Multiposition alignment of strapdown inertial navigation system

The authors demonstrate that the stationary alignment of strapdown inertial navigation system (SDINS) can be improved by employing the multiposition/technique. Using an observability analysis, it is shown that an optimal two-position alignment not only satisfies complete observability conditions but also minimizes alignment errors. This is done by analytic rank testing of the stripped observability matrix and numerical calculation of the error covariance. It is also shown that an optimal three-position alignment accelerates the convergence of the alignment error compared with two-position alignment     

Adaptive Tracking Filter for Maneuvering Targets

A general method of continually restructuring an optimum Bayes-Kalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om; puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter.     

Angular Accuracy of a Scanning Radar Employing a Two-Pole Filter

A two-pole filter is proposed as a detector for a scanning radar. The optimum values of the filter coefficients are found and are approximated by a simple expression. The optimum two-pole filter requires a 0.15-dB increase in signal-to-noise ratio in order to provide the same detection capability as the optimum detector, and yields azimuth estimates whose standard deviation are within 15 percent of the Cramér-Rao lower bound. The estimator is simple to implement, avoiding the storage requirements of the moving window detector and the bias complications of the feedback integrator.     

Ground Testing of Inertial Navigators to Improve Dynamic Performance

A ground testing method has been devised to evaluate the dynamic errors of an inertial navigation system. A trimmed stationary inertial system in the navigation mode can be subjected to programmed platform orm drift rates, and generate position outputs which are compared with those of a perfect navigator. The linearized error equations for this testing mode are derived and the resulting position error propagation is analyzed using Kalman filtering in order to identify the error sources. A simulated platform with a fixed set of error sources is analyzed to evaluate this testing concept. An example is presented to show that the gyro and accelerometer scale factor and misalignment error coefficients can be estimated.     

An exact expression for resolution cell area in special case ofbistatic radar systems

An exact expression for the bistatic resolution-cell area (A _B) is developed for the special case in which either the transmit or receive antenna has a broad azimuth beamwidth or is omnidirectional. Quantitative examples are presented to illustrate the variation of A_B with location. A comparison is made with the performance of a commonly used approximation formula     

Modeling of dynamic evolution of coronal loops

Parameters of expanding magnetic loops and arches and of mass flows generated by them in the corona have been computed in a 1D two-fluid approximation. Two possible trigger mechanisms of the coronal transients have been considered: (i) sudden increase of the background magnetic field strength, and (ii) heating and compression plasma inside these magnetic structures. We discuss the formation of shock waves and their dependence on dynamics and geometry of the magnetic structures.     

Radar Target Detection and Map-Matching Algorithm Studies

Results of a study of adaptive threshold target detection and map-matching algorithms are presented. Log threshold processing is shown to be preferred over linear threshold processing when the clutter data surrounding the target cell is contaminated by other targets, decoy corner reflectors, or bright clutter cells. Whereas previous studies have resorted to extensive Monte-Carlo simulations of log threshold algorithms, the results were obtained using a novel analytical approach based upon Parseval's theorem.