&lt;title&gt;Information theoretics for improved tracking and fusion performance&lt;/title&gt;

Liggins, Martin E.; Nebrich, Mark; Chang, Kuo‐Chu

doi:10.1117/12.498652

Cited by 3 publications

(5 citation statements)

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“…equation 57, and Figures 7 -8, and as such is not an optimal association discriminator, contrary to the previous recommendation in the literature 7 . The last observation from Table 1, as alluded to in the discussion for Example 2, is that despite its drawbacks, use of the logarithmic score function as the association discriminator does give the optimal global assignment for this problem and thus is a significantly better choice than the K-L divergence.…”

Section: Example 3 Let a Previously Observed Entity's Information Be contrasting

confidence: 71%

“…The tracking example touted that the K-L divergence enabled sustained tracking and target separability in heavy clutter, but was very scant on the details of the scenario, the type of tracker used, etc. Due to the lack of symmetry in the K-L divergence, see property (c) below, equation (57), and in Section V, Figures 7 -8 and Example 3, the K-L divergence will be shown to be a nonoptimal data association discriminator, which contradicts the previous literature 7 .…”

Section: Introductionmentioning

confidence: 77%

“…Previous works which used information theoretic measures for data association were primarily in the areas of multiple target tracking and sensor fusion and focused on mutual information 6 and the Kullback-Leiber divergence / discrimination. 7,8 The information form of the Kalman filter combined with the information theoretic measures of differential entropy and mutual information were utilized to formulate a local sensor fusion and control solution from a decentralized systems perspective 6 . The approach and examples presented therein made the data association problem "noiseless", and therefore trivialized the real difficulties with the proposed methodology.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of gating performed during the data association process 7 , the Kullback-Leibler (K-L) divergence / discrimination was compared against the traditional Mahalanobis divergence / χ 2 gating approach for determining whether a given measurement should be used to update a fused state estimate. The K-L divergence examined 7 using the notation employed herein was ( )…”

Section: Introductionmentioning

confidence: 99%

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Associating Data in System of Systems Using Measures of Information

Clancy

2014

Journal of Aerospace Information Systems

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Many modern day collaborative systems / system of systems (SoS) rely heavily on the sharing of information in order to improve performance, manage resources, and maximize overall capability. These types of systems are characterized by many decentralized nodes which can either all be identical or are divided into a finite set of specialized types. When information is shared within any SoS, the overall performance hinges on its ability to correctly associate the information received. The primary hypotheses evaluated by any SoS after the receipt of new information are (i) Does this information belong to an entity previously observed; or (ii) Does this information belong to a new entity, which includes both real and false entities. In order to evaluate these hypotheses, an association discriminator needs to be defined and evaluated which properly assesses the received information. This paper defines the properties required of a data association discriminator, highlights the measures of information which satisfy these properties, and develops the corresponding gating equations for use during the data association process. The architecture upon which a SoS is developed and designed plays a fundamental role when selecting an appropriate association discriminator. Some of these architectural considerations are (a) The type of SoS, decentralized or centralized; (b) The interface to and the performance of the individual data sources; (c) The capacity for managing the data sources and the receipt of information; (d) The computational resources available; and (e) The desired level of robustness to data association errors. This paper discusses various measures of information commonly used between and within the components of these types of systems and compares and contrasts their behavior in a common framework with a focus on the data association problem. Commonly used measures of information, namely, differential entropy, mutual information, and divergences, along with the logarithmic score function are examined analytically, the advantages and disadvantages of each are discussed, new results are derived and presented, and several simulation examples are presented which illuminate the use of these measures of information. Lastly, contrary to the current literature, it is demonstrated that the Kullback-Leibler discriminator / divergence is not an optimal data association discriminator.

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Section: Example 3 Let a Previously Observed Entity's Information Be contrasting

confidence: 71%

Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Associating Data in System of Systems Using Measures of Information

Clancy

2014

Journal of Aerospace Information Systems

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“…In Refs. [37][38][39] an information theoretic KL-divergence measure has been proposed to measure the confidence for fusion and tracking that has been lacking earlier in the literature.…”

Section: Target Hypothesis Predict Target Position Using Adaptive Gaumentioning

confidence: 99%

Entropy-Based Space Object Data Association Using an Adaptive Gaussian Sum Filter

Giza

Singla

Linares

et al. 2010

AIAA/AAS Astrodynamics Specialist Conference

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This paper shows an approach to improve the statistical validity of orbital estimates and uncertainties as well as a method of associating measurements with the correct resident space objects and classifying events in near realtime. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the resident space object tracking problem. The Fokker-Planck-Kolmogorov equation describes the time-evolution of the probability density function for nonlinear stochastic systems with Gaussian inputs, which often results in non-Gaussian outputs. The adaptive Gaussian sum filter provides a computationally efficient and accurate solution for this equation, which captures the non-Gaussian behavior associated with these nonlinear stochastic systems. This adaptive filter is designed to be scalable, relatively efficient for solutions of this type, and thus is able to handle the nonlinear effects which are common in the estimation of resident space object orbital states. The main purpose of this paper is to develop a technique for data association based on entropy theory that is compatible with the adaptive Gaussian sum filter. The adaptive filter and corresponding measurement association methods are evaluated using simulated data in realistic scenarios to determine their performance and feasibility.

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Associating Data In System Of Systems Using Measures Of Information

Clancy

2011

Infotech@Aerospace 2011

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<title>Information theoretics for improved tracking and fusion performance</title>

Cited by 3 publications

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Associating Data in System of Systems Using Measures of Information

Associating Data in System of Systems Using Measures of Information

Entropy-Based Space Object Data Association Using an Adaptive Gaussian Sum Filter

Associating Data In System Of Systems Using Measures Of Information

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