In order to solve the problem that measured values of key state parameters such as lateral velocity and yaw rate vehicle are easily interfered by random errors, a filter estimation method is proposed based on principle robust filtering unscented particle algorithm. Based establishment 3-DOF non-linear dynamic model Dugoff tire vehicle, adaptive filter(ARUPF) used estimate state, realize longitu...

37 Ensemble-based parameter estimation for a climate model is emerging as an 38 important topic in climate research. For a complex system as a coupled ocean-atmosphere 39 general circulation model, the sensitivity and response of a model variable to a model 40 parameter could vary spatially and temporally. Here, we propose an adaptive spatial 41 average (ASA) algorithm to increase the efficienc...

in this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. the estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. most researchers implemented nonlinear methods to extract the harmonic parameters. however, nonlinear methods for amplitude estimation increase time of convergence. hen...

Accurate traffic characterization by packet source is needed to predict network behavior and to properly allocate network resources to achieve a desired Quality of Service for all network users. As networks have become faster, the processing load required for complete packet sampling has also grown. In some cases, for example Gigabit Ethernet, the network can deliver packets faster than a netwo...

We develop generalized bounds for quantum single-parameter estimation problems for which the coupling to the parameter is described by intrinsic multisystem interactions. For a Hamiltonian with k-system parameter-sensitive terms, the quantum limit scales as 1/Nk, where N is the number of systems. These quantum limits remain valid when the Hamiltonian is augmented by any parameter-independent in...

In this paper, a parameter and uncertainty bound estimation functions for adaptive-robust control of robot manipulators are developed. A Lyapunov function is defined and parameters and uncertainty bound estimation functions are developed based on the Lyapunov function. Thus, stability of an uncertain system is guaranteed and uniform boundedness of the tracking error is achieved. As distinct fro...

Quantum parameter estimation has many applications, from gravitational wave detection to quantum key distribution. The most commonly used technique for this type of estimation is quantum filtering, using only past observations. We present the first experimental demonstration of quantum smoothing, a time-symmetric technique that uses past and future observations, for quantum parameter estimation...

We construct honest confidence regions for a Hilbert space-valued parameter in various statistical models. The confidence sets can be centered at arbitrary adaptive estimators, and have diameter which adapts optimally to a given selection of models. The latter adaptation is necessarily limited in scope. We review the notion of adaptive confidence regions, and relate the optimal rates of the dia...

This paper presents adaptive bidirectional minimum mean-square error (MMSE) parameter estimation algorithms for fast-fading channels. The time correlation between successive channel gains is exploited to improve the estimation and tracking capabilities of adaptive algorithms and provide robustness against time-varying channels. Bidirectional normalized least meansquare (NLMS) and conjugate grad...

The paper presents a unified approach to local likelihood estimation for a broad class of nonparametric models, including e.g. the regression, density, Poisson and binary response model. The method extends the adaptive weights smoothing (AWS) procedure introduced in Polzehl and Spokoiny (2000) in context of image denois-ing. Performance of the proposed procedure is illustrated by a number of nu...