A multi-temporal multi-sensor circular fusion filter

Stienne, Georges; Reboul, Serge; Azmani, Monir; Choquel, Jean-Bernard; Benjelloun, M.

doi:10.1016/j.inffus.2013.05.012

Cited by 36 publications

(27 citation statements)

References 23 publications

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“…In [15], we use the algorithm by Amos [28] 2 to calculate A(·) and MATLAB's fsolve to invert this function. Stienne et al have proposed closed-form approximations, which can be calculated very easily but have a large approximation error [12], [10]. A more detailed discussion on approximations of A −1 (·) can be found in [29] and [30, Sec.…”

Section: For a Given Wn Distribution With Density Wnmentioning

confidence: 99%

“…All proposed methods are thoroughly evaluated and compared to several state-of-the-art solutions. system measurement publication distribution model noise model noise Azmani, Reboul, Choquel, Benjelloun [9] von Mises identity additive identity additive Markovic, Chaumette, Petrovic [14] von Mises-Fisher identity additive identity additive Kurz, Gilitschenski, Julier, Hanebeck [21] Bingham identity additive identity additive Kurz, Gilitschenski, Hanebeck [15] wrapped normal/von Mises nonlinear additive identity additive Kurz, Gilitschenski, Hanebeck [16] wrapped normal nonlinear additive nonlinear any this paper wrapped normal/von Mises nonlinear any nonlinear anyTable 1: Circular filters based on directional statistics.There has been some work on filtering algorithms based on circular statistics by Azmani et al [9], which was further investigated by Stienne et al [10]. Their work is based on the von Mises distribution and allows for recursive filtering of systems with a circular state space.…”

mentioning

confidence: 99%

“…There has been some work on filtering algorithms based on circular statistics by Azmani et al [9], which was further investigated by Stienne et al [10]. Their work is based on the von Mises distribution and allows for recursive filtering of systems with a circular state space.…”

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confidence: 99%

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Recursive Bayesian filtering in circular state spaces

Kurz¹,

Gilitschenski

Hanebeck³

2016

IEEE Aerosp. Electron. Syst. Mag.

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For recursive circular filtering based on circular statistics, we introduce a general framework for estimation of a circular state based on different circular distributions, specifically the wrapped normal distribution and the von Mises distribution. We propose an estimation method for circular systems with nonlinear system and measurement functions. This is achieved by relying on efficient deterministic sampling techniques. Furthermore, we show how the calculations can be simplified in a variety of important special cases, such as systems with additive noise as well as identity system or measurement functions. We introduce several novel key components, particularly a distribution-free prediction algorithm, a new and superior formula for the multiplication of wrapped normal densities, and the ability to deal with non-additive system noise. All proposed methods are thoroughly evaluated and compared to several state-of-the-art solutions. system measurement publication distribution model noise model noise Azmani, Reboul, Choquel, Benjelloun [9] von Mises identity additive identity additive Markovic, Chaumette, Petrovic [14] von Mises-Fisher identity additive identity additive Kurz, Gilitschenski, Julier, Hanebeck [21] Bingham identity additive identity additive Kurz, Gilitschenski, Hanebeck [15] wrapped normal/von Mises nonlinear additive identity additive Kurz, Gilitschenski, Hanebeck [16] wrapped normal nonlinear additive nonlinear any this paper wrapped normal/von Mises nonlinear any nonlinear anyTable 1: Circular filters based on directional statistics.There has been some work on filtering algorithms based on circular statistics by Azmani et al. [9], which was further investigated by Stienne et al. [10]. Their work is based on the von Mises distribution and allows for recursive filtering of systems with a circular state space. However, it is limited to the identity with additive noise as the system equation and the measurement equation. The filter from [9] has been applied to phase estimation of GPS signals [11], [12] as well as map matching [13]. Markovic et al. have published a similar filter [14] based on the von Mises-Fisher distribution, a generalization of the von Mises distribution to the hypersphere.We have previously published a recursive filter based on the wrapped normal distribution allowing for a nonlinear system equation [15]. The paper [16] extends this approach to make a nonlinear measurement update possible. Both papers rely on a deterministic sampling scheme, based on the first circular moment. This kind of sampling is reminiscent of the well-known unscented Kalman filter (UKF) [2]. We have extended this sampling scheme to the first two circular moments in [17], so the proposed filters are, in a sense, circular versions of the UKF.The developed filters have been applied in the context of constrained tracking [18], bearings-only sensor scheduling [19], as well as circular model predictive control [20].Furthermore, we proposed a recursive filter based on the circular Bingham distribution in [21]....

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Section: For a Given Wn Distribution With Density Wnmentioning

confidence: 99%

mentioning

confidence: 99%

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Recursive Bayesian filtering in circular state spaces

Kurz¹,

Gilitschenski

Hanebeck³

2016

IEEE Aerosp. Electron. Syst. Mag.

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“…I 0 (κ R ) can be considered as a normalizing constant. κ R defines the accuracy of the measurements obtained with a circular filter [5] used to filter the heading's observations provided by the magnetometer. The weights are processed and then normalized.…”

Section: Particle Map Matching Algorithmmentioning

confidence: 99%

“…We provided in [10], [5] another improvement on the heading measurement through the implementation of circular filters. We showed that this filter gives better estimates of the heading and is unaffected by transitions between −π and π.…”

Section: Introductionmentioning

confidence: 99%

A map matching algorithm based on a particle filter

Mokhtari

Reboul²,

Azmani³

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

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Sensor Fusion Measurement

Zhang

Zhao

2015

Nondestructive Measurement in Food and Agro-Products

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A multi-temporal multi-sensor circular fusion filter

Cited by 36 publications

References 23 publications

Recursive Bayesian filtering in circular state spaces

Recursive Bayesian filtering in circular state spaces

A map matching algorithm based on a particle filter

Sensor Fusion Measurement

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