This paper proposes a Distributed Moving Horizon Estimation (DMHE) approach performed by an external static Sensor Network (SN) composed of surveillance cameras and their associated low-cost computers. This approach allows to localize a non-cooperative Multi-Vehicle System (i.e. intruder vehicles which do not communicate with the SN) from sporadic measurements. In this context, measurements are available at time instants a priori unknown and the proposed DHME technique is designed to face this issue by resorting to time dependent parameters in the problem formulation. Moreover, this technique is well-suited to better estimate the state of the intruder vehicles thanks to its capability to efficiently exploit environmental information via constraints. In fact, when dealing with sporadic measurements and biased noisy sensors data, the use of output constraints can contribute to locally enhance the estimation accuracy. In order to confirm its effectiveness, the proposed method is validated on an experimental setup (video presentation available at https: //youtu.be/1CkSba2wVuI) within an indoor arena equipped with a motion capture system. Three scenarios are considered for the localization of a non-cooperative Multi-Vehicle System composed of five robots, where the proposed DMHE technique is performed using sporadic position measurements provided by an external static Sensor Network with low-cost cameras (webcams) and computers (Raspberry PI) connected to them. Rigorous comparisons in terms of computation time and accuracy of the estimates highlight the efficacy of the proposed approach.

Multi-vehicle localization by distributed MHE over a sensor network with sporadic measurements: Further developments and experimental results

Venturino, A
;
2023-01-01

Abstract

This paper proposes a Distributed Moving Horizon Estimation (DMHE) approach performed by an external static Sensor Network (SN) composed of surveillance cameras and their associated low-cost computers. This approach allows to localize a non-cooperative Multi-Vehicle System (i.e. intruder vehicles which do not communicate with the SN) from sporadic measurements. In this context, measurements are available at time instants a priori unknown and the proposed DHME technique is designed to face this issue by resorting to time dependent parameters in the problem formulation. Moreover, this technique is well-suited to better estimate the state of the intruder vehicles thanks to its capability to efficiently exploit environmental information via constraints. In fact, when dealing with sporadic measurements and biased noisy sensors data, the use of output constraints can contribute to locally enhance the estimation accuracy. In order to confirm its effectiveness, the proposed method is validated on an experimental setup (video presentation available at https: //youtu.be/1CkSba2wVuI) within an indoor arena equipped with a motion capture system. Three scenarios are considered for the localization of a non-cooperative Multi-Vehicle System composed of five robots, where the proposed DMHE technique is performed using sporadic position measurements provided by an external static Sensor Network with low-cost cameras (webcams) and computers (Raspberry PI) connected to them. Rigorous comparisons in terms of computation time and accuracy of the estimates highlight the efficacy of the proposed approach.
2023
Distributed Moving Horizon Estimation
Constrained state estimation
Sensor Networks
Sporadic measurements
Multi-Vehicle Systems' localization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/360762
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