A new adaptive Kalman filter-based subspace tracking algorithm and its application to DOA estimation

IEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006This paper presents a new Kalman filter-based subspace tracking algorithm and its application to directions of arrival (DOA) estimation. An autoregressive (AR) process is used to describe the dynamics of the subspace and a new adaptive Kalman filter with variable measurements (KFYM) algorithm is developed to estimate the time-varying subspace recursively from the state-space model and the given observations. For stationary subspace, the proposed algorithm will switch to the conventional PAST to lower the computational complexity. Simulation results show that the adaptive subspace tracking method has a better performance than conventional algorithms in DOA estimation for a wide variety of experimental condition. © 2006 IEEE.published_or_final_versio

A New Variable Regularized QR Decomposition-Based Recursive Least M-Estimate Algorithm-Performance Analysis and Acoustic Applications

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A channel subspace post-filtering approach to adaptive equalization

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2002One of the major problems in wireless communications is compensating for the time-varying intersymbol interference (ISI) due to multipath. Underwater acoustic communications is one such type of wireless communications in which the channel is highly dynamic and the amount of ISI due to multipath is relatively large. In the underwater acoustic channel, associated with each of the deterministic propagation paths are macro-multipath fluctuations which depend on large scale environmental features and geometry, and micro-multipath fluctuations which are dependent on small scale environmental inhomogeneities. For arrivals which are unsaturated or partially saturated, the fluctuations in ISI are dominated by the macro-multipath fluctuations resulting in correlated fluctuations between different taps of the sampled channel impulse response. Traditional recursive least squares (RLS) algorithms used for adapting channel equalizers do not exploit this structure. A channel subspace post-filtering algorithm that treats the least squares channel estimate as a noisy time series and exploits the channel correlation structure to reduce the channel estimation error is presented. The improvement in performance of the algorithm with respect to traditional least squares algorithms is predicted theoretically, and demonstrated using both simulation and experimental data. An adaptive equalizer structure that explicitly uses this improved estimate of the channel impulse response is discussed. The improvement in performance of such an equalizer due to the use of the post-filtered estimate is also predicted theoretically, and demonstrated using both simulation and experimental data.This research was supported by an ONR Graduate Traineeship Award Grant #N00014-00-10049

Move Suppression Calculations for Well-Conditioned MPC

Several popular tuning strategies applicable to Model Predictive Control (MPC) schemes such as GPC and DMC have previously been developed. Many of these tuning strategies require an approximate model of the controlled process to be obtained, typically of the First Order Plus Dead Time type. One popular method uses such a model to analytically calculate an approximate value of the move suppression coefficient to achieve a desired condition number for the regularized system dynamic matrix; however it is not always accurate and tends to under-estimate the required value. In this paper an off-line method is presented to exactly calculate the move suppression coefficient required to achieve a desired condition number directly from the unregularized system dynamic matrix. This method involves an Eigendecomposition of the system dynamic matrix - which may be too unwieldy in some cases –and a simpler analytical expression is also derived. This analytical expression provides a guaranteed tight upper bound on the required move suppression coefficient yielding a tuning formula which is easy to apply, even in on-line situations. Both methods do not require the use of approximate or reduced order process models for their application. Simulation examples and perturbation studies illustrate the effectiveness of the methods in both off-line and on-line MPC configurations. It is shown that accurate conditioning and improved closed loop robustness can be achieved

Adaptive beamforming using frequency invariant uniform concentric circular arrays

This paper proposes new adaptive beamforming algorithms for a class of uniform concentric circular arrays (UCCAs) having near-frequency invariant characteristics. The basic principle of the UCCA frequency invariant beamformer (FIB) is to transform the received signals to the phase mode representation and remove the frequency dependence of individual phase modes through the use of a digital beamforming or compensation network. As a result, the far field pattern of the array is electronic steerable and is approximately invariant over a wider range of frequencies than the uniform circular arrays (UCAs). The beampattern is governed by a small set of variable beamformer weights. Based on the minimum variance distortionless response (MVDR) and generalized sidelobe canceller (GSC) methods, new recursive adaptive beamforming algorithms for UCCA-FIB are proposed. In addition, robust versions of these adaptive beamforming algorithms for mitigating direction-of-arrival (DOA) and sensor position errors are developed. Simulation results show that the proposed adaptive UCCA-FIBs converge much faster and reach a considerable lower steady-state error than conventional broadband UCCA beamformers without using the compensation network. Since fewer variable multipliers are required in the proposed algorithms, it also leads to lower arithmetic complexity and faster tracking performance than conventional methods. © 2007 IEEE.published_or_final_versio