Interacting Multiple Model Approach Research Articles

Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach

The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting de facto its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.

Indirect Estimation of Tire Pressure on Several Road Pavements via Interacting Multiple Model Approach

Generally, tire deflation results in a decrease in both handling performance and tire lifetime, and in fuel consumption increment. Therefore, the real-time knowledge of the pressure is important. Direct approaches via pressure sensors mounted on the rim of each tire are not practical, due to technical and economic reasons. Cost-effective solutions with real-time estimation of tire pressure are generally less accurate and reliable than direct ones. Dynamical estimators based on a suspension model need road surface topology information to compute disturbances on the suspension system as an input, which is typically unknown. This paper proposes an innovative approach to estimate tire pressure indirectly, without actual road surface roughness information. A vertical suspension dynamic model is used to build several unscented Kalman filters, parametrised around different road surface topologies. These estimators are combined following the Interacting Multiple Model approach, which gives an acceptable estimation of tire stiffness through a weighted average obtained from a probabilistic model. A known linear static relationship between the tire stiffness and inflation pressure is utilized to indirectly estimate the tire inflation pressure. A Monte Carlo analysis has been performed on a wide range of driving scenarios and vehicle manoeuvres. The results of the estimation have been compared to those of a single unscented Kalman filter, in order to validate the effectiveness of the proposed solution and to highlight the improved performances in monitoring tire pressure.

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.

Fully Distributed Dynamic State Estimation With Uncertain Process Models

This paper studies the problem of distributed state estimation in the scenario where the process model of interest is not certain to any local agent. By extending the previously proposed distributed hybrid information fusion (DHIF) algorithm to this considered scenario, two algorithms are proposed by following the frameworks of two well-known multiple-model (MM) approaches, namely, the first-order generalized pseudo-Bayesian and interacting MM approaches. The extended algorithms inherit the advantages of the original DHIF algorithm and are hence fully distributed, robust against agents not directly sensing the target, and only require a single communication iteration among agents during each sampling interval. It is also shown in the case when the unknown underlying model/mode is fixed; all local agents are able to asymptotically identify the true underlying model/mode and estimate the state of interest simultaneously. Sufficient conditions are formulated. Simulations are shown to illustrate the analytical results as well as the performances of the proposed algorithms.

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles.

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

A Kullback–Leibler-Based IMM Information Filter for the Jump Markov System with Unknown Noise

This paper is concerned with the state estimation of the jump Markov system with the unknown measurement noise. The proposed algorithm is derived under the framework of Interacting Multiple Model approach, and the recently reported Kullback---Leibler (KL) divergence-based scheme is used for estimation fusion. To facilitate KL divergence-based scheme, the information state and Wishart distribution are, respectively, used to describe the state and the unknown precision matrix of the measurement noise. Specifically, at both the mixing and estimation fusion stages, the KL divergence-based fusion scheme is adopted to fuse the information matrices, information state vectors, and parameters of the Wishart distribution from all the modes. At mode-conditioned filtering stage, parallel noise adaptive cubature information filters are designed to recursively estimate the information states, information matrices, and the Wishart distributed noise parameters. Simulation results prove the efficacy of the proposed approach.

Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise

ABSTRACTThis article addresses the problem of tracking a manoeuvring target in a wireless sensor network (WSN) consisting of distance-measuring sensor nodes. In order to cope with target manoeuvres, an interacting multiple model (IMM) filter is applied to estimate the position and velocity of the target. The distance-dependent measurement error of sensors is formulated as both additive and multiplicative noise in the observation equation. To deal with nonlinearities in the process and observation equations and also to solve the problem of multiplicative measurement noise, a new particle filter (PF)-based IMM approach is developed. Furthermore, the multiple-model posterior Cramér-Rao lower bound (PCRLB) is derived in the presence of both additive and multiplicative noise and it is used to perform a sensor selection algorithm to reduce energy consumption in WSN nodes. Simulation results show the effectiveness of the proposed IMMPF and sensor selection algorithms in target tracking.

An IMM/EKF Approach for Enhanced Multitarget State Estimation for Application to Integrated Risk Management System

For the generic assessment and the total management of collision risks with multitraffic in complex driving situations, it is essential to estimate and represent the target vehicles' overall behaviors such as heading, yaw rate, absolute velocity and acceleration, and relative position and relative velocity, which are the state of the target vehicle with respect to the host vehicle. To achieve this, this paper describes an interacting multiple model (IMM) approach using extended Kalman filters (EKFs) to improve multitarget state estimation performance with utilization of automotive radars. Automotive radar is the best fitted vehicular surround sensing technology with respect to functionality, robustness, reliability, dependence on weather conditions, etc. In an application of the automotive radar, the most important issue is to handle an uncertain measurement model problem that is wandering on the target's physical boundary. To cope with this problem, multimodels are formulated, and a new multitarget tracking algorithm is developed based on the IMM approach, global-nearest-neighbor-based data association, and the EKF method with elaborated modeling of automotive radar. The performance of the proposed multitarget tracking algorithm is verified via vehicle tests on real roads. Moreover, a performance comparison with a model-switching algorithm, which is a simple approach to handling the multimodel problem, has been conducted. It is shown that the target vehicle's overall behavior can be estimated by the proposed elaborated models, and the estimation performance can be significantly enhanced by the proposed IMM-based algorithm.

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

IMM-based sensor fault detection and identification for a drive-by-wire vehicle

With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. This is particularly true in the case of partially and fully autonomous vehicles. In this paper, we focus our researches on a subpart of this issue dedicated to the detection and the identification of sensor faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach has been implemented, based on a non-linear vehicle dynamics observer, including tire-road force and actuator modeling. Experimental validation tests, relying on real vehicle signals, show a fast and robust fault identification.

