A novel approach is proposed to H2 finite impulse response (FIR) prediction in discrete-time state-space. The biased-constrained H2 optimal unbiased FIR (H2-OUFIR) predictor derived under disturbances and measurement errors is shown to have the maximum likelihood form and be equivalent to the OUFIR predictor under Gaussian noise. The derivation is provided using the backward Euler method by minimizing the squared weighted Frobenius norm. A bias-constrained suboptimal H2 FIR filtering algorithm using the linear matrix inequality is also designed. The H2-OUFIR predictor performance is investigated by simulations and experimentally in a comparison with the Kalman and unbiased FIR predictors.

A well-known problem of visual object tracking is the difficulty of accurately estimating the object trajectory under conditions of environmental disturbances in the bounding box (BB) of a video camera. In this paper, we consider BB variations as Gaussian-Markov colored measurement noise (CMN). In order to perform accurate tracking in the presence of CMN, we use measurement differencing and develop a robust general unbiased finite impulse response (GUFIR) filter and use the general Kalman filter (GKF) as a benchmark. The GUFIR and GKF algorithms are tested by the “Car4" benchmark. It is shown that, in terms of the tracking precision and under the heavy disturbance with the 0.65 ⩽ Ψ ⩽ 0.95 coloredness factor, the best tracking performance is achieved using the robust GUFIR filter. When Ψ < 0.6, the GUFIR and GKF algorithms perform near equally. In the extreme point of Ψ = 1.0, where the Gauss-Markov CMN loses the stationarity, both algorithms provide zero precision and become inefficient. In general, it is concluded that the GUFIR filter, which ignores any zero mean disturbance and initial values, is much more suitable for applications in visual object tracking than Kalman-like algorithms relying on complete object information.

This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.

In the present work, a biocatalytic glucose optical sensor produced by immobilizing glucose oxidase (GOD) as a recognition molecule over a PMMA (polymethylmethacrylate) optical fiber is introduced. An enzymatic encapsulation process was carried out using the sol–gel method, depositing a TEOS-based coating by immersion at the end of an optical fiber; the biosensor was characterized using different glucose levels. Finally, the best way to encapsulate the enzyme and prevent it from degrading is to perform the process at room temperature, and later implement the deposition of the coating on the fiber. The drying process was optimal below 8 °C.