Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity

10/20/2010
by   Leonid I. Perlovsky, et al.
0

We have developed an efficient algorithm for the maximum likelihood joint tracking and association problem in a strong clutter for GMTI data. By using an iterative procedure of the dynamic logic process "from vague-to-crisp," the new tracker overcomes combinatorial complexity of tracking in highly-cluttered scenarios and results in a significant improvement in signal-to-clutter ratio.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

09/20/2017

Multi-camera Multi-Object Tracking

In this paper, we propose a pipeline for multi-target visual tracking un...
10/02/2021

Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis

We propose an iterative algorithm that computes the maximum-likelihood e...
02/24/2021

Maximum Likelihood Constraint Inference from Stochastic Demonstrations

When an expert operates a perilous dynamic system, ideal constraint info...
08/08/2021

Multi-Target Tracking with Dependent Likelihood Structures in Labeled Random Finite Set Filters

In multi-target tracking, a data association hypothesis assigns measurem...
09/19/2016

The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences

Various applications involve assigning discrete label values to a collec...
08/04/2021

Maximum likelihood thresholds via graph rigidity

The maximum likelihood threshold (MLT) of a graph G is the minimum numbe...
12/15/2010

Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs

Restricted Boltzmann Machines (RBM) have attracted a lot of attention of...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.