This paper discusses the estimation of a class of discrete-time linear stochastic systems with statistically constrained unknown inputs (UI), which can represent an arbitrary combination of a class of un-modeled dynamics, random UI with unknown covariance matrix and deterministic UI. In filter design, an upper bound filter is explored to compute, recursively and adaptively, the upper-bounds of ...

In this work a constrained adaptive filtering strategy based on conjugate gradient (CG) and set-membership (SM) techniques is presented for adaptive beamforming. A constraint on the magnitude of the array output is imposed to derive an adaptive algorithm that performs data-selective updates when calculating the beamformer’s parameters. We consider a linearly constrained minimum variance (LCMV) ...

This article focuses on designing a consistent and efficient filter for visual-inertial localization given prebuilt map. First, we propose new Lie group with its algebra based which novel invariant extended Kalman (invariant EKF) is designed. We theoretically prove that, when do not consider the uncertainty of map information, proposed EKF able to naturally preserve correct observability proper...

We explore the potential of variance matrices to represent not just statistical error on object pose estimates but also partially constrained degrees of freedom. Using an iterated extended Kalman filter as an estimation tool, we generate, combine and predict partially constrained pose estimates from 3D range data. We find that partial constraints on the translation component of pose which occur...

This paper deals with estimating the frequency of sinusoidal signal buried in broad band noise. The simple unbiased indirect frequency estimation (IFE) algorithm is proposed for a second-order adaptive finite impulse response (FIR) notch filter with constrained zeros. The technique of estimating input noise variance is employed to remove the bias existing in the estimated filter parameter. Also...

A key result of the Capital Asset Pricing Model (CAPM) is that the market portfolio— the portfolio of all assets in which each asset’s weight is proportional to its total market capitalization—lies on the mean-variance-efficient frontier, the set of portfolios having mean-variance characteristics that cannot be improved upon. Therefore, the CAPM cannot be consistent with efficient frontiers for...