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1. Tracking of Dynamical Processes with Model Switching Using Temporal Convolutional Networks

   https://doi.org/10.1109/AERO50100.2021.9438450  

   Grootveld, Arick; Bugayev, Vlad I.; Lackey, Leah; Klein, Andrew G.; Vedula, Kirty; Brown, D. Richard (March 2021, 2021 IEEE Aerospace Conference)

   This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system. 

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2. Multisensor Multiobject Tracking With Improved Sampling Efficiency

   https://doi.org/10.1109/TSP.2024.3374047  

   Zhang, Wenyu; Meyer, Florian (January 2024, IEEE Transactions on Signal Processing)

   Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or “particles”. The multimodal probability density functions provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results, obtained by processing synthetic and real data, demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques. 

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3. Neural Networks for Learning Counterfactual G-Invariances from Single Environments

   Mouli, S Chandra; Ribeiro, Bruno (July 2021, Proceedings of the 9th International Conference on Learning Representations)

   Despite —or maybe because of— their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This work shows that, for extrapolations based on finite transformation groups, a model’s inability to extrapolate is unrelated to its capacity. Rather, the shortcoming is inherited from a learning hypothesis: Examples not explicitly observed with infinitely many training examples have underspecified outcomes in the learner’s model. In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework counterfactually-guided by the learning hypothesis that any group invariance to (known) transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data. Unlike existing invariance-driven methods for (counterfactual) extrapolations, this framework allows extrapolations from a single environment. Finally, we introduce sequence and image extrapolation tasks that validate our framework and showcase the shortcomings of traditional approaches. 

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4. Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression

   https://doi.org/10.23919/ACC53348.2022.9867845  

   Xiang, Weiming; Shao, Zhongzhu (June 2022, 2022 American Control Conference (ACC))

   In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural network merging method is developed to compute the approximate bisimulation error between two neural networks based on reachability analysis of neural networks. The developed method is able to quantitatively measure the distance between the outputs of two neural networks with the same inputs. Then, we apply the approximate bisimulation relation results to perform neural networks model reduction and compute the compression precision, i.e., assured neural networks compression. At last, using the assured neural network compression, we accelerate the verification processes of ACAS Xu neural networks to illustrate the effectiveness and advantages of our proposed approximate bisimulation approach. 

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5. On the Impact of Different Kernels and Training Data on a Gaussian Process Approach for Target Tracking

   https://doi.org/10.23919/FUSION45008.2020.9190413  

   Aftab, W; Mihaylova, L (July 2020, the International Conference on Information Fusion (Fusion 2020))

   The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method. 

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