This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, it examine the hybrid Kalman-based LMS (KNLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter....

In this paper unknown input observer using projection operator method is used to estimate five states of the fifth order lateral axis model of L-1011 system in discrete domain. Discrete reduced order UI observer is used to estimate the states of the system in noiseless and noisy environment. Kalman filter has been designed to estimate the state of L-1011 system when noise is considered. Finally...

In this paper, a mechanism is designed for integration of inertial navigation system information (INS) and global positioning system information (GPS). In this type of system a series of mathematical and filtering algorithms with Tightly Coupled techniques with several objectives such as application of integrated navigation algorithms, precise calculation of flying object position, speed and at...

A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that the measurement error may come from either one of two normal distributions and that transition between these distribution is governed by a Markov Chain. The state estimate is obtained as a weighted average of the estimates from the two parallel filters where the weights are the posterior probabi...

This paper considers state estimation of linear systems using analog amplify and forwarding with multiple sensors, for both multiple access and orthogonal access schemes. Optimal state estimation can be achieved at the fusion center using a time varying Kalman filter. We show that in many situations, the estimation error covariance decays at a rate of 1/M when the number of sensors M is large. ...

This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in a n inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the co...

An adaptive extended Kalman filter is developed and investigated for a transient heat transfer problem in which a high heat flux spot source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. The novel approach is based on the uncertainty in the state model covariance and leverages tre...

We present an approach for augmenting a linear, time-varying Kalman filter with an adaptive neural network (NN) for the state estimation of systems with linear process models acted upon by unknown inputs. The application is to the problem of tracking maneuvering targets. The unknown system inputs represent the effect of unmodeled disturbances acting on the system and are assumed to be continuou...

A critical component of a multi-sensor system is sensor scheduling to optimize system performance under constraints (e.g. power, bandwidth, and computation). In this paper, we apply particle filter sequential Monte Carlo methods to implement multiple sensor scheduling for target tracking. Under the constraint that only one sensor can be used at each time step, we select a sequence of sensor use...