A study is performed of several multiple model tracking filter architectures that do not employ a Markov Switching
Matrix in its weighting mathematics. The Markov Switching Matrix which is common to multiple model tracking
filters does not have an "optimum" rule for defining its constituent probabilities. The only real constraint on
the probabilities is that each row of the matrix must add to unity. The other general rule is that the diagonal
elements should be "close to unity" and the off-diagonal terms should be correspondingly "small". Other than
these constraints, values are typically selected by observing the filter tracking performance over a wide set
of trajectory types and target dynamics. Several architectures are presented and their tracking performance
discussed. Comparisons are made with the performance of a conventional IMM for the same data.
In this note we introduce the idea of adaptive scheduling based on a cost function methodology. As the warfare environment becomes more complex, individual sensor resources are stretched, and the usage of the sensors has grown. In a multi-ship multi-platform environment, one has the potential to share information across platforms. This would dramatically increase the strategic and tactical picture available to mission planners and commanders at all force levels. In order to accomplish this mission, the sensors must all be coordinated so adaptability and multi-force tasking can be accomplished with netted sensors. Adaptive sensor management expands group capabilities by freeing up resources such as dwells/energy management. Savings arise by effective usage of tracking resources by revisiting threats with radar resources only when needed. This can be done by introducing analytic cost functions of the revisit time that enable one to minimize revisit time while maintaining error within acceptable bounds.
A particular method of detecting unresolved targets using simulated generic monopulse radar data is examined in detail. The system is assumed to be incorrectly calibrated i.e. the decision boundary is calculated based on erroneous values governing the hypothesis that only a single target is present in the range cell. The system performance is analyzed under varying values for target ranges, angles between the beam pointing direction and the actual off-boresight angle of the targets, waveform power and number of pulses. Design strategies are advanced to maintain good detection probabilities under conditions of miscalibrated decision boundaries.
Discussed is a number of mechanisms for complex systems that can lead to unexpected behavior. All are related to graph theoretic models of interactions. In addition, the theory of random interval graphs can be applied to the characterization of simultaneously occurring variable finite length events. The theory is sufficiently general to provide simple parametric description of the natural relationships between a system's task processing rate and associated decision and communication rates required to control it. One can estimate conditions which induce temporal decoupling and provide general insight into control methodologies which can be used to avoid decoupling. The usage of intervals to characterize resource allocation problems has widespread applications to processing and decision making algorithms which consumed finite bounded time intervals. Finally, various aspects of track quality are discussed as a measure of the strength of interaction.
KEYWORDS: Target detection, Radar, Silicon, Calibration, Signal to noise ratio, Detection and tracking algorithms, Sensors, Infrared search and track, Warfare, Signal processing
A particular method of detecting unresolved targets using simulated monopulse radar data is examined in detail. The system is assumed to be correctly calibrated i.e. the decision boundary is calculated based on the true values governing the hypothesis that only a single target is present in the range cell. The system performance is analyzed under varying values for target ranges, angles between the beam pointing direction and the actual off-boresight angle of the targets, waveform power and number of pulses. It is shown that these parameters have a pronounced impact on the Boundary, Metric and Decision Surfaces. The False Alarm probability for a single target as a function of waveform power is considered, as also are the detection probabilities when two targets are present. The important issue of locating the decision point on the Boundary Surface is briefly discussed.
KEYWORDS: Lithium, Detection and tracking algorithms, Filtering (signal processing), Data analysis, Lutetium, Data processing, Network centric warfare, Data fusion, Signal processing, Information operations
What underpins this vision as axiomatic is the mantra information is power. Besides the necessary requirement of information exchange networks with sufficient bandwidth and computational power to treat the data being passed around the network; algorithms are required to make sense of the data. It is estimation algorithms that turn the straw (data) into gold (information). Both proper execution and improvements in estimation algorithms are the enabling technology that facilitates the formation and usage of data across the envisioned warfare networks. We focus on some of the requirements that are driving the formation of these networks from a surface navy perspective in terms of estimation. We also discuss how these requirements focus the design of potentially new algorithms. We also discuss some of the crucial issues that may drive future requirements and algorithms.
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