Michal Stanislaw Meller

A New Approach to Active Noise and Vibration Control—Part II: The Unknown Frequency Case

This paper presents a new approach to rejection of complex-valued sinusoidal disturbances acting at the output of a discrete-time stable linear plant with unknown dynamics. It is assumed that the frequency of the sinusoidal disturbance is known, and that the output signal is contaminated with wideband measurement noise. The disturbance rejection control rule is first derived and analyzed for a nominal plant model, different from the true model. Then a special adaptation mechanism is added, which is capable of compensating modeling biases (errors in both magnitude and phase) so that, under Gaussian assumptions, the closed-loop system can converge in mean to the optimal solution.

Processing of Noise Radar Waveforms using Block Least Mean Squares Algorithm

The problem of signal reception in a noise radar is considered. It is assumed that heavy clutter reflections are present. In such conditions, correlation processing suffers from the problem of noise floor in the ambiguity function of random signals. It is proposed to replace correlation processing with least squares (LS) based methods. In particular, a highly efficient block least mean squares (block LMS) algorithm is studied in detail. It is demonstrated that, compared with the correlation processing, block LMS shows significant advantages in terms of robustness to clutter. Simulation experiments confirm that when heavy clutter is present, the proposed method outperforms correlation processing significantly.

New Algorithms for Adaptive Notch Smoothing

The problem of extraction/elimination of a nonstationary complex sinusoidal signal buried in noise is considered. This problem is usually solved using adaptive notch filtering (ANF) algorithms. It is shown that accuracy of signal estimation can be increased if the results obtained from ANF are further processed using a cascade of appropriately designed filters. The resulting adaptive notch smoothing (ANS) algorithms can be employed in many offline or nearly real-time online applications. Whenever signal frequency varies in a sufficiently smooth manner, the proposed fixed-interval and fixed-lag ANS algorithms, based on a new, quasi-linear model of frequency changes, outperform the existing solutions.

Fast clutter cancellation for noise radars via waveform design

Canceling clutter is an important, but very expensive part of signal processing in noise radars. It is shown that considerable improvements can be made to a simple least squares (LS) canceler if minor constraints are imposed onto noise waveform. Using a combination of field programmable gate array (FPGA) and CPU, the proposed scheme is capable of canceling both stationary clutter and moving targets in real time, even for high sampling rates.

Parallel frequency tracking with built-in performance evaluation

The problem of estimation of instantaneous frequency of a nonstationary complex sinusoid (cisoid) buried in wideband noise is considered. The proposed approach employs a bank of adaptive notch filters, extended with a nontrivial performance assessment mechanism which automatically chooses the best performing filter in the bank. Additionally, a computationally attractive method of implementing the bank is proposed. The new structure allows one to improve tracking results considerably, especially in nonstationary conditions. In terms of accuracy of frequency estimates, the proposed scheme outperforms existing ones considerably.

Tracking analysis of an adaptive vibration controller

The problem of rejection of a sinusoidal disturbance of known frequency, acting at the output of a discrete-time complex-valued linear stable plant with unknown dynamics, is considered. It is assumed that output signal is contaminated with wideband measurement noise. The paper presents convergence and tracking analysis of a new narrow-band disturbance elimination scheme described recently. It is shown that the proposed adaptive feedback regulator converges locally in the mean to the optimal (minimum-variance) regulator. It is also shown that it has very good robustness properties.

Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals

The problem of both causal and noncausal identification of linear stochastic systems with quasi-harmonically varying parameters is considered. The quasi-harmonic description allows one to model nonsinusoidal quasi-periodic parameter changes. The proposed identification algorithms are called generalized adaptive comb filters/smoothers because in the special signal case they reduce down to adaptive comb algorithms used to enhance or suppress nonstationary harmonic signals embedded in noise. The paper presents a thorough statistical analysis of generalized adaptive comb algorithms, and demonstrates their statistical efficiency in the case where the fundamental frequency of parameter changes varies slowly with time according to the integrated random-walk model.

New approach to adaptive vibration control

The paper presents a new approach to rejection of sinusoidal disturbances acting at the output of a discrete-time linear stable plant with unknown dynamics. It is assumed that frequency of the sinusoidal disturbance is known, and that the output signal is contaminated with wideband measurement noise. It is not assumed that a reference signal, correlated with the disturbance, is available. The proposed solution combines the coefficient fixing technique, used to ¿robustify¿ self-tuning minimum-variance regulators, with automatic adaptation gain tuning. Simulation experiments confirm that, under Gaussian assumptions, the closed-loop system converges in the mean to the optimal one.

Multichannel self-optimizing narrowband interference canceller

The problem of cancellation of a nonstationary sinusoidal interference, acting at the output of an unknown multivariable linear stable plant, is considered. No reference signal is assumed to be available. The proposed feedback controller is a nontrivial extension of the SONIC (self-optimizing narrowband interference canceller) algorithm, developed earlier for single-input, single-output plants. The algorithm consists of two loops: the inner, control loop, which predicts and cancels disturbance, and the outer, self-optimization loop, which automatically adjusts the gain matrix so as to optimize the overall system performance. The proposed scheme is capable of adapting to slow changes in disturbance characteristics, measurement noise characteristics, and plant characteristics. It is shown that in the important benchmark case - for disturbances with random-walk-type amplitude changes - the designed closed-loop control system converges locally in mean to the optimal one. The algorithm, derived and analyzed assuming a single-tone, complex-valued disturbance with known frequency, can be extended to cope with a range of realistic applications, such as real-valued disturbances, multitone signals, and unknown frequency.

Estimation of nonstationary harmonic signals and its application to active control of MRI noise

A new adaptive comb filtering algorithm, capable of tracking the fundamental frequency and amplitudes of different frequency components of a nonstationary harmonic signal embedded in white measurement noise, is proposed. Frequency tracking characteristics of the new scheme are studied analytically, proving (under Gaussian assumptions and optimal tuning) its statistical efficiency for quasi-linear frequency changes. Laboratory tests show that the proposed algorithm can be successfully used for active control of MRI noise.