A New Adaptive Kalman Filter-Based Subspace Tracking Algorithm and Its Application to DOA Estimation

Abstract

This paper presents a new Kalman filter-based noise, while avoiding excessive bias for non-stationary signals. One subspace tracking algorithm and its application to directions of can determine the number of measurements using the intersection of arrival (DOA) estimation. An autoregressive (AR) process is confidence intervals (ICI) bandwidth selection [4]. Alternately, the used to describe the dynamics of the subspace and a new proposed Kalman filter aims to determine the appropriate number of adaptive Kalman filter with variable measurements (KFVM) the measurements adaptively according to the approximated algorithm is developed to estimate the time-varying subspace derivatives of the system state in order to reduce the complexity in recursively from the state-space model and the given the bandwidth selection algorithm. Basically, when the signal observations. For stationary subspace, the proposed algorithm subspace varies rapidly, few measurements will be used to update will switch to the conventional PAST to lower the the state estimate. On the other hand, when the subspace is slowcomputational complexity. Simulation results show that the varying or even static, increasingly more past measurements should adaptive subspace tracking method has a better performance be employed. However, a heavy computational complexity will be incurred for large numbers of measurements in the KFVM. than conventionaenal gorithms in DOA cstimation for a wid Therefore, the KFVM can be switched to the PAST (RLS) varicty of experimental condition. algorithm to lessen the arithmetic complexity. This gives a new adaptive subspace tracking algorithm, which composes of KFVM and RLS. We will use the directions of arrival (DOA) estimation to I.

A robust correntropy based subspace tracking algorithm in impulsive noise environments

“…In [13] and [5], Chan et al proposed the robust KFVNM algorithm to deal with the tracking problem for fast varying subspaces in impulsive noise environments. Addressing the problem of the solution for ( ) t W with the following state-space model…”

DOA Estimation and Tracking of ULAs with Mutual Coupling

“…Choosing L adaptively in KFVM has the advantage of achieving a better bias-variance tradeoff at each time instant. The scheme previously proposed in [35], [36] can be utilized here to select L at each time t. First, we definê e(t) =ẑ(t ¡ 1) ¡z(t ¡ 1) (49)…”

“…In the MPAST and MOPAST algorithms, we find that a better tracking performance can be obtained by repeating the respective PAST and OPAST iteration one or more times, since the "projection approximation" will be further improved with the subspace estimates. In the adaptive Kalman filter-based algorithm, the signal subspace is regarded as the system state and an adaptive Kalman filter with variable number of measurements (KFVM) [35,36] is employed for tracking the fast-varying subspace. Hence, the fast-varying DOAs can be estimated using the framework previously developed in the paper.…”

Inverse QR iterative method for subspace tracking

“…The quasicode of the IQR iterative algorithm for subspace tracking is summarized in Tab.1. Now note that the proposed subspace tracking algorithm updates 1 …”

“…Subspace has played an important role in a wide range of signal processing applications, such as DOA estimation [1] , target recognition [2] , channel estimation [3] , and so on. The computation of the subspace is a primary task in practical applications.…”