A Multihypothesis Sequential Probability Test for Fault Detection and Identification of Vehicles' Ultrasonic Parking Sensors

Mamoun F Abdel-Hafez

doi:10.1155/2011/137671

Abstract

This paper presents a sequential fault detection and identification algorithm for detecting a fault in a vehicle's ultrasonic parking sensors. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual that is only a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors' reading. The accuracy and convergence characteristics of the proposed algorithm are verified using experimental results. This study is essential to ensure accurate operation of vehicle's ultrasonic parking sensors.

Highlights

The problem of a fault occurring in dynamic systems is being addressed continuously by researchers due to its high impact on the accuracy and integrity of the overall system performance [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
Extensive study is focused on the integrity monitoring of navigation systems [1,2,3,4,5]
A sensor fault can be modeled in two ways first, as an additive random process with an associated statistics, that is, mean and covariance [1,2,3]

Summary

Introduction

The problem of a fault occurring in dynamic systems is being addressed continuously by researchers due to its high impact on the accuracy and integrity of the overall system performance [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. Upon the onset of a fault, the observations’ probability density will change to another distribution that needs to be identified

Methods

Results

Conclusion