arXiv subjects

Irene Perez-Salesa

Publications and source records attributed to Irene Perez-Salesa.

5 recordsLinked to original sources

arXiv2026

Event-triggered parameter estimator for sensor fusion

This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.

eess.SY↗

arXiv2025

A Note on Optimal Distributed State Estimation for Linear Time-Varying Systems

In this technical note, we prove that the ODEFTC algorithm constitutes the first optimal distributed state estimator for continuous-time linear time-varying systems subject to stochastic disturbances. Particularly, we formally show that it is able to asymptotically recover the performance, in terms of error covariance of the estimates at each node, of the centralized Kalman-Bucy filter, which is known to be the optimal filter for the considered class of systems. Moreover, we provide a simple sufficient value for the consensus gain to guarantee the stability of the distributed estimator.

math.OC↗

arXiv2024

NN-ETM: Enabling safe neural network-based event-triggering mechanisms for consensus problems

Event-triggering mechanisms (ETM) have been developed for consensus problems to reduce communication while ensuring performance guarantees, but their design has grown increasingly complex by incorporating the agent's local and neighbor information. This typically results in ad-hoc solutions, which may only work for the consensus protocol under consideration. We aim to safely incorporate neural networks in the ETM to provide a general solution while guaranteeing performance. To decouple the stability analysis of the consensus protocol from the abstraction of the neural network, we derive design criteria for the consensus and ETM pair, allowing independent analysis of each element under mild constraints. Then, we propose NN-ETM, a novel ETM featuring a neural network, to optimize communication while preserving the stability guarantees of the consensus protocol.

eess.SY↗

arXiv2023

Event-Triggered Consensus for Continuous-Time Distributed Estimation

Distributed sensor networks have gained interest thanks to the developments in processing power and communications. Event-triggering mechanisms can be useful in reducing communication between the nodes of the network, while still ensuring an adequate behaviour of the system. However, very little attention has been given to continuous-time systems in this context. In this work, we propose a strategy for distributed state estimation in sensor networks, based on average dynamic consensus of the continuous measurements. While communication between nodes is discrete and heavily reduced due to the event-triggering mechanism, our method ensures that the nodes are still able to produce a continuous estimate of the global average measurement and the state of the plant, within some tuneable error bounds.

eess.SY↗

arXiv2022

Event-based Visual Tracking in Dynamic Environments

Visual object tracking under challenging conditions of motion and light can be hindered by the capabilities of conventional cameras, prone to producing images with motion blur. Event cameras are novel sensors suited to robustly perform vision tasks under these conditions. However, due to the nature of their output, applying them to object detection and tracking is non-trivial. In this work, we propose a framework to take advantage of both event cameras and off-the-shelf deep learning for object tracking. We show that reconstructing event data into intensity frames improves the tracking performance in conditions under which conventional cameras fail to provide acceptable results.

cs.CV↗