Direct-Ranging Loran Model Identification and Performance Predictions

With the availability of modern data processing techniques and lowcost stable time references, the use of Loran in a direct-ranging mode offers certain potential advantages. In order to generate reliable direct-ranging Loran (DRL) performance projections and system designs, however, accurate system error models are required. This paper first describes the processing of airborne flight data from a DRL receiver to identify models for significant DRL system errors. These models are then used in a Kalman filter covariance simulation to generate performance predictions for an optimally integrated DRL system. Comparisons with conventional hyperbolic Loran are also given. DRL is shown to be capable of substantially improved position accuracy over that of conventional hyperbolic Loran, especially in regions of poor geometry. A stationary ground-align technique for improving DRL performance is also discussed.     

Applications of Mininum Variance Reduced-State Estimators

This paper presents an algorithm for a class of suitably constrained reduced-order filters which minimize the variance of the estimated variables. The algorithm generates both the filter gain history and the true estimation error covariance. The algorithm provides a quantitative criterion which can be used to measure the performance of any reduced-order estimator. Both continuous and discrete estimators are considered. Several examples are treated including an application of the technique to a hybrid navigation system of high order.