Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

An introduction to multisensor data fusion

The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm/s. >

Mobile robot localization by tracking geometric beacons

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations.

Interacting multiple model methods in target tracking: a survey

Interacting Multiple Model Methods in Target Tracking: A Survey

List of Figures. List of Tables. List of Abbreviations and Symbols. Preface. 1. Introduction. 2. A Sonar Sensor Model. 3. Model-Based Localization. 4. Map Building. 5. Simultaneous Map Building and Localization. 6. Directed Sensing Strategies. 7. Why Use Sonar? A. Hardware and Software. Bibliography. Index.

Directed Sonar Sensing for Mobile Robot Navigation

The purpose of this paper is to examine several Kalman filter algorithms that can be used for state estimation with a multiple sensor system. In a synchronous data collection system, the statistically independent data blocks can be processed in parallel or sequentially, or similar data can be compressed before processing; in the linear case these three filter types are optimum and their results are identical. When measurements from each sensor are statistically independent, the data compression method is shown to be computationally most efficient, followed by the sequential processing; the parallel processing is least efficient.

Kalman filter algorithms for a multi-sensor system

An iterative least square estimation algorithm is applied to the problem of state estimation of ballistic trajectories with angle-only measurements. A filter initiation procedure is suggested. The application of trajectory a priori knowledge for improving the state estimate is discussed and solved as a constrained estimation problem. A Monte Carlo simulation study was conducted to evaluate these techniques. It was found that the iterative least square filter achieves the Cramer-Rao bound while the extended Kalman filter generally does not.

Ballistic trajectory estimation with angle-only measurements

In this paper, we describe an algorithm for correlating measurements from several sensors This is a problem area in multiple sensor tracking in a dense target environment It is shown that the correlation problem is similar to the assignment problem in operation research with assisgnment penalties being equal to the sufficient statistic of the generalized likelihood ratio test

Measurement correlation for multiple sensor tracking in a dense target environment

In this paper, we present a recursive generalized likelihood ratio (GLR) test algorithm for detecting sudden changes in linear discrete systems. We demonstrate the application of linear filtering techniques to obtain a recursive GLR algorithm so that the requirement for matrix inversions in the previously known GLR algorithms can be reduced or avoided. Furthermore, the GLR algorithm is extended to the case when the sudden change follows known linear dynamics. An adaptive filtering scheme which uses the input estimate to correct the state estimate is also presented for the time-varying input case.

On GLR detection and estimation of unexpected inputs in linear discrete systems

: The purpose of this report is to examine several Kalman filter algorithms that can be used for state estimation with a multiple sensor system. These algorithms are described in detail and their results are compared with a suboptimum tracking algorithm which processes only multiple range measurements. A state estimate compression algorithm is also described. Various radar measurement transformation formulas are listed. Algorithms for a nonsynchronous data collection system are not examined in detail but possible approaches are suggested.

C. Chang

Papers

Kalman filter algorithms for a multi-sensor system

Ballistic trajectory estimation with angle-only measurements

Measurement correlation for multiple sensor tracking in a dense target environment

On GLR detection and estimation of unexpected inputs in linear discrete systems

Kalman filter configurations for multiple radar systems