An Interacting Multiple Model Approach for State Estimation with Non‐Gaussian Noise Using a Variational Bayesian Method

AbstractThe conventional interacting multiple models (IMM) approach for a hybrid system under the Gaussian assumption is limited for most real applications due to the noisy measurements often being in the presence of outliers. This paper aims at accommodating the IMM approach to the non‐Gaussian cases where outliers exist. In the proposed IMM algorithm, the Student‐t distribution is used to model the non‐Gaussian measurement noise. At the interaction step, the mixed statistics of the noise parameter under a Bayesian framework are obtained via a Gamma approximation and a recently reported moments matching method. To address the state noise‐coupled intractability in Bayesian filtering, a variational Bayesian method is utilized to approximate the posterior distributions of the noise and state recursively. The proposed algorithm is tested with a maneuvering target tracking example and is shown to be robust to the outliers.

Fault diagnosis and fault tolerant control for nonlinear satellite attitude control systems

In this paper, an integrated fault diagnosis and fault tolerant control scheme is investigated for nonlinear satellite attitude control systems. The fault diagnosis is performed by a novel interactive multiple model approach aiming at reducing the size of the model set. Unknown fault parameters are jointly estimated with system states by state augmentation and a bank of Unscented Kalman Filters. Based on the faulty information obtained from the fault diagnosis system, a proportional-derivative controller is used to recover the faulty system. Simulation results demonstrate the effectiveness of the proposed fault diagnosis and fault tolerant control scheme.

A COMPUTATIONALLY EFFICIENT APPROACH FOR TARGET TRACKING USING RECENT FINITE OBSERVATIONS

This paper proposes a computationally efficient approach for the target tracking of two-dimensional target systems with unknown maneuvers. To describe efficiently target with maneuvers, only two target models are developed. To estimate target trajectories and unknown maneuvers, two tracking filters using only the most recent finite observations are used due to fast estimation performance and low computation burden. It will be shown that constant-bias maneuvers do not induce the error in estimates of target trajectories. A detection method is also developed in order to indicate the presence of maneuvers and select the valid estimate from two filters. It is shown that the on-line computation amount of the proposed approach is much less than that of the well-known interacting multiple model (IMM) approach. Via extensive simulations, the performance of the proposed target tracking approach is evaluated by the comparison with existing IMM approaches.

State Estimation for Stochastic Linear Hybrid Systems with Continuous-State-Dependent Transitions: An IMM Approach

Hybrid systems consist of both continuous state evolutions and discrete state (or mode) transitions. In many hybrid systems, mode transitions are governed by guard conditions that are dependent on the continuous state. These kind of mode transitions can be described by continuous-state-dependent mode transition probabilities. However, most multiple model Kalman filter based algorithms, such as the well-known interacting multiple model (IMM) algorithm, assume constant mode transition probabilities. We propose a hybrid estimation algorithm based on the IMM approach for the stochastic hybrid systems with continuous-state-dependent transitions. We first develop two models to describe the guard conditions for various stochastic hybrid systems. From the models of the guard conditions, we derive the corresponding continuous-state-dependent mode transition probabilities, which are expressed in terms of Gaussian probability density functions (pdfs) or Gaussian cumulative density functions (cdfs). We then propose a hybrid estimation algorithm for the stochastic hybrid systems based on the IMM algorithm. Like the IMM algorithm, our algorithm utilizes Gaussian distributions to represent the continuous state pdfs which are updated analytically at each time step. We also use analytical integration methods to reduce the computational cost of updating the mode probabilities. As a result, the proposed algorithm is computationally efficient and is applicable to high-dimensional problems. The performance of the proposed algorithm is illustrated with two examples in air traffic control tracking applications.

A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. In this paper, we provide an effective solution to the tracking of multiple single-pixel maneuvering targets in a sequence of infrared images by developing an algorithm that combines a sequential probabilistic multiple hypothesis tracking (PMHT) and interacting multiple model (IMM). We explicitly model maneuver as a change in the target's motion model and demonstrate its effectiveness in our tracking application discussed in this paper. We show that inclusion of IMM enables tracking of any arbitrary trajectory in a sequence of infrared images without any a priori special information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the observation origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses observation association as missing data.

An Expectation–Maximization-Based Interacting Multiple Model Approach for Cooperative Driving Systems

In this paper, we present a novel combined sensor registration and fusion approach for cooperative driving in intelligent transportation systems (ITSs). A realistic augmented registration and fusion-state space model in three dimensions is first developed for dissimilar sensors. In order to have unbiased sensor registration parameter estimates, the expectation-maximization (EM) algorithm is incorporated with the extended Kalman filter (EKF) to give simultaneous state and parameter estimates. Furthermore, the interacting multiple model (IMM) filter is introduced here for collaborative driving in order to deal with the jumping model problem occurred in different vehicles driving status. To evaluate the registration and fusion performance, a new recursive relationship is derived theoretically for computing the posterior Cramer-Rao bound (PCRB). It is shown by simulation that the proposed EM-IMM-EKF method has a more robust estimation performance than the conventional approach. The performance is furthermore verified by comparing the mean square error with the PCRB.

Integrated active fault-tolerant control using IMM approach

An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained.

The IMM Approach to the Fault Detection Problem

An observer-based fault detection scheme utilizing the interacting multiple model (IMM) approach is presented for an industrial actuator benchmark problem. The requirement is to detect current and position faults in a brushless D.C. motor which is subject to both actuator and sensor faults in the presence of unknown load torques. Fault detection is carried out by threshold checking on the model probabilties calculated by the algorithm. The proposed scheme has been applied to the given benchmark data with current and position fault detection achieved in a good detection time and with a high detection probabilty, regardless of the load torque.