This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filte...

Sigma-delta (Σ-Δ) modulation techniques have moved into mainstream applications in signal processing and have found many practical uses in areas such as high-resolution A/D, D/A conversions, voice communication, and software radio. Σ-Δ modulators produce a single, or few bits output resulting in hardware saving and thus making them suitable for implementation in very large scale integration (VL...

The practical aspects of efficient implementation of the Kalman Filter Bank for scenario analysis are considered. Such implementation have to shorten running time, computational cost, and memory demands, required for computer model of the Kalman Filter Bank. The first requirement is met by concurrent computing. The second and third requirements are satisfied by substitution the Equivalent Kalma...

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

The computational efficiency of a sampling based nonlinear Kalman filtering process is mainly conditional on the number sigma/sample points required by filter at each time step to effectively quantify statistical properties related states and parameters. Efficaciously minimizing needed would therefore have important implications, especially for large n-dimensional systems. A set minimum n + 1 s...

This paper introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of Kalman (LKF) that leverages sliding mode theory. SILF incorporates boundary layer in measurement update formulation, enabling filter innovation to slide within predefined upper and lower bounds. enhances robustness SILF, making it resilient model uncertainties noise. Additionally, derivative-free formulati...

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection the propagation environment channel. To solve SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading high computational complexity, precluding real-time execution. We propose a novel low-complexity filter, based Poisson...

Kalman adaptive filtering was applied for the first time, to our knowledge, to the real-time simultaneous determination of water isotopic ratios using laser absorption spectroscopy at 2.73 microm. Measurements of the oxygen and hydrogen isotopologue ratios delta(18)O, delta(17)O, and delta(2)H in water showed a 1-sigma precision of 0.72 per thousand for delta(18)O, 0.48 per thousand for delta(1...