A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking

Leong, Pei H.; Arulampalam, Sanjeev; Lamahewa, Tharaka A.; Abhayapala, Thushara D.

doi:10.1109/taes.2013.6494405

Cited by 137 publications

(110 citation statements)

References 21 publications

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“…The authors of [29] propose Gaussian sum cubature filters. In [30,31], the authors consider a Rao-Blackwellization scheme for SLAM with a particle filter for the user state and UKFs for the landmark states, where the measurement model is based on linearization, though.…”

Section: Journal Of Electrical and Computer Engineeringmentioning

confidence: 99%

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Ulmschneider

Gentner

Jost

et al. 2018

Journal of Electrical and Computer Engineering

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In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust longterm SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements.

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Section: Journal Of Electrical and Computer Engineeringmentioning

confidence: 99%

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Ulmschneider

Gentner

Jost

et al. 2018

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…In target tracking problem, the process model is generally linear, while the measurement model, mainly including the measured range and bearing angle, is nonlinear [2,3]. The essence of the target tracking is to use a series of measured ranges and bearing angle information to estimate the position and velocity of the target in real time; hence, it belongs to the nonlinear filtering problem, which has always been dealt with using the nonlinear Kalman filters [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

Yang

Ding

2018

Mathematical Problems in Engineering

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A novel fifth-degree strong tracking cubature Kalman filter is put forward to improve the two-dimensional maneuvering target tracking accuracy. First, a new fifth-degree cubature rule, with only one point more than the theoretical lower bound, is used to approximate the intractable nonlinear Gaussian weighted integral in the nonlinear Kalman filtering framework, and a novel fifthdegree cubature Kalman filter is proposed. Then, the suboptimal fading factor is designed for the filter to adjust the filtering gain matrix online and force the residual sequences mutually orthogonal, thus improving the ability of the filter to track the mutation state, and the fifth-degree strong tracking cubature Kalman filter is derived. The suboptimal fading factor is calculated in a new method, which reduces the number of calculations for the cubature points from three times to twice without calculating the Jacobian matrix. The simulation results indicate that the proposed filter has the ability to track the maneuvering target and achieve higher target tracking accuracy and thus verifies the effectiveness of the proposed filter.

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“…For the latter, the Gaussian pdf is approximated using the deterministic sampling approach, which mainly includes the unscented transform (UT) and spherical-radial rule (SRR). Then, the unscented Kalman filter (UKF) [10,11] and cubature Kalman filter (CKF) [12][13][14] are obtained by embedding UT and SRR into the Bayesian filtering framework, respectively, these have a wide range of applications in engineering [15][16][17][18][19][20], but these two types of algorithm have only third-degree filtering accuracy, which is required to be further improved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

Li¹,

Yang²,

Ding³

et al. 2017

Mathematical Problems in Engineering

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A novel fifth-degree cubature Kalman filter is proposed to improve the accuracy of real-time orbit determination by ground-based radar. A fully symmetric cubature rule, approaching the Gaussian weighted integral of a nonlinear function in general form, is introduced, and the specific points and weights are calculated by matching the monomials of degree not greater than five with the exact values. On the basis of the above rule, a novel fifth-degree cubature Kalman filter, which can achieve a higher accuracy than UKF and CKF, is derived under the Bayesian filtering framework. Then, to describe the nonlinear system more accurately, the orbital dynamics equation with J2 perturbation is used as the state equation, and the nonlinear relationship between the radar measurement elements and orbital states is built as the measurement equation. The simulation results show that, compared with the traditional third-degree algorithm, the proposed fifth-degree algorithm has a higher accuracy of orbit determination.

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A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking

Cited by 137 publications

References 21 publications

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

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