Fast and Robust State Estimation and Tracking via Hierarchical Learning

• URL: http://arxiv.org/abs/2306.17267v2
• Date: Fri, 13 Sep 2024 21:29:32 GMT
• Title: Fast and Robust State Estimation and Tracking via Hierarchical Learning
• Authors: Connor Mclaughlin, Matthew Ding, Deniz Erdogmus, Lili Su,
• Abstract summary: We aim to speed up the convergence and enhance the resilience of state estimation and tracking for large-scale networks. We numerically validate our algorithms through simulation studies of underwater acoustic networks and large-scale synthetic networks.
• Score: 9.341558827016332
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fast and reliable state estimation and tracking are essential for real-time situation awareness in Cyber-Physical Systems (CPS) operating in tactical environments or complicated civilian environments. Traditional centralized solutions do not scale well whereas existing fully distributed solutions over large networks suffer slow convergence, and are vulnerable to a wide spectrum of communication failures. In this paper, we aim to speed up the convergence and enhance the resilience of state estimation and tracking for large-scale networks using a simple hierarchical system architecture. We propose two ``consensus + innovation'' algorithms, both of which rely on a novel hierarchical push-sum consensus component. We characterize their convergence rates under a linear local observation model and minimal technical assumptions. We numerically validate our algorithms through simulation studies of underwater acoustic networks and large-scale synthetic networks.

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
  This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks. We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
  arXiv  Detail & Related papers  (2024-05-08T17:28:07Z)
• Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks [2.4012886591705738]
  Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis. Their practical implementation on extensive networks remains computationally demanding due to their high time complexity. We propose the CNCA-IGE model, which is an inductive graph encoder-decoder model designed to rank nodes based on specified CC or BC metrics.
  arXiv  Detail & Related papers  (2024-03-08T01:23:12Z)
• Generalization and Estimation Error Bounds for Model-based Neural Networks [78.88759757988761]
  We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks. We derive practical design rules that allow to construct model-based networks with guaranteed high generalization.
  arXiv  Detail & Related papers  (2023-04-19T16:39:44Z)
• Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopes [0.0]
  We study the verification problem for closed-loop dynamical systems with neural-network controllers (NNCS) We present an algorithm to chain approaches based on Taylor models and zonotopes, yielding a precise reachability algorithm for NNCS.
  arXiv  Detail & Related papers  (2021-12-16T20:46:39Z)
• Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee [30.86806523281873]
  Massive access is a critical design challenge of Internet of Things (IoT) networks. We consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. We propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness.
  arXiv  Detail & Related papers  (2021-06-19T05:23:05Z)
• Revisiting the double-well problem by deep learning with a hybrid network [7.308730248177914]
  We propose a novel hybrid network which integrates two different kinds of neural networks: LSTM and ResNet. Such a hybrid network can be applied for solving cooperative dynamics in a system with fast spatial or temporal modulations.
  arXiv  Detail & Related papers  (2021-04-25T07:51:43Z)
• Reinforcement Learning for Datacenter Congestion Control [50.225885814524304]
  Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. We devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training.
  arXiv  Detail & Related papers  (2021-02-18T13:49:28Z)
• Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints [61.46323213702369]
  This work presents an unsupervised learning framework that is able to predict at-scale depth maps and egomotion. Our results demonstrate strong performance when compared to the current state-of-the-art on multiple sequences of the KITTI driving dataset.
  arXiv  Detail & Related papers  (2020-11-02T22:26:58Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.