Interest in the distribution of processing in unattended ground sensing (UGS) networks has resulted in new technologies and system designs targeted at reduction of communication bandwidth and resource consumption through managed sensor interactions. A successful management algorithm should not only address the conservation of resources, but also attempt to optimize the information gained through each sensor interaction so as to not significantly deteriorate target tracking performance. This paper investigates the effects of Distributed Cluster Management (DCM) on tracking performance when operating in a deployed UGS cluster. Originally designed to reduce communications bandwidth and allow for sensor field scalability, the DCM has also been shown to simplify the target tracking problem through reduction of redundant information. It is this redundant information that in some circumstances results in secondary false tracks due to multiple intersections and increased uncertainty during track initiation periods. A combination of field test data playback and Monte Carlo simulations are used to analyze and compare the performance of a distributed UGS cluster to that of an unmanaged centralized cluster.
Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central “management” node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also simplify multi-target tracking within the cluster.