Sensor-based allocation for path planning and area coverage using UGSs

Dezert²,

Pannetier³

2013

SPIE Proceedings

Over the years, there have been many proposed methods in set-based tracking. One example of set-based methods is the use of Dempster-Shafer (DS) techniques to support belief-function (BF) tracking. In this paper, we overview the issues and concepts that motivated DS methods for simultaneous tracking and classification/identification. DS methods have some attributes, if applied correctly; but there are some pitfalls that need to be carefully avoided such as the redistribution of the mass associated with conflicting measurements. Such comparisons and applications are found in Dezert-Smarandache Theory (DSmT) methods from which the Proportional Conflict Redistribution (PCR5) rule supports a more comprehensive approach towards applying evidential and BF techniques to target tracking. In the paper, we overview two decades of research in the area of BF tracking and conclude with a comparative analysis of Bayesian, Dempster-Shafer, and the PCR5 methods.

Section: Tracking Methods Using Dempster-shafer Theorymentioning

confidence: 99%

Overview of Dempster-Shafer and belief function tracking methods

Dezert²,

Pannetier³

2013

SPIE Proceedings

“…Blasch applied information theory to image registration for target classification [20] and subsequently for target detection and identification in a simultaneous target tracking and identification (STID) scenario [21]. Other areas where information theoretic measures have been applied include measurement request for tracking with UGS [22], ontology alignment [23], and image fusion [24,25]. Methods developed with the D-divergence can enhance multiple areas in information fusion.…”

Section: Information-theoretic Sensor Management Analysis [13]mentioning

confidence: 99%

Comparison of information theoretic divergences for sensor management

Yang¹,

Kadar

et al. 2011

SPIE Proceedings

In this paper, we compare the information-theoretic metrics of the Kullback-Leibler (K-L) and Renyi (D) divergence formulations for sensor management. Information-theoretic metrics have been well suited for sensor management as they afford comparisons between distributions resulting from different types of sensors under different actions. The difference in distributions can also be measured as entropy formulations to discern the communication channel capacity (i.e., Shannon limit). In this paper, we formulate a sensor management scenario for target tracking and compare various metrics for performance evaluation as a function of the design parameter (D) so as to determine which measures might be appropriate for sensor management given the dynamics of the scenario and design parameter.

“…Information theoretical approaches including the KL method were applied to the data for sensor management [53] as shown in Figure 2. Processing sensor data for target classification using acoustic [54,55] and seismic [56] results have been explored in support of information theoretical sensor placement [57].…”

Section: Introductionmentioning

confidence: 99%

DSmT applied to seismic and acoustic sensor fusion

Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)

Dezert

Valin

2011

Self Cite

In this paper, we explore the use of the DezertSmarandache Theory (DSmT) for seismic and acoustic sensor fusion. The seismic/acoustic data is noisy which leads to classification errors and conflicts in declarations. DSmT affords the redistribution of masses when there is a conflict. The goal of this paper is to present an application and comparison on DSmT with other classifier methods to include the support vector machine(SVM) and DempsterShafer (DS) methods. The work is based on two key references (1) Marco Duarte with the initial SVM classifier application of the seismic and acoustic sensor data and (2) Arnaud Martin in Vol. 3 with the Proportional Conflict Redistribution Rule 5/6 (PCR5/PCR6) developments. By using the developments of Duarte and Martin, we were able to explore the various aspects of DSmT in an unattended ground sensor scenario. Using the receiver operator curve (ROC), we compare the methods for individual classification as well as a measure of overall classification using the area under the curve (AUC). Conclusions of the work show that the DSmT results with a maximum forced choice are comparable to the SVM.