| 1. |
- Xia, Yuxuan, 1993, et al.
(author)
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Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar
- 2020
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. ; , s. 4900-4904
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Conference paper (peer-reviewed)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.
(author)
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Extended object tracking using hierarchical truncation model with partial-view measurements
- 2020
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In: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020 June
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Conference paper (peer-reviewed)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.
(author)
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Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model
- 2020
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In: IEEE National Radar Conference - Proceedings. - 1097-5659. ; 2020-September
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Conference paper (peer-reviewed)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.
(author)
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Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
- 2021
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In: IEEE Journal on Selected Topics in Signal Processing. - 1941-0484 .- 1932-4553. ; 15:4, s. 1013-1029
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Journal article (peer-reviewed)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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| 5. |
- Zhang, Jinyun, et al.
(author)
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UWB Systems for Wireless Sensor Networks
- 2009
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In: Proceedings of the IEEE. - 0018-9219. ; 97:2, s. 313-331
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Journal article (peer-reviewed)abstract
- Wireless sensor networks are emerging as an important area for communications. They enable a wealth of new applications including surveillance, building control, factory automation, and in-vehicle sensing. The sensor nodes have to operate under severe constraints on energy consumption and form factor, and provide the ability for precise self-location of the nodes. These requirements can be fulfilled very well by various forms of ultra-wide-band (UWB) transmission technology. We discuss various techniques and tradeoffs in UWB systems and indicate that time-hopping and frequency-hopping impulse radio physical layers combined with simple multiple-access techniques like ALOHA are suitable designs. We also describe the IEEE 802.15.4a standard, an important system that adopts UWB impulse radio to ensure robust data communications and precision ranging. in order to accommodate heterogeneous networks, it uses specific modulation, coding, and ranging waveforms that can be detected well by both coherent and noncoherent receivers.
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