APPLYING MACHINE LEARNING FOR COP/CTP DATA FILTERING
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Authors
Goh, Wei Ting
Subjects
machine learning
Kalman Filter
state estimation
data fusion
Kalman Filter
state estimation
data fusion
Advisors
Blais, Curtis L.
Garza, Victor R.
Date of Issue
2022-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Accurate tracks and targeting are key to providing decision-makers with the confidence to execute their missions. Increasingly, multiple intelligence, surveillance, and reconnaissance (ISR) assets across different intelligence sources are being used to increase the accuracy of track location, resulting in the need to develop methods to exploit heterogeneous sensor data streams for better target state estimation. One of the algorithms commonly used for target state estimation is the Kalman Filter (KF) algorithm. This algorithm performs well if its covariance matrices are accurate approximations of the uncertainty in sensor measurements. Our research complements the artificial intelligence/machine learning (AI/ML) efforts the U.S. Navy is conducting by quantitatively assessing the potential of using an ML model to predict sensor measurement noise for KF state estimation. We used a computer simulation to generate sensor tracks of a single target and trained a neural network to predict sensor error. The hybrid model (ML-KF) was able to outperform our baseline KF model that uses normalized sensor errors by approximately 20% in target position estimation. Further research in enhancing the ML model with external environment variables as inputs could potentially create an adaptive state estimation system that is capable of operating in varied environment settings.
Type
Thesis
Description
Student Thesis (NPS NRP Project Related)
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
NPS Naval Research Program
This project was funded in part by the NPS Naval Research Program.
This project was funded in part by the NPS Naval Research Program.
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.