Demetrios G. Lainiotis

AR model identification with unknown process order

A method for simultaneous autoregressive (AR) model order selection and identification is proposed, which is based on the adaptive Lainiotis filter (ALF). The method is not restricted to the Gaussian case, is applicable to online/adaptive operation, and is computationally efficient. It can be realized in a parallel processing fashion. The AR model order selection and identification problem is reformulated so that it can be fitted into the framework of a state space under uncertainty estimation problem framework. The ALF is briefly presented and its application to the specific problem is discussed. Simulation examples are presented to demonstrate the superior performance of the method in comparison with previously reported ones. >

A partitioned adaptive approach to nonlinear channel equalization

The problem of identifying digital transmitted symbols over nonlinear communication channels is addressed. The equalization scenario is considered from the decision point of view, and constitutes a joint identification and estimation situation due to incomplete knowledge of the system model. A new class of multilinearization algorithms for nonlinear systems is derived according to partitioning theory concepts. The procedure targets on adaptively selecting the best reference points for linearization from an ensemble of generated trajectories that span the whole state space of the desired signal. In the various simulations examined, the partitioned-based equalizer is found superior to the classical extended Kalman filter.

A survey of recursive algorithms for the solution of the discrete time Riccati Equation

Abstract The Riccati Equation plays a fundamental role in many fields of mathematics, science and engineering. Its solution constitutes an integral prerequisite to the solution of important problems in the above fields. Due to the importance of the Riccati Equation, there exists considerable literature on its algebraic as well as algorithmic solution. A very large number of those studies are devoted to the continuous time Riccati Equation. In this paper we present a survey of classical as well as more recent recursive algorithms that solve the discrete time Riccati Equatbn emanating from the Kaiman Filter as well as from the Lainiotis Filter equations either using per step calculations or the doubling principle. It is established that these algorithms converge fast and are numerically stable.

On the parallel implementations of the linear Kalman and Lainiotis filters and their efficiency

Abstract In this paper, the parallel implementations of two well-known linear state-space filtering algorithms, namely the Kalman and the Lainiotis filters, in MIMD machines are studied from a computational standpoint. The analysis assumes both time invariant and time varying system models and uses precedence graphs and critical paths. The parallelism efficiency of the implementations is also defined and studied. Results indicate that these algorithms can be implemented in parallel using a comparatively small number of processors. Furthermore, the efficiency of the parallel implementations can be very high or very low, depending on the state and measurement vector dimensions.

A new class of efficient adaptive nonlinear filters (ANLF)

A multilinearization procedure is described, with the use of which a new class of algorithms for nonlinear filtering can be realized. The methodology targets on adaptively selecting the best reference points for linearization from an ensemble of generated trajectories that span the whole state space of the desired signal. Through simulations, the approach is shown to be significantly superior to the classical extended Kalman filter and comparable in computational burden.

Passive tracking of a maneuvering target: an adaptive approach

The problem of target tracking using passive sensors is examined. The target is assumed to execute maneuvers at times unknown to the observer. An adaptive algorithm, which takes into account such maneuvers, is derived using the multimodel partitioning approach. Simulation results show the superiority of the proposed algorithm over a tracker employing the extended Kalman filter. >

Real Time Ship Motion Estimation

The problem of real time estimation of ship mo- tions is consided. The problem is viewed as a nonlinear adaptive estimation problem for partially unknown time- varying linear systems with non-Gaussian initial conditions. The adaptive Lainiotis partitiong approach is applied and com- parisons are made with the Kalman filter. Extensive simula- tion experiments show that the use of the Lainiotis adaptive approach is highly beneficial, with regards to performance, over the Kalman filter.

Optimal seismic deconvolution: distributed algorithms

Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelets replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed solution. The new distributed methodology, perfectly suitable for a multisensor environment, such as the seismic signal processing, is compared to the centralized approach, with respect to computational complexity and architectural efficiency. It is shown that the distributed approach greatly outperforms the currently used centralized methodology offering flexibility in the design of the data fusion network.

Underwater tracking of a maneuvering target using time delay measurements

Abstract The problem of estimating the relative position of an underwater maneuvering target is treated as an estimation problem when an unknown and time varying bias is present in the plant noise process. Pilot-initiated maneuvers are modeled as impulsive unknown inputs affecting the bias term at times unknown to the observer. A new algorithm, capable of efficiently handling the problem of state estimation with time varying unknown bias, is derived by using the Lainiotis multimodel partitioning theory coupled with conventional constant bias estimation algorithms. Simulation results show that the proposed algorithm performs very well under adverse operating conditions, such as high measurement noise, long target to observer range and large-scale target maneuvers.

Adaptive filter applications to LIDAR: return power and log power estimation

The problem of estimating the return power in a LIDAR system in the presence of multiplicative noise (speckle) is addressed. A significant class of the partitioning approach is applied and comparisons are made with the extended Kalman filter (EKF) in the case where model parameter uncertainty exists. Through extensive simulations, the partitioned filter is shown to be significantly superior to the EKF algorithm.