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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.

This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The second filter provides a multi-Bernoulli approximation of the posterior density over the set of all trajectories (alive and dead) up to the current time. We also explain the Gaussian implementation of the filters and compare them with other multiple target tracking algorithms in a simulated scenario.

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.

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.

The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.

This paper presents a disturbing recovery approach for lower extremity exoskeletons with passive ankle joints, which particularly works when an external disturbance is applied on the standing exoskeleton. Firstly, by analyzing the mechanical structure of lower extremity exoskeletons during its stance phase, the mathematical model is established using state-space equations. Then, on the basis of LQR (Linear Quadratic Regulator) theory, a novel neural network controller is designed to obtain the optimal solution for the balance control of model proposed. Finally, experimental results demonstrate that the proposed disturbing recovery approach not only has faster recovery speed, but also can achieve higher control precision than the traditional LQR.

In this paper, we evaluate the performance of labelled and unlabelled multi-Bernoulli conjugate priors for multi-target filtering. Filters are compared in two different scenarios with performance assessed using the generalised optimal sub-pattern assignment (GOSPA) metric. The first scenario under consideration is tracking of well-spaced targets. The second scenario is more challenging and considers targets in close proximity, for which filters may suffer from coalescence. We analyse various aspects of the filters in these two scenarios. Though all filters have pros and cons, the Poisson multi-Bernoulli filters arguably provide the best overall performance concerning GOSPA and computational time.

In this paper, nanoscale germanium (Ge) fin etching with inductively coupled plasma (ICP) equipment by Cl2/BCl3/Ar gas is experimentally demonstrated. The impact of Cl2/BCl3/Ar gas ratio on etching induced Ge surface roughness, etch rate, sidewall steepness, uniformity and layout dependence are comprehensively investigated. The surface roughness is improved by increasing Ar flow rate. A nearly vertical Ge Fin is obtained by optimizing Cl2/BCl3/Ar gas ratio. By using silicon oxide as hard mask, 60nm-width Ge Fin array with height of 100nm is experimentally illustrated with high uniformity of etch depth and Fin width. Therefore, this method shows great potential for Ge-based multi-gate device fabrication.

In this paper, an interface treatment for (100) Ge using HCl cleaning with different concentration and passivation with HCl and NH 4 F is experimentally demonstrated. The root mean square roughness (Rms) of Ge surface is reduced from 1.95nm to 0.274nm by 36% HCl cleaning, due to the high efficiency to remove the sub-oxide on Ge surface. After the pre-cleaning process, passivation with 10% NH 4 F achieves a stable surface by suppressing the re-oxidation of Ge surface. Ge MOS capacitors using HCl cleaning and NH 4 F passivation method exhibits better C-V performance with the hump disappearance and less frequency dispersion.