&lt;title&gt;Multisensor data fusion for mine detection&lt;/title&gt;

Chaudhuri, Syama P.; Crandall, Alan L.; Reidy, Denis Michael

doi:10.1117/12.21624

Cited by 6 publications

(5 citation statements)

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“…Chauduri et al [3] developed a demining system in which coregistered GPR and EMI sensors were fused using several decision-level fusion approaches. Brusmark et al [4] demonstrated the decision-level fusion of coincidently sampled GPR and EMI sensor data collected over targets buried in a sand box.…”

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confidence: 99%

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

Gunatilaka

Baertlein

2001

IEEE Trans. Pattern Anal. Machine Intell.

113

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AbstractÐWe present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both featurelevel and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available (ªsoft-decisionº fusion).

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confidence: 99%

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

Gunatilaka

Baertlein

2001

IEEE Trans. Pattern Anal. Machine Intell.

113

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“…(2) Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/24/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx (4) One can easily verify that summing up equations (2), (3), and (4), as per expectations, leads to:…”

Section: Initial Performance Under the Or Logic Based Fusionmentioning

confidence: 96%

<title>Recursive strategies for decision fusion in imperfect multisensor environments. Part I--fusion benefits</title>

Dasarathy

1994

Sensor Fusion and Aerospace Applications II

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The paper presents alternative sUategies framed within a recursive structure for enhancing multisensor system performance through fusion of individual decisions derived from imperfect sensors. A confidence measure can be associated with each decision derived from an imperfect sensor and as such, decisions obtained with a confidence less than an acceptable threshold value are viewed as non-decisions in developing the fused decision. The initial and asymptotic performances of these alternative strategies are analyzed and compared against the datum of single sensor performance.

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“…To have optimal fusion performance given the sensor rules, (3) indicates that all we need to do is to compute the required ratio of likelihood or conditional joint sensor decision probabilities. The contribution of [2] was in essence the simplification of the above likelihood ratio to a product of the ratios of two conditional decision probabilities of every sensor when sensor observations are independent and there are no communicatiom among sensors.…”

Section: Computation Of Likelihood Ratiosmentioning

confidence: 99%

Optimal decision fusion given sensor rules

Zhu

2005

J. Control Theory Appl.

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When all the rules of sensor decision are known, the optimal distributed decision fusion, which relies only on the joint conditional probability densities, can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion. Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented, which take the form of a generalized likelihood ratio test. Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision, but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.

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<title>Multisensor data fusion for mine detection</title>

Cited by 6 publications

References 0 publications

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

<title>Recursive strategies for decision fusion in imperfect multisensor environments. Part I--fusion benefits</title>

Optimal decision fusion given sensor rules

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