Adaptive H∞ robust beamforming for imperfect antenna array
Signal Processing
H ∞Filtering for a Mobile Robot Tracking a Free Rolling Ball
RoboCup 2006: Robot Soccer World Cup X
Decentralized H∞ filtering in a multi-agent system
ACC'09 Proceedings of the 2009 conference on American Control Conference
H∞ estimation for fuzzy membership function optimization
International Journal of Approximate Reasoning
Comparison of sensor fusion methods for an SMA-based hexapod biomimetic robot
Robotics and Autonomous Systems
Frequency-selective and nonlinear channel estimation with unknown noise statistics
IEEE Communications Letters
Efficient multisensory barrier for obstacle detection on railways
IEEE Transactions on Intelligent Transportation Systems
Automatica (Journal of IFAC)
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A finite-horizon discrete H∞ filter design with a linear quadratic (LQ) game approach is presented. The exogenous inputs composed of the “hostile” noise signals and system initial condition are assumed to be finite energy signals with unknown statistics. The design criterion is to minimize the worst possible amplification of the estimation error signals in terms of the exogenous inputs, which is different from the classical minimum variance estimation error criterion for the modified Wiener or Kalman filter design. The approach can show how far the estimation error can be reduced under an existence condition on the solution to a corresponding Riccati equation. A numerical example is given to compare the performance of the H∞ filter with that of the conventional Kalman filter