.png){kind=icon}
.png){kind=icon}
Return
Underwater Minefield Detection in Clutter Data Using Spatial Point-Process Models
.png){kind=icon}
PRE
AI
DOI：10.1109/JOE.2015.2493598.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we study the problem of detection of underwater minefields amidst dense clutter as that of statistical inference under a spatial point-process model. Specifically, we model the locations ( mine and clutter) as samples of a Thomas point process with parent locations representing mines and children representing clutter. Accordingly, the parents are distributed according to a homogeneous Poisson process and, given the parent locations, the children are distributed as independent Poisson processes with intensity functions that are Gaussian densities centered at the parents. This provides a likelihood function for parent locations given the observed clutter ( children). Under this model, we develop a framework for penalized maximum-likelihood (ML) estimation of model parameters and parent locations. The optimization is performed using a combination of analytical and Monte Carlo methods; the Monte Carlo part relies on a birth-death-move procedure for adding/removing points in the parent set. This framework is illustrated using both simulated and real data sets, the latter obtained courtesy of Naval Surface Warfare Center Panama City Division (NSWC-PCD), Panama City, FL, USA. The results, evaluated both qualitatively and quantitatively, underscore success in estimating parent locations and other parameters, at a reasonable computation cost.
Keywords:
Maximum-likelihood estimation
simulated annealing
spatial point process
synthetic aperture sonar
Thomas process
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.3
Papers:
2.6K
Citations:
7.4K
Organization
View more organizations