate sensor networks

Hong Lin, John Rushing, Sara Graves, Evans Criswell, Steve Tanner

Proceedings Volume 6571, 65710K (2007) https://doi.org/10.1117/12.719608

KEYWORDS: Sensors, Target detection, Sensor networks, Position sensors, Data fusion, Binary data, Computer simulations, Detection and tracking algorithms, Data processing, Radar

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