Kalman Filtering with Nonlinear State Constraints

An analytic method was developed by D. Simon and T. L. Chia to incorporate linear state equality constraints into the Kalman filter. When the state constraint was nonlinear, linearization was employed to obtain an approximately linear constraint around the current state estimate. This linearized constrained Kalman filter is subject to approximation errors and may suffer from a lack of convergence. We present a method that allows exact use of second-order nonlinear state constraints. It is based on a computational algorithm that iteratively finds the Lagrangian multiplier for the nonlinear constraints. Computer simulation results are presented to illustrate the algorithm.     

Assembling a distributed fused information-based human-computercognitive decision making tool

A human presented with a variety of displays is expected to fuse data to obtain information. An effective presentation of information would assist the human in fusing data. This paper describes a multisensor-multisource information decision making tool designed to augment human cognitive fusion     

Resource Management Coordination with Level 2/3 Fusion Issues and Challenges [Panel Report]

Information fusion system designs require sensor and resource management (SM) for effective and efficient data collection, processing, and dissemination. Common Level 4 fusion sensor management (or process refinement) inter-relations with target tracking and identification (Level 1 fusion) have been detailed in the literature. At the ISIF Fusion Conference, a panel discussion was held to examine the contemporary issues and challenges pertaining to the interaction between SM and situation and threat assessment (Level 2/3 fusion). This summarizes the key tenants of the invited panel experts. The common themes were: (1) Addressing the user in system control, (2) Determining a standard set of metrics, (3) Evaluating fusion systems to deliver timely information needs, (4) Dynamic updating for planning mission time-horizons, (5) Joint optimization of objective functions at all levels, (6) L2/3 situation entity definitions for knowledge discovery, modeling, and information projection, and (7) Addressing constraints for resource planning and scheduling.