Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

Abstract

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

Sensor Modalities and Fusion for Robust Indoor Localisation

“…Most of the approaches use Deep Neural Networks (DNN) [84,94,161]. While there exists a body of literature dedicated to objectivespecific fusion methods using ANN [151,154,155,176], there is an evident lack of standarisation between the positioning methods, and it still remains largely unexplored.…”

“…Interestingly, ANN has often been used as a preprocessing step before actual fusion [154,155,161]. Whilst not strictly related to indoor positioning application, Vargas-Meléndez et al [154,155] used an ANN to estimate the pseudo roll angle of a vehicle, before performing fusion based on a PF. Wang et al [161] performed indoor localisation, using CSI and deep learning.…”

Design of Low-Cost Vehicle Roll Angle Estimator Based on Kalman Filters and an IoT Architecture

“… The Roll Angle Estimator implements Linear and Unscented Kalman Filters to estimate roll angle in real time using a two Degree of Freedom (DoF) which represents the vehicle roll motion. A description of this model is presented in [ 35 ] and summarized in Section 2.1.5 . As observation measurements required for both Kalman filters, lateral acceleration, roll rate and time are considered.…”

“…The model used in this research is presented in [ 35 ]. This model describes the roll vehicle motion in a discrete-time system (see Figure 5 ) where: …”

“…The Unscented Kalman filter introduces a workaround that transforms the curve to a bunch of points before executing the Kalman filter [ 34 ]. The difference between Linear and Unscented Kalman Filters is relatively small, but in the cases when the cumulative effect of small errors can lead to relevant errors in the estimations provided (such as vehicle roll angle estimation), the use of an Unscented Kalman Filter can be appropriate [ 35 ].…”