This paper describes the design, implementation, and performance of a real-time multiconfiguration Kalman filter for high-performance Navstar global positioning system (GPS) navigation. The design provides extreme flexibility in order to operate with a wide variety of host sensors. It configures automatically (four filter configurations) based upon the host vehicle requirements and sensor availability, in order to process GPS measurements and provide the best estimate of the navigation states. Two new techniques, namely an unaided dead-reckoning Kalman filter implementation and an automatic inertial platform tilt estimation control scheme, are developed to improve the navigation accuracy, especially for high-dynamics applications. Performance results are presented to demonstrate the advantages of these techniques.

Multiconfiguration Kalman filter design for high-performance GPS navigation

Various important aspects of the satellite orbit determination problem are critically discussed with a view to emphasizing the importance of a wise choice of suitable system models, coordinates sets, and estimators. We describe several aspects of the orbit determination process and also review much of the available literature on the application of Kalman-filter type algorithms to the problem of near-Earth, geosynchronous, and deep-space mission type orbit determination. Some features of on-board orbit determination are also addressed. It is believed that this review with a touch of tutorial will enable engineers and scientists to arrive at an orbit determination methodology (ODM) that will have attributes of good numerical stability, efficiency, and precision in orbit estimation.

/pdf/on-the-orbit-determination-problem-4khqatgk0r.pdf

On the Orbit Determination Problem

This bibliography on the Kalman filter was obtained from the Cornell University Library Resources computer network. The four indexes used were Agricola, Biosis pre-1993, Biosis post-1992, and CAB. The references to Kalman filtering found in these indexes are listed together as one bibliography in Section 2 of the paper. Identification of content for each citation is listed after the reference. The Current Index in Statistics was used to obtain the bibliography given in Section 3 . By not combining the Current Index with the others, it is possible to observe the completeness of the Current Index relative to the others. References in the physical sciences fields for the most part are not included in this bibliography unless they appeared in these indexes. References on applications for the most part come from the ·fields of agriculture, biology, and medicine.

/pdf/kalman-filter-bibliography-agriculture-biology-and-medicine-2gcah6s8c6.pdf

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

One of the steps in creating a mathematical model of a system is to test the model after it has been fully specified, to see whether it is performing adequately. Often, it is found that the model is not performing acceptably (e.g. the model is not giving accurate predictions of the performance of the actual system). The same lack of fidelity can also be observed in established models that had been performing well, indicating a change in the actual system. At this point, it is necessary to diagnose where the problem in the model lies; a process called error isolation. An error isolation technique for detecting the misspecified parameter (or set of parameters) is described. This technique is especially designed for use on state-space models of large-scale systems. The authors report on an example of an application of the methodology to localizing errors in the model of an inertial navigation system. >

Isolating errors in models of complex systems

The fundamental TPBVP usually underlying true "optimal sensor selection strategy" is revisited to obtain practical real-time mechanizations as a solution to an exclusively initial value problem. >

Further comments on "Optimal sensor selection strategy for discrete-time estimators" [and reply]

The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described. The SPWR algorithm for this application was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented covariance and filter gains at a slower rate than the state measurement update, and it uses U-D factor formulation to perform covariance computations. The SPWR algorithm saves real-time processing requirements without appreciable degradation of filter performance. Another important feature of the SPWR algorithm is that it incorporates pseudorange and delta-range data from each GPS satellite sequentially for navigation solution. The SPWR algorithm, for this application, was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented.

John N. Damoulakis

Papers

Sequential Piecewise Recursive Filter for GPS Low-Dynamics Navigation