KEYWORDS: Data modeling, Principal component analysis, Algorithm development, Switches, Monte Carlo methods, Performance modeling, Error analysis, Detection and tracking algorithms, Mathematical modeling, Systems modeling
This paper extends the two set data association performance model developed by Mori, et al to include miss detections and bias. The referenced paper developed an analytical model for the probability of correct association of two data sets, called 'tracks' and 'measurements,' using an optimal 2 dimensional assignment algorithm, where the 'true' objects are distributed uniformly but at random in a circular disk. For these true objects, measurements are obtained by adding independent random errors with the same covariance. Tracks are obtained in the same way except a different, fixed covariance is used. Finally, one of the data sets includes an additional distribution of random points, considered 'false alarms.' This paper extends their results to obtain an analytical model that accounts for bias between the data sets and missed detections in either data set. The analytical model is useful in assessing the impact of system requirements for sensor sensitivity, random error and inter-sensor bias error on measurement-to- measurement, measurement-to-track or track-to-track association.
Jon Magnuson, Mitchell Troy, Mark Gibney, Kenneth Krall, Jon Tindall, Bradley Flanders, Michael Kovacich, David McIntyre, William Lutjens, Nielson Schulenburg
KEYWORDS: Sensors, Detection and tracking algorithms, Algorithm development, Data processing, Satellites, Missiles, Software development, Data centers, Surveillance, Lead
The Midcourse Space Experiment program will launch a satellite with several optical surveillance sensors onboard that will observe targets launched separately in dedicated and cooperative target programs. The satellite is scheduled to be launched in 1994 and the targets will be observed in several missions over the ensuing eighteen months. The Early Midcourse Target Experiments Team is developing ground based software that will process data to collect target signature phenomenology and demonstrate key surveillance system functions of the IR sensors during the early midcourse phase of a ballistic missile trajectory. Satellite sensor data will be transmitted to the ground and hosted at the Early Midcourse Data Analysis Center (EMDAC). The Early Midcourse Data Reduction and Analysis Workstation (EMDRAW) is a testbed for the algorithm chain of software modules which process the data from end to end, from Time Dependent Processing through object detection and tracking to discrimination. This paper will present the EMDRAW testbed and the baseline algorithm chain.
KEYWORDS: Target detection, Target acquisition, Detection and tracking algorithms, Sensors, Logic, Signal detection, Monte Carlo methods, Performance modeling, Photodynamic therapy, Pulmonary function tests
This paper presents an analytical performance model, verified by Monte Carlo simulation, that predicts the track acquisition performance of a simple, single sensor tracking algorithm. The tracking algorithm consists of a constant velocity Kalman filter, a pure splitting data association algorithm and an M/N/K promotion algorithm. The M/N/K promotion algorithm declares a track to be firm if it receives M hits within N frames of track start and deletes a firm track if it receives K misses in a row. Track acquisition is evaluated in terms of the expected number of frames to declare a firm track on a real target and the spatial density of false tracks that are declared to be firm. The expected time to firm track is derived by calculating the first passage time for the Markov chain modeling target track acquisition, and false track generation is modeled as a multi-type branching process.
KEYWORDS: Detection and tracking algorithms, Signal processing, Data processing, Sensors, Satellites, Filtering (signal processing), Fourier transforms, Signal to noise ratio, Target detection, Electronic filtering
This paper analyzes the performance of a version of the multiple hypothesis tracking concept applied to the group-to-object tracking problem for a pair of passive, scanning, space-based sensors. Group-to-object tracking is the process of tracking clusters of unresolved or nearly resolved objects as a group and then when individual objects become clearly resolved, tracking each object separately. An n scan back MHT algorithm, based on the work by Reid, is used to manage the scan-to-scan association process. It uses the A* search algorithm to find the best hypothesis and its statistically equivalent neighbors, based on their likelihood scores. An analysis is made of the performance and cost of the algorithm as a function of key algorithm parameters.
This paper describes an application of Bayesian Networks, or Influence Diagrams, to the multitarget tracking
problem of a single, angles only, scanning sensor. The Bayesian Network combines the continuous track state
vectors and discrete report-to-track association hypotheses into one network which is then used to perform track
state vector prediction and update, and to generate, score and prune association hypotheses. The advantages of
operating on the network are discussed via an example in which a track resolves into two tracks. The example
demonstrates that the network operations provide a highly flexible, numerically stable, computationally efficient.
mechanism for calculating the state vectors, covariances and intertrack correlations of the resolved tracks. It
is shown that these intertrack correlations, which are somewhat cumbersome to maintain in the usual track
filter formulations, are automatically maintained in the network formulation and can improve track accuracy and
resolution.
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