Nonlinear Estimation Via Kalman Filtering

The extended Kalman-Bucy filtering algorithm is applied to the problem of estimating the system parameters of a single-channel missile attitude control system. In particular, noisy observations of body angular rate, normal acceleration, and control surface deflec tion are used in the algorithm to obtain estimates of body angular rate, angle of attack, control surface deflection, air density, and missile velocity. Computational results obtained for this problem are presented. These results are evaluated to determine the applicapility of this estimator as a part of an adaptive control scheme. In addition, a general approach to the formulation of practical nonlinear estimation problems is suggested.     

The Detection of Moderately-Fluctuating Rayleigh Targets

Pulse train detection of fluctuating targets whose coherence time is long compared to the time between pulses and short compared to the pulse train duration is compared for two systems: (1) a recursive digital optimal receiver operating in conjunction with a coherent pulse train transmitter, and (2) a good (but not optimal) receiver operating with a block coherent frequency-hopping transmitter. The equivalence of this type of problem to noise-in-noise detection problems is demonstrated, the performance relations for both systems are derived and comparisons are made.