Introduction to Multisensor Data Fusion

By P. Vasseur, El Mustapha Mouaddib, C. Pégard. The Industrial Information Technology Handbook, 2005

1,229 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ac349bd5980445dea3be935ca7988ffe992c8d6f

How researchers see this paper

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

In Artificial Intelligence

In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection (2015, background): “At learning time, each tree learns one of the characteristic configurations of local patches, thus accommodating for different flexible articulations occurring in the training set.”
Multi-source remote sensing data fusion: status and trends (2010, methodology): “Early high-level multi-sensor fusion methods (Hall and James 1997) have been applied to land use classification.”
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox (2017, background): “Similarly, different fusion levels have their own advantages and disadvantages, and the suitable one for different fault diagnosis tasks is usually different [5].”
Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction (2013, methodology): “, Level 2 processing in the Joint Directors of Laboratories (JDL) model [6]).”
City Data Fusion: Sensor Data Fusion in the Internet of Things (2015, methodology): “Hall and Llinas [27] have defined the sensor data fusion as a method of combining sensor data from multiple sensors to produce more accurate, more complete, and more dependable information that could not be possible to achieve through a single sensor.”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Following [134], the techniques that have been applied to LIDAR and camera fusion can be roughly divided into two main categories based on their fusion process locations, including fusion at feature level (early stage, centralized fusion) and fusion at decision level (late stage, decentralized fusio”
Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey (2015, background): “classification [56], [57], prediction [85], or anomaly detection [61] to determine the current and future status of the distributed network.”
Distributed target classification and tracking in sensor networks (2003, background): “They are typically battery powered with limited communication and computation abilities [4].”
Advances in Multi-Sensor Data Fusion: Algorithms and Applications (2009, background): “In 1997, Hall and Llinas gave a general introduction to multi-sensor data fusion [1].”
On Data-Centric Trust Establishment in Ephemeral Ad Hoc Networks (2008, background): “The above process is tightly related to the multisensor data fusion techniques [11].”
Context Aware Computing for The Internet of Things: A Survey (2013, methodology): “In order to lay a solid foundation to our discussion, we adopt the deﬁnition provided by Hall and Llinas [149] on sensor data fusion.”
On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage (2006, neutral): “Recently there has been a lot of interest in networks of sensing agents which act cooperatively to obtain the best estimate possible, e.g., see (Roumeliotis and Bekey 2002, Hall and Llinas 1997, Viswanathan and Varshney 1997) and the references therein.”
A Review of Data Fusion Techniques (2013, background): “” Hall and Llinas [2] provided the following well-known definition of data fusion: “data fusion techniques combine data from multiple sensors and related information from associated databases to achieve improved accuracy and more specific inferences than could be achieved by the use of a single sens”
Pedestrian detection combining RGB and dense LIDAR data (2014, methodology): “Bayesian inference, simple voting techniques and many other approaches [29].”
Deep Multimodal Fusion by Channel Exchanging (2020, background): “Another categorization of multimodal fusion can be specified as early, middle, and late fusion, depending on when to fuse, which have been discussed in earlier works [2, 7, 17, 41] and also in the deep learning literature [4, 26, 27, 45].”
Multimodal deep learning for biomedical data fusion: a review (2022, neutral): “errors than unimodal approaches would [3].”
Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction (2012, background): “However, there are many challenges faced by those maritime surveillance systems [6]:”