This newly revised and greatly expanded edition of the popular Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems offers you a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching . It provides both an introduction to navigation systems and an in-depth treatment of INS/GNSS and multisensor integration. The second edition offers a wealth of added and updated material, including a brand new chapter on the principles of radio positioning and a chapter devoted to important applications in the field. Other updates include expanded treatments of map matching, image-based navigation, attitude determination, acoustic positioning, pedestrian navigation, advanced GNSS techniques, and several terrestrial and short-range radio positioning technologies. The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning. DVD Included: Features eleven appendices, interactive worked examples, basic GNSS and INS MATLAB simulation software, and problems and exercises to help you master the material.

Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition

An efficient, federated Kalman filtering method is presented, based on rigorous information-sharing principles. The method applies to decentralized navigation systems in which one or more sensor-dedicated local filters feed a larger master filter. The local filters operate in parallel, processing unique data from their local sensors, and common data from a shared inertial navigation system. The master filter combines local filter outputs at a selectable reduced rate, and yields estimates that are globally optimal or subset-optimal. The method provides major improvements in throughput (speed) and fault tolerance, and is well suited to real-time implementation. Practical federated filter examples are presented, and discussed in terms of structure, accuracy, fault tolerance, throughput, data compression, and other real-time issues. >

Federated filter for fault-tolerant integrated navigation systems

This paper describes federated filter applications to integrated,
fault-tolerant navigation systems, with emphasis on real-time
implementation issues and numerical simulation results. The
federated filter is a near-optimal estimator for decentralized,
multi-sensor data fusion. Its decentralized estimation archi-
tecture is based on theoretically sound information-sharing
principles. A federated filter consists of one or more sensor-
dedicated local filters, generally operating in parallel, plus a
master combining filter. The master filter periodically com-
bines (fuses) the local filter solutions to form the best total
solution. Fusion generally occurs at a reduced rate, relative
to the local measurement rates. The method can provide sig-
nificant improvements in data throughput, fault tolerance, and
system modularity. Numerical simulation results are pre-
sented for an example multi-sensor navigation system. These
results demonstrate federated filter performance characteristics in terms of estimation accuracy, fault-tolerance, and
computation speed.
This work was supported by the Defense Small Business Innovation

Federated Kalman filter simulation results

A survey of advances in vision-based vehicle re-identification

https://academic.csuohio.edu/simond/pubs/Safari2014.pdf

Multirate multisensor data fusion for linear systems using Kalman filters and a neural network

Common Kalman Filter is an ongoing U.S. Air Force Avionics Laboratory program to develop, simulate. and evaluate computational techniques and software architectures that will enhance the robustness and reliability of Integrated navigation systems for the next generation of military aircraft. The project addresses four areas: filter mechanization and architecture including decentralized filtering; fault detection and isolation; system software reconfiguration strategies; and exploitation of advanced avionics technologies such as VHSIC, parallel processing and Pave Pillar data bus architectures. Problems of Kalman filter throughput, cascaded filter instability, and the high rate data interface requirements of decentralized filtering are being addressed. 

Decentralized filtering is a natural technique for tying together the many sensors of the modern navigation suite aboard the next-generation fighter. Each navigation sensor typically has been designed as a locally autonomous navigation system, with its own dedicated Kalman filter. The decentralized filter framework ideally would improve fault tolerance and fault recovery by retaining the autonomy of the filters and at the same time, preserve navigation accuracy by maintaining a central master filter with a globally optimal or near optimal solution, while enhancing throughput in a distributed processing environment. 

The decentralized, fault-tolerant filter scheme described here demonstrates the Carlson technique of trading off optimality of the master solution to gain an extra measure of fault tolerance and throughput. The small accuracy loss due to suboplimality is more than compensated by the increase in robustness of the navigation solution. The navigation sensor suite around which this is designed represents the avionics suite of the next-generation fighter aircraft, with dual inertial units, GPS, JTIDS, SIT AN, and other landmark reference sensors.
 
This paper describes the CKF design which is nearing maturity and Is currently entering a computer simulation test cycle. Approaches to sensor information requirements definition, local sensor integration/filtering, master filter modeling, and automatic fault detection/isolation and system reconfiguration are discussed. Failure modes are described which will be the basis for the evaluation of CKF failure response in subsequent simulations.

Neal A. Carlson

Papers

Federated Kalman filter simulation results

Common Kalman Filter: Fault-Tolerant Navigation for Next Generation Aircraft