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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.
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