TY - GEN
T1 - EM-based Gaussian mixture model estimation for GMTI-based tracking using speedboat data
AU - Akselrod, David
AU - McDonald, Michael
AU - Kirubarajan, T.
PY - 2009/11/2
Y1 - 2009/11/2
N2 - In this paper, the problem of detection, classification and tracking of highly manoeuvring boats in sea clutter is considered. The considered problem is challenging due to numerous inherent issues: abrupt direction changes, high level of false alarms, lowered detectability, group movement and re-grouping, among many others. The results of applying a proposed measurement extraction and estimation technique to a set of real data from DRDC-Ottawa trials using Ground Moving Target Indicator (GMTI) radar are described. Real radar data containing a small manoeuvring boat in sea clutter is processed using Expectation Maximization (EM) Gaussian Mixture Model (GMM) based estimation. A trial was undertaken to collect data against highly maneuvering speedboats in the sea. All the data were collected in the GMTI single-channel high-resolution spotlight mode. True data were collected using GPS recording equipment. Real data processing results are presented.
AB - In this paper, the problem of detection, classification and tracking of highly manoeuvring boats in sea clutter is considered. The considered problem is challenging due to numerous inherent issues: abrupt direction changes, high level of false alarms, lowered detectability, group movement and re-grouping, among many others. The results of applying a proposed measurement extraction and estimation technique to a set of real data from DRDC-Ottawa trials using Ground Moving Target Indicator (GMTI) radar are described. Real radar data containing a small manoeuvring boat in sea clutter is processed using Expectation Maximization (EM) Gaussian Mixture Model (GMM) based estimation. A trial was undertaken to collect data against highly maneuvering speedboats in the sea. All the data were collected in the GMTI single-channel high-resolution spotlight mode. True data were collected using GPS recording equipment. Real data processing results are presented.
KW - Expectation Maximization
KW - Gaussian Mixture Model
KW - Measurement extraction
UR - https://www.scopus.com/pages/publications/70350353517
U2 - 10.1117/12.827056
DO - 10.1117/12.827056
M3 - Conference contribution
AN - SCOPUS:70350353517
SN - 9780819477354
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Signal and Data Processing of Small Targets 2009
T2 - Signal and Data Processing of Small Targets 2009
Y2 - 4 August 2009 through 6 August 2009
ER -