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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. 

This paper studies the “age of information” in a general multi-source multi-hop wireless network with explicit channel contention. Specifically, the scenario considered in this paper assumes that each node in the network is both a source and a monitor of information, that all nodes wish to receive fresh status updates from all other nodes in the network, and that only one node can transmit in each time slot. Lower bounds for peak and average age of information are derived and expressed in terms of fundamental graph properties including the connected domination number. An algorithm to generate near-optimal periodic status update schedules based on sequential optimal flooding is also developed. These schedules are analytically shown to exactly achieve the peak age bound and also achieve the average age bound within an additive gap scaling linearly with the size of the network. Moreover, the results are sufficiently general to apply to any connected network topology. Illustrative numerical examples are presented which serve to verify the analysis for several canonical network topologies of arbitrary size, as well as every connected network with nine or fewer nodes.