To successfully predict the actions of an adversary and develop effective counteractions, knowledge of the enemy's
mission and organization are needed. In this paper, we present new models and algorithms to identify behaviors of
adversaries based on probabilistic inference of two main signatures of behavior: plans (what the enemy wants to do) and
organizations (how the enemy is organized and who is responsible for what). The technology allows extraction,
classification, and temporal tracking of behavior signatures using multi-source data, as well as prescribes intelligence
collection plans to reduce the ambiguity in current predictions.
KEYWORDS: Sensors, Control systems, Detection and tracking algorithms, Radar, Computer simulations, Computer programming, Algorithm development, Monte Carlo methods, Data modeling, Stochastic processes
We consider the sensor management problem arising in air-to-ground tracking of moving targets. The sensing-tracking system includes a radar and a feature-aided tracker. The radar collects target-signature data in high-resolution-radar (HRR) mode. The tracker is using the collected HRR-signature data to create and maintain target-track identification information. More specifically, the tracker is learning target-track profiles from the collected signature data, and is using these profiles to resolve the potential report-to-track or track-to-track association ambiguities. In this paper, we focus on
the management of the HRR-signature data collection. Specifically, the sensor management problem is to determine where to collect signature data on targets in time so as to optimize the utility of the collected data. As with other sensor management problems, determining the optimal data collection is a hard combinatorial problem due to many factors including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics stems in part from the presence of the sensor slew time. A distinguishing feature of the sensor management problem considered here is that the HRR-signature data collected during the learning phase has no immediate value. To optimize the data collections, a sensor manager must look sufficiently far into the future to adequately trade-off alternative plans. Here, we propose some farsighted algorithms, and evaluate them against a sequential scanning and a greedy algorithm. We present our simulation results obtained by applying these algorithms to a problem of managing a single sensor providing HRR-signature data.
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