Optimal adaptive filter realizations for sampled stochastic processes with an unknown parameter

Optimal adaptive filter for sampled stochastic processes with unknown paramete

Digital matched filters for detecting Gaussian signals in Gaussian noise

Digital filters for detecting random signals in random nois

Comments on linear feature extraction

Linear transformation method for random data vector reduction by matrix algebr

Advanced Communication Theory Techniques TECHNICAL DOCUMENTARY REPORT NO. ASD-TDR-63-186

Under this contract a number of topics have been studied and analyzed in detail in order to bring together and somewhat extend the concepts of communication theory as they apply to some current problems in digital communication systems. Radio wave channels are characterized by a model\u27 which accounts for both multiplicative and additive disturbances, A large amount of experimental data pertaining to radio disturbances is evaluated and correlated. She. importance of the Rayleigh fading channel is emphasized and previous work is extended to determine the capacity and efficiency of the Rayleigh, channel. Detection theory concepts have been extended to treat the problem of signal detection in the presence of statistically unknown additive disturbances. Several detectors based on non-parametric statistical techniques are treated in detail. Obese detectors are compared to the conventional likelihood detectors. Design procedures are formulated. Signal design techniques are used to optimize transmitted wave- forms and the improvement in system performance is determined. The criterion used in this\u27 analysis is the minimization of intersymbol influence and the minimization of transmitter power for a fixed probability of received, errors . The tradeoffs available between transmitter power and coding complexity are thoroughly investigated for the binary symmetric channel. Results are obtained for both Hamming and Bose-Chandhuri codes. Recommendations for further work in promising areas are made, the need to supplement theoretical work with experimental work is pointed ou

Multisensor estimation: New distributed algorithms

The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. The proposed distributed algorithms are theoretically interesting and computationally attractive

Nonlinear filtering for LIDAR signal processing

LIDAR (Laser Integrated Radar) is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF), which has been the standard nonlinear filter in past engineering applications