| 1. |
- Xia, Yuxuan, 1993, et al.
(författare)
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Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar
- 2020
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. ; , s. 4900-4904
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Konferensbidrag (refereegranskat)abstract
- Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.
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| 2. |
- Xia, Yuxuan, 1993, et al.
(författare)
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Extended object tracking using hierarchical truncation model with partial-view measurements
- 2020
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020 June
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Konferensbidrag (refereegranskat)abstract
- This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.
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| 3. |
- Xia, Yuxuan, 1993, et al.
(författare)
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Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model
- 2020
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Ingår i: IEEE National Radar Conference - Proceedings. - 1097-5659. ; 2020-September
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Konferensbidrag (refereegranskat)abstract
- This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.
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| 4. |
- Xia, Yuxuan, 1993, et al.
(författare)
-
Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
- 2021
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Ingår i: IEEE Journal on Selected Topics in Signal Processing. - 1941-0484 .- 1932-4553. ; 15:4, s. 1013-1029
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
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