Sensor Models and Multisensor Integration

By H. F. Durrant-Whyte. 1988

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Billy Pik Lik Lau
2. Marakkalage Sumudu Hasala
3. Behailu Negash
4. Yuren Zhou
5. Sherali Zeadally
6. Juan Contreras-Castillo
7. Chau Yuen
8. J. Ibáñez

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “In non-smart city literature, there are four well-known data fusion classification techniques, which are Dasarathy’s Classification [39], Whyte’s Classification [40], Fusion Architecture’s Classification [18], and US Joint Di-rectories of Laboratories (JDL) data fusion classification [41].”
• Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach (2018, background): “Data fusion is categorized into three classes: complementary, competitive, and cooperative [38].”
• A Review of Data Fusion Techniques (2013, background): “(1) attending to the relations between the input data sources, as proposed by Durrant-Whyte [3].”
• Leveraging Fog Computing for Healthcare IoT (2018, background): “Data fusion can be divided into three classes as complementary, competitive, and cooperative [22].”
• Towards reliable multimodal sensing in aware environments (2001, background): “Sensors can be divided into the following classes [11]: complementary, competitive, and cooperative.”
• Multimodal deep learning for biomedical data fusion: a review (2022, background): “More specifically, the advantages of data fusion can be categorized as complementary, redundant and cooperative features [4, 5], though these are not mutually exclusive.”
• Information fusion for wireless sensor networks: Methods, models, and classifications (2007, background): “Classi.cation Based on Relationship Among the Sources According to the relationship among the sources, information fusion can be classi.ed as complementary, redundant, or cooperative [Durrant-Whyte 1988].”
• A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods (2020, methodology): “The three fundamental ways of combining sensor data are the following [99], [104]:”
• Multimodal machine learning in precision health: A scoping review (2022, methodology): “Bayesian ’ s methods are typically employed at this level 40 to support a voting process between the set of models into a global decision.”
• Sensor Technologies for Intelligent Transportation Systems (2018, methodology): “Data fusion techniques have been used extensively in multisensory environments with the aim of fusing and aggregating data from different sensors according to the following criteria: (a) Input data source relationships: supplementary data, redundant data, or cooperative data [99].”