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Journal ArticleDOI

Kalman filter versus IMM estimator: when do we need the latter?

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TLDR
In this paper, a performance comparison between a Kalman filter and the interacting multiple model (IMM) estimator is carried out for single-target tracking, and it is shown that above a certain maneuvering index an IMM estimator was preferred over a KF to track the target motion.
Abstract
In this paper, a performance comparison between a Kalman filter and the interacting multiple model (IMM) estimator is carried out for single-target tracking. In a number of target tracking problems of various sizes, ranging from single-target tracking to tracking of about a thousand aircraft for air traffic control, it has been shown that the IMM estimator performs significantly better than a Kalman filter. In spite of these studies and many others, the condition under which an IMM estimator is desirable over a single model Kalman filter versus an IMM estimator are quantified here in terms of the target maneuvering index, which is a function of target motion uncertainty, measurement uncertainty, and sensor revisit interval. Using simulation studies, it is shown that above a certain maneuvering index an IMM estimator is preferred over a Kalman filter to track the target motion. These limits should serve as a guideline in choosing the more versatile, but somewhat costlier, IMM estimator over a simpler Kalman filter.

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Citations
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Journal ArticleDOI

Survey of maneuvering target tracking. Part V. Multiple-model methods

TL;DR: A comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty is presented in this article, which is centered around three generations of algorithms: autonomous, cooperating, and variable structure.
Journal ArticleDOI

Multiple-model probability hypothesis density filter for tracking maneuvering targets

TL;DR: In this paper, a multiple-model implementation of the probability hypothesis density (PHD) filter is proposed, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMC) methods.
Proceedings ArticleDOI

A Multiple Model Probability hypothesis density filter for tracking maneuvering targets

TL;DR: In this paper, a multiple model implementation of the Probability Hypothesis Density (PHD) filter, which approximates the PHD by a set of weighted random samples propagated over time using Sequential Monte Carlo methods, is proposed.
Book ChapterDOI

Multitarget Multisensor Tracking

TL;DR: Various multitarget-multisensor tracking algorithms to handle state estimation, data association, track initialization, spatial clutter intensity estimation, debaising, and multisensor fusion in centralized/distributed/decentralized architecture are discussed in detail, including their quantitative and qualitative merits.
Journal ArticleDOI

A survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

TL;DR: In this paper, a comprehensive review of the vision-based See and Avoid problem for unmanned aircraft is provided, where the unique problem environment and associated constraints are detailed, followed by an in-depth analysis of visual sensing limitations.
References
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Journal ArticleDOI

The interacting multiple model algorithm for systems with Markovian switching coefficients

TL;DR: In this paper, a novel approach to hypotheses merging is presented for linear systems with Markovian switching coefficients (dynamic multiple model systems) which is necessary to limit the computational requirements.
Journal ArticleDOI

Interacting multiple model methods in target tracking: a survey

TL;DR: The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems, with special attention to the assumptions underlying each algorithm and its applicability to various situations.
Journal ArticleDOI

Unbiased converted measurements for tracking

TL;DR: In this article, the exact compensation for the bias in the classical polar-to-cartesian conversion is shown to be multiplicative and to depend on the statistics of the cosine of the angle measurement errors.
Journal ArticleDOI

Benchmark for radar allocation and tracking in ECM

TL;DR: In this paper, a benchmark problem for tracking maneuvering targets is presented, where the best tracking algorithm is the one that minimizes a weighted average of the radar energy and radar time, while satisfying a constraint of 4% on the maximum number of lost tracks.