A Kalman filter to estimate direction for automotive navigation

Hoshino, Mitsuo; Gunji, Yukio Pegio; Oho, Shigeru; Takano, Kyu

doi:10.1109/mfi.1996.572171

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…On the other hand, basic prefiltering technique proves to be a very effective compensation scheme for random disturbances [74]. Other compensation methods depend on measurements from other sensors or systems, such as gyroscope [35], [49], [89] or GPS [36], [38].…”

Section: ) Accelerometermentioning

confidence: 99%

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Fang

Antsaklis

Montestruque³

et al. 2005

IEEE Trans. Instrum. Meas.

231

144

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Abstract-In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.Index Terms-Dead reckoning, pedestrian navigation system, wireless sensor network.

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Section: ) Accelerometermentioning

confidence: 99%

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Fang

Antsaklis

Montestruque³

et al. 2005

IEEE Trans. Instrum. Meas.

231

144

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“…Study [5] uses it to plan the correct route for commuting to a destination in an urban environment. Study [6] and Study [7] develop expanded Kalman filters. Literature also describes modified Kalman filters.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

Fracczak

Podsedkowski

Kobierska

2015

Int. J. Fuzzy Syst.

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This article describes a data fusion algorithm that uses fuzzy logic techniques. The algorithm involves weighting factors, the values of which change depending on whether the conditions dictated by processes of fuzzy logic have been met. By modifying the values of weighting factors, one can achieve a measurement signal with expected properties. The article includes a general description of the algorithm and an example of its application. The algorithm was developed for the purposes of data fusion for contactless measurement of the linear and angular position of an automatic laparoscopic device or a laparoscopic camera (hereinafter Laparoscope sleeve or LS).These measurements are used in the article to close the feedback loop on the position within a servomechanism with two degrees of freedom.

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“…To improve dead reckoning accuracy, many works have focused on calculating exact heading angle by utilizing gyroscopes and other sensors (such as a electronic compass or a tilt sensor) [9,10,11]. But these methods accumulate position error in the longitudinal direction of the robot.…”

Section: Introductionmentioning

confidence: 99%

Improvement of dead reckoning accuracy of a mobile robot by slip detection and compensation using multiple model approach

Lee

Choi

Park

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Although dead reckoning based on odometry and inertial sensors is essential for a robotic localization system, none of previous works gives reliable and accurate position estimates on irregular terrain over long periods of time. Classical approaches use one estimator (such as a Kalman filter) with a single system model. However the single system model is not good to deal with both of slip and no-slip situations because of the dynamics changes. In this paper, a multiple model approach that uses two Kalman filters is presented: one Kalman filter accounting for no-slip condition and the other for slip condition. The Interacting Multiple Model (IMM) is adopted to switch two Kalman filters depending on whether slip occurs or not, and gives the weighted sum of two filter estimates. Experimental results are included to validate our approach.

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A Kalman filter to estimate direction for automotive navigation

Cited by 11 publications

References 11 publications

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

Improvement of dead reckoning accuracy of a mobile robot by slip detection and compensation using multiple model approach

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