NDLI: Can this target be tracked?

Content Provider	IEEE Xplore Digital Library
Author	Schoenecker, S. Willett, P. Bar-Shalom, Y.
Copyright Year	2015
Description	Author affiliation: Div. Newport, NUWC, Newport, RI, USA (Schoenecker, S.) \|\| Electr. Eng., Univ. of Connecticut, Storrs, CT, USA (Willett, P.; Bar-Shalom, Y.)
Abstract	In the field of target tracking, it is often assumed that as long as a target is present and detectable, it should be “trackable.” Any failure to track a target that is generating detectable measurements is assumed to be due to a sub-optimal tracking algorithm or perhaps an algorithm that is not properly “tuned.” There seems to exist the idea that “if this knob is turned slightly, or this parameter is adjusted a little, our tracker should be able to follow the target ... ” This work shows that, as should really be expected, there are times when, even though there is a target present that is producing measurements, the output statistical distribution produced by these measurements cannot be differentiated from the output statistical distribution of the too-numerous clutter-generated measurements, and the target simply cannot be tracked. This degree of “trackability” is demonstrated by employing the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT), which is a powerful non-Bayesian algorithm that uses a generalized likelihood ratio test (GLRT) to check for a target in the presence of clutter. For various combinations of measurement dimensionality, amplitude features, and classification features, we treat the ML-PMHT log-likelihood ratio (LLR) as a random variable (RV) transformation and then use extreme-value theory to calculate the probability density function (PDF) for the peak point in the LLR due to clutter as well as the PDF of the peak point in the LLR due to a target. In doing so, the tracking problem is reduced to a simple detection problem, making it possible to answer the question, “Can this target be tracked?”.
Starting Page	1732
Ending Page	1739
File Size	738365
Page Count	8
File Format	PDF
e-ISBN	9780982443866
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ISIF
Subject Keyword	Clutter Target tracking Probability density function Volume measurement Random variables Azimuth Statistical distributions extreme value theory Tracking trackability ML-PMHT maximum likelihood
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Extreme-value analysis for mlML-PMHT, Part 2: target trackability

ML-PMHT threshold determination for false track probability using extreme-value analysis

Extreme-value analysis for ML-PMHT, Part 1: threshold determination

The Effect of K-Distributed Clutter on Trackability

A modified PMHT tracking algorithm

Comparing multitarget multisensor ML-PMHT with ML-PDA for VLO targets

A comparison of the ML-PDA and the ML-PMHT algorithms

Parameter Estimation for Censored Exponential Random Variables

Probability of detection and clutter rate estimation in target tracking systems: generalised maximum likelihood approach

Can this target be tracked?

Similar Documents

Extreme-value analysis for mlML-PMHT, Part 2: target trackability

ML-PMHT threshold determination for false track probability using extreme-value analysis

Extreme-value analysis for ML-PMHT, Part 1: threshold determination

The Effect of K-Distributed Clutter on Trackability

A modified PMHT tracking algorithm

Comparing multitarget multisensor ML-PMHT with ML-PDA for VLO targets

A comparison of the ML-PDA and the ML-PMHT algorithms

Parameter Estimation for Censored Exponential Random Variables

Probability of detection and clutter rate estimation in target tracking systems: generalised maximum likelihood approach

Can this target be tracked?