# Fast and Robust State Estimation and Tracking via Hierarchical Learning

arXiv: https://arxiv.org/abs/2306.17267 · HTML: https://mlsanity.com/paper/2306.17267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions](https://mlsanity.com/paper/1811.03982/) (2018)
- [Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements](https://mlsanity.com/paper/1810.10086/) (2018)
- [Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles](https://mlsanity.com/paper/2303.04340/) (2023)
- [Reaching Approximate Byzantine Consensus with Multi-hop Communication](https://mlsanity.com/paper/1411.5282/) (2014)
- [Graph-based Cyber Security Analysis of State Estimation in Smart Power Grid](https://mlsanity.com/paper/1612.05878/) (2016)
- [Agent-aware State Estimation in Autonomous Vehicles](https://mlsanity.com/paper/2108.00366/) (2021)

## Abstract (arXiv)

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.