In radar tracking, the Preferred Ordering Theorem for updating the state vector in rectangular coordinates using an
Extended Kalman Filter states that the measurement components of a detection should be used sequentially in the order
azimuth first, then elevation, and range last. Such is counterintuitive to a common belief that the most accurate
measurement should be used first, since that is usually range. However, it is observed here that the theorem can lose its
efficacy as a track converges - and that the expected value of an EKF update is not always well-defined. An "extension"
is therefore given, which is dubbed the DKF after Desargues, since it is based on an analysis involving projective
geometry. With this approach the conditional update of a track in rectangular coordinates becomes well defined in the
sense that a preferred order is obviated, and it can now be updated using a range or angle observation, separately or
sequentially in either order, with less error. In this presentation the basic issues are illustrated, and the DFK is defined
and contrasted with the EKF for the two-dimensional motionless target case.
This paper reports on results from an ongoing project to develop methodologies for representing and managing multiple, concurrent levels of detail and enabling high performance computing using parallel arrays within distributed object-based simulation frameworks. At this time we present the methodology for representing and managing multiple, concurrent levels of detail and modeling accuracy by using a representation based on the Kalman approach for estimation. The Kalman System Model equations are used to represent model accuracy, Kalman Measurement Model equations provide transformations between heterogeneous levels of detail, and interoperability among disparate abstractions is provided using a form of the Kalman Update equations.
KEYWORDS: Data communications, Data modeling, Computer simulations, C++, Performance modeling, Parallel processing, Device simulation, Data fusion, Information fusion, Computing systems
This paper reports on results from an ongoing project to develop methods for representing and managing multiple, concurrent levels of modeling detail and enabling high performance computing, namely parallel processing, within object-based simulation frameworks such as HLA. We present here the interface structure and runtime support service concept for using parallel arrays for high performance computing within distributed object-based simulation frameworks. The approach employs a distributed array descriptor, which can be a basis for extending the HLA standard to provide support for efficiently sharing very large data arrays or sub-arrays among federates. The goal is to reduce communications overhead and thereby improve simulation performance involving C4ISR models that require, for example, interpolation and extrapolation of large data sets, such as those that naturally occur for overlay, coupling, and fusion of phenomenology information in multi- sensor networks.
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