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Abstract-A neural extended Kalman filter algorithm was embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which, uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict the correct velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn is used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the results of a neural extended Kalman filter embedded in an interacting multiple model tracking architecture will be shown using a high fidelity model of a phased array radar. Six different targets of varying maneuverability will be tracked. The phased array radar is controlled via Level 4 Data Fusion feedback to the Level 0 radar process. Highly maneuvering threats are a major concern for the Navy and DoD and this technology will help address this issue.

Current line tracker approaches only address resources in the form of memory and processing time requirements. These architecture approaches limit the amount of complex algorithmic processing a line tracker may perform to provide the correct solution. Past approaches include single-hypothesis Kalman filter-based tracking and a-b tracking. Both approaches assume a real time recursive data processing requirement, and are fairly memory intensive. These architectures are not expandable.Recent research has demonstrated the benefits of a multiple hypothesis, multiple model sonar line tracking solution, achieved at significant computational cost. We have developed an adaptive architecture that trades computational resources for algorithm complexity based on environmental conditions. A Fuzzy Logic Rule-Based approach is applied to adaptively assign algorithmic resources to meet system requirements. The resources allocated by the Fuzzy Logic algorithm include (a) the number of hypotheses permitted (yielding multi-hypothesis and single-hypothesis modes), (b) the number of signal models to use (yielding an interacting multiple model capability), (c) a new track likelihood for hypothesis generation, (d) track attribute evaluator activation (for signal to noise ratio, frequency bandwidth, and others), and (e) adaptive cluster threshold control. Algorithm allocation is driven by a comparison of current throughput rates to a desired real time rate.The Fuzzy Logic Controlled (FLC) line tracker, a single hypothesis line tracker, and a multiple hypothesis line tracker are compared on real sonar data. System resource usage results demonstrate the utility ofthe FLC line tracker.

Highly maneuvering threats are a major concern for the Navy and the DoD and the technology discussed in this paper is intended to help address this issue. A neural extended Kalman filter algorithm has been embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict corrections to the velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn are used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the simulation results of a tracking problem using a neural extended Kalman filter embedded in an interacting multiple model tracking architecture are shown. Preliminary results on the 2 nd Benchmark Problem are also given.

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