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

This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods $the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

Survey of maneuvering target tracking. Part V. Multiple-model methods

A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an online adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.

Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots

This paper provides an introduction to sensor fusion techniques for target tracking. It presents an overview of common filtering techniques that are effective for moving targets as well as methods of overcoming problems specific to target tracking, such as measurement-to-track association and sensor registration. The computational demand of such algorithms is discussed and various practices, including distributed processing of target tracks and sensor management, are proposed to help reduce this demand. Final comments include a discussion of applications and implementation issues specific to the presented scenarios.

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Tutorial on multisensor management and fusion algorithms for target tracking

Bi/multi-static radars using cooperative or noncooperative transmitters have attracted wide attention. This paper analyzes the method of locating a moving target from corrupted Doppler frequency measurements obtained by single or multiply receivers located together and with corresponding cooperative or noncooperative transmitters distributing in remote places. Both the observability requirements in two dimensions and the expressions for the Cramer-Rao bound are derived. Further analysis with simulation results reveals the relationship between the performance and the number of transmitters and the sensitivity between the performance and the time integration. It is shown that the bi/multi-static radars can exploit only Doppler information to locate a moving target effectively.

Observability and Performance Analysis of Bi/Multi-Static Doppler-Only Radar

In many multisensor systems the number and type of sensors supporting a particular target track can vary with time due to the mobility, type, and resource limitations of the individual sensors. This variability in the configuration of the sensor system poses a significant problem when tracking maneuvering targets because of the uncertainty in the target motion model. A Kalman filter is often employed to filter the position measurements for estimating the position, velocity, and acceleration of a target. When designing the Kalman filter, the process noise (acceleration) variance Q/sub k/ is selected such that the 65 to 95% probability region contains the maximum acceleration level of the target. However, when targets maneuver, the acceleration changes in a deterministic manner. Thus, the white noise assumption associated with the process noise is violated and the filter develops a bias in the state estimates during maneuvers. The problem of tracking maneuvering targets with multiple sensors is illustrated through an example involving target motion in a single coordinate in which it is shown that with two sensors one can have (under certain conditions that include perfect alignment of the sensors) worse track performance than a single sensor. The Interacting Multiple Model (IMM) algorithm is applied to the illustrative example to demonstrate a potential solution to this problem of track filter performance.

T. Bar-Shalom

Papers

Tracking maneuvering targets with multiple sensors: does more data always mean better estimates?