Maximum likelihood registration for multiple dissimilar sensors

Okello, N.; Ristić, Branko

doi:10.1109/taes.2003.1238759

Cited by 129 publications

(48 citation statements)

References 6 publications

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“…In this paper, the finite element analysis software ANSYS is used on the fixture device strength and stiffness analysis (Okello, 2003). In practice, the large base plate of the sensor fixture is fixed with the bracket.…”

Section: Comparison Of Finite Element Analysis Resultsmentioning

confidence: 99%

Performance Analysis of A New Type Weighing Sensor Error Calibration Fixture

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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For the ubiquitous problem of heavier weight, difficult assembling, low efficiency of the sensor calibration of the sensor clamp device of traditional weighing sensor error calibration device, a new sensor clamp device is designed in this paper. The finite element analysis of a new type of fixture is carried out by using the finite element analysis technology, the results show that the weight of the new sensor device is reduced by 7.46% compared with the traditional fixture, but also has a higher stiffness and strength. The dynamic characteristics of the sensor fixture is analyzed and studied through the modal analysis, the results show that the third and fifth mode shape have the greatest impact on the cylinder and the cylinder head which connected to the transverse junction. The above research can provide some theoretical reference and engineering reference for the design of other sensor fixture.

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Section: Comparison Of Finite Element Analysis Resultsmentioning

confidence: 99%

Performance Analysis of A New Type Weighing Sensor Error Calibration Fixture

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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“…In this case, the similarity measure based on absolute coordinates is unbelievable any more. Generally speaking, it is required to implement sensor registration [3,4] to remove the sensor biases from the biased sensor reports. In [5,6], the on-line algorithms for estimating sensor biases based on the batch processing were proposed.…”

Section: Introductionmentioning

confidence: 99%

Robust track-to-track association in the presence of sensor biases and missed detections

Zhu

Wang

2016

Information Fusion

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“…As sensor biases are additive to the measurements, sensor registration is usually performed at the measurement level. Many sensor registration algorithms at measurement level have been proposed in the literature such as the least squares (LS) method [18], and the maximum likelihood estimator (MLE) method [28]. In addition, registration and fusion processes have also been proposed to be performed together so that tracking and registration can be carried out simultaneously in a nonstationary environment [15,20].…”

Section: Introductionmentioning

confidence: 99%