The concept of trackability is intimately related to the establishment of optimal trade-offs between the nosiness of
the environment, due to poor sensing, and the randomness of the kinematics of the phenomena being examined,
due to poor knowledge of their behaviors.
Classically, a sensor system receives low level data in the form of numerical or analog signals and then through
signal processing produces a high level observation suitable for a higher level state estimation process. These two
phases may be further refined into a hierarchical chain of "tiers", where observations at each level are obtained
through the computation of a set of properties of the system's estimated state at the lower level.
An important factor that seems to have an impact on the overall ability to track high level phenomena in real
time is the computational complexity of deciding those properties when generating observations between the
tiers. And this complexity characterizes the accuracy of what can be computed within a bounded time frame.
In this paper we intend to investigate the "real time" trackability of phenomena through the analysis of the
complexity of individual models in relation to the computational complexity of computing observations in any
multi-tiered tracking system.
A Process Query System (PQS) is a generic software system that
can be used in tracking applications across a variety of domains.
As in most other tracking systems, multiple hypotheses about which
reports are assigned to which tracks must be maintained. Since the
number of hypotheses that are possible can be exponential in the number
of reports, some technique for managing a pool of the best candidate hypotheses
must be used.
In this paper, we compare a genetic algorithm approach and a hypothesis
clustering approach with the basic top-H pruning policy. Metrics for comparison
include performance accuracy and computational requirements. Simulations show
positive results for both of these approaches and suggest that the clustering approach has
the best overall performance.
Other experiments indicate that the genetic
algorithm technique can converge over time to the ground truth.
User interfaces are important for process modeling and detection systems. This paper discusses the user interface design and implementation for the innovative Process Query System (PQS). Discussion focuses on the Hidden Markov Model (HMM) editor, model validation, process query types, and result visualization. A log likelihood indicator is used to evaluate and visualize the goodness of model-observation matching. A numerical method to measure the distinguishability between two HMMs is proposed and proved effective. The measurement is performed by estimating misdetection rate using the Monte Carlo simulation method. A visual HMM comparison tool using this method is implemented in the user interface.