Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements

Radtke, Susanne; Li, Kailai; Noack, Benjamin; Hanebeck, Uwe D.

doi:10.1109/icphys.2019.8780330

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…Thus, it is also of interest to evaluate the filter’s performance in real-world tasks. Potential application scenarios include orientation estimation using omnidirectional vision [ 5 ], visual tracking on unit hyperspheres [ 39 ], bearing-only localization in sensor networks [ 40 ], wavefront orientation estimation in the surveillance field [ 29 ] and sound source localization [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

Pfaff

Hanebeck

2021

Sensors

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In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.

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Section: Discussionmentioning

confidence: 99%

Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

Pfaff

Hanebeck

2021

Sensors

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“…It is thus valuable to further investigate the difference between drawing samples from the approximated density and the true one. Moreover, by applying the proposed sampling approach to the Bingham-based pose filtering frameworks [4], [6], [34]- [36], a better performance regarding the tracking accuracy, efficiency, and robustness can be expected.…”

Section: Discussionmentioning

confidence: 99%

Hyperspherical Deterministic Sampling Based on Riemannian Geometry for Improved Nonlinear Bingham Filtering

Pfaff

Hanebeck

2019

2019 22th International Conference on Information Fusion (FUSION)

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We present a novel geometry-driven scheme for generating equally weighted deterministic samples of Bingham distributions in arbitrary dimensions. Unlike existing approaches, our method provides flexibility in the sampling size with samples satisfying requirements of the unscented transform while approximating higher-order moments of the Bingham distribution. This is done by first using Dirac mixture approximation as a sampling scheme on the tangent plane at the mode with respect to the Bingham density via gnomonic projection. Subsequently, the tangent sigma points are retracted backwards to the hypersphere, after which an on-manifold moment correction is performed via Riemannian optimization. The proposed approach is further applied to quaternion Bingham filtering for recursive orientation estimations. Evaluation results show that the geometry-adaptive sampling scheme gives better tracking accuracy and robustness for nonlinear orientation estimations.

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“…To be scalable with respect to the data load on the network, these approaches share preprocessed object lists rather than raw data. These object lists then are merged using a track-to-track (T2T) approach [26]. However, T2T fusion suffers from the problem that the incoming data has already been filtered and is therefore no longer statistically independent, as most Bayesian approaches require.…”

Section: A Related Workmentioning

confidence: 99%

“…However, T2T fusion suffers from the problem that the incoming data has already been filtered and is therefore no longer statistically independent, as most Bayesian approaches require. Hence, optimal T2T fusion requires effortful correction mechanisms [26]. In contrast, typically used sub-optimal fusion methods overestimate the covariance of the states of the objects.…”

Section: A Related Workmentioning

confidence: 99%

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Müller¹,

Strohbeck²,

Herrmann³

et al. 2021

Preprint

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Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, the approach provides safety guarantees in a probabilistic sense, while for a vanishing risk, the formal safety guarantees of the setbased methods are inherited. By exploring all available behavior options, our approach solves decision making and longitudinal trajectory planning in one step. The available behavior options are provided by a formal representation of the situation context, which is also used to reduce calculation efforts. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track fusion, the information is used in parallel. The motion planning scheme is validated through real-world experiments.

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Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements

Cited by 18 publications

References 23 publications

Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

Hyperspherical Deterministic Sampling Based on Riemannian Geometry for Improved Nonlinear Bingham Filtering

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

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