A Finite Point Process Approach to Multi-Target Localization

Abstract

A finite point process approach to multi-target localization from a transient signal is presented. After modeling the measurements as a Poisson point process, we propose a twofold scheme that includes an expectation maximization algorithm to estimate the target locations for a given number of targets and an information theoretic algorithm to select the target model, i.e., number of targets. Similar to the finite point process solution for the multi-target tracking, i.e., the probability hypothesis density filter, the proposed localization scheme does not require solving the data association problem and can account for clutter noise as well as missed detection. The optimal subpattern assignment metric is used to assess the performance and accuracy of the proposed localization algorithm. Implementation of the proposed algorithm on synthetic data yields desirable results. The proposed algorithm is then applied to multi-shooter localization problem using acoustic gunfire detection systems.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA586560

Entities

People

  • Jemin George
  • Lance Kaplan

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Acoustic Waves
  • Algorithms
  • Computational Complexity
  • Computational Science
  • Data Association
  • Detection
  • Detectors
  • False Alarms
  • Filters
  • Filtration
  • Mathematical Filters
  • Multitarget Tracking
  • Probability
  • Probability Hypothesis Density Filters
  • Simultaneous Localization And Mapping
  • Target Tracking
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Mathematical Modeling and Probability Theory.
  • Sensor Fusion and Tracking Systems.