Bayesian Filtering for Location Estimation

By Dieter Fox, Jeffrey Hightower, Lin Liao, Dirk Schulz, Gaetano Borriello. IEEE pervasive computing, 2003

813 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1be896a05ef7558df23d1ef5de4b9ab8f3aad283

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In Artificial Intelligence

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In Electrical and Electronic Engineering

Citations in the corpus

Distributed Kalman filtering for sensor networks (2007, background): “Multi-sensor fusion and tracking problems have a long history in signal processing, control theory, and robotics [1], [2], [3], [6], [16].”
Active Neural Localization (2018, methodology): “This kind of grid-based representation of belief is popular among localization methods as if offers several advantages over topological representations (Burgard et al., 1996; Fox et al., 2003).”
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, neutral): “People tracking is a fundamental problem in ubiquitous computing and has also been approached via a variety of sensors, including cameras, laser range finders, wireless networks, RFID (Radio frequency identification) badges, and infrared or ultrasound badges [2, 3, 8, 12, 18, 15, 26].”
Adaptive Temporal Radio Maps for Indoor Location Estimation (2005, methodology): “In this paper, we present a novel method to adapt the temporal radio maps for indoor location estimation by offsetting the variational environmental factors using data mining techniques and reference points.”
Indoor Tracking: Theory, Methods, and Technologies (2015, methodology): “Real-time approaches that have been proposed are mainly based on the Bayesian filtering methodology, including variants of the Kalman filter (KF) and the much more versatile framework provided by particle filtering (PF) [7].”
A Survey on Localization for Mobile Wireless Sensor Networks (2009, methodology): “Such methods are used in [55], [53], and [70].”
Deep Neural Networks for wireless localization in indoor and outdoor environments (2016, methodology): “Bayesian-based filtering methods were then proposed in [5] [6] [7] [8] to enhance the positioning robustness.”
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, background): “A number of variants on probabilistic Bayesian inference approaches have appeared in the literature [4], [7], [20]–[28].”
Wireless sensor network localization techniques (2007, methodology): “Particle filters have been used in navigation and tracking [101].”
Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning (2007, methodology): “Optionally, we can apply a Bayes Filter (Fox et al. 2003; Hu & Evans 2004) to smooth the trajectory and enhance the performance of localization.”
Cooperative Localization in Wireless Networks (2009, background): “Sequential or online estimation deals with such scenarios by estimating variables at a certain time, say, variablex ðtÞ at time t, taking into account independent observations taken up to and including t ,s ay,z ð1:tÞ ¼½ z ð1Þ ; ... ;z ðtÞ � [ 17 ], [97],”
Multimodal sensory fusion for soccer robot self-localization based on long short-term memory recurrent neural network (2017, background): “Table 1, taken in part from (Fox et al. 2003), summarizes the classification of these localization methods according to the processed problem.”
A Behavioral Approach to Visual Navigation with Graph Localization Networks (2019, methodology): “As an example, we explore using a Bayes filter [15] to improve localization.”
Earthquake shakes Twitter users: real-time event detection by social sensors (2010, methodology): “Estimating an object’s location is arguably the most fundamental sensing task in many ubiquitous and pervasive computing scenarios [ 7 ].”
Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development (2013, methodology): “Representing locations probabilistically provides a unified interface for location information, which enables us to produce applications that are independent of the sensors used, even when using starkly different sensor types such as GPS and infrared badges [11], or even Twitter.”
Accuracy characterization for metropolitan-scale Wi-Fi localization (2005, methodology): “A particle lter is a probabilistic approximation algorithm that implements a Bayes lter [ 6 ].”