Henk A. P. Blom

Bio: Henk A. P. Blom is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Air traffic control & Air traffic management. The author has an hindex of 34, co-authored 168 publications receiving 5992 citations. Previous affiliations of Henk A. P. Blom include National Aerospace Laboratories & Leiden University.

The interacting multiple model algorithm for systems with Markovian switching coefficients

Abstract: An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients. >

Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm

Abstract: Two maneuvering-target tracking techniques are compared. The first, called input estimation, models the maneuver as constant unknown input, estimates its magnitude and onset time, and then corrects the state estimate accordingly. The second models the maneuver as a switching of the target state model, where the various state models can be of different dimension and driven by process noises of different intensities, and estimates the state according to the interacting multiple model (IMM) algorithm. While the first requires around twenty parallel filters, it is shown that the latter, implemented in the form of the IMM, performs equally well or better with two or three filters. >

An efficient filter for abruptly changing systems

Abstract: For a linear discrete time system with Markovian coefficients a new filtering algorithm is given, which is called the Interacting Multiple Model (IMM) algorithm. The mathematical support for this algorithm is outlined and a qualitative comparison with other known filtering algorithms is made. The main conclusion is that the ratio between performance and computational complexity is far better for the IMM algorithm.

Probabilistic data association avoiding track coalescence

Abstract: For the problem of tracking multiple targets, the joint probabilistic data association (JPDA) approach has shown to be very effective in handling clutter and missed detections. The JPDA, however, tends to coalesce neighboring tracks and ignores the coupling between those tracks. Fitzgerald (1990) has shown that hypothesis pruning may be an effective way to prevent track coalescence. Unfortunately, this process leads to an undesired sensitivity to clutter and missed detections, and it does not support any coupling. To improve this situation, the paper follows a novel approach to combine the advantages of JPDA coupling, and hypothesis pruning into new algorithms. First, the problem of multiple target tracking is embedded into one filtering for a linear descriptor system with stochastic coefficients. Next, for this descriptor system, the exact Bayesian and new JPDA filters are derived. Finally, through Monte Carlo simulations, it is shown that these new PDA filters are able to handle coupling and are insensitive to track coalescence, clutter, and missed detections.

Exact Bayesian and particle filtering of stochastic hybrid systems

Abstract: The standard way of applying particle filtering to stochastic hybrid systems is to make use of hybrid particles, where each particle consists of two components, one assuming Euclidean values, and the other assuming discrete mode values. This paper develops a novel particle filter (PF) for a discrete-time stochastic hybrid system. The novelty lies in the use of the exact Bayesian equations for the conditional mode probabilities given the observations. Therefore particles are needed for the Euclidean valued state component only. The novel particle filter is referred to as the interacting multiple model (IMM) particle filter (IMMPF) because it incorporates a filter step which is of the same form as the interaction step of the IMM algorithm. Through Monte Carlo simulations, it is shown that the IMMPF has significant advantage over the standard PF, in particular for situations where conditional switching rate or conditional mode probabilities have small values

Sequential Monte Carlo methods in practice

Abstract: Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

The interacting multiple model algorithm for systems with Markovian switching coefficients

Abstract: An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients. >

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Abstract: Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A review of conflict detection and resolution modeling methods

Abstract: A number of methods have been proposed to automate air traffic conflict detection and resolution (CDR), but there has been little cohesive discussion or comparative evaluation of approaches. The paper presents a survey of 68 CDR modeling methods, several of which are currently in use or under operational evaluation. A framework that articulates the basic functions of CDR is used to categorize the models. The taxonomy includes: dimensions of state information (vertical, horizontal, or three-dimensional, 3-D); method of dynamic state propagation (nominal, worst case, or probabilistic); conflict detection threshold; conflict resolution method (prescribed, optimized, force field, or manual); maneuvering dimensions (speed change, lateral, vertical, or combined manoeuvres); and management of multiple aircraft conflicts (pairwise or global). An overview of important considerations for these and other CDR functions is provided, and the current system design process is critiqued.