Phase-based methods for estimating the frequency of a sinusoid have typically suffered from a threshold effect, where for signal to noise ratio (SNR) below the threshold, the mean squared error of the estimate rapidly increases. Furthermore, it is a significant problem that the threshold is considerably high and strongly depends on frequency. To overcome the difficulties, a Kalman-based sinusoidal estimator bank (KSEB) is proposed. In the derivation of the KSEB, a four-channel filter bank and decimation technique are effectively used. The computer simulation also demonstrates the superiority of the KSEB to the other frequency estimators.

An extension of the traditional color-based visual tracker, i.e., the continuously adaptive mean shift tracker, is given for improving the convenience and generality of the color-based tracker. This is achieved by introducing a probability density function for pixels based on the hue histogram of object. As its merits, the direction and size of the tracked object are easily derived by the principle component analysis (PCA), and its extension to three-dimensional case becomes straightforward.

A new compact form of the sliding window recursive least squares (SWRLS) algorithm, the I-SWRLS algorithm, is derived using an indefinite matrix. The resultant algorithm has a form similar to that of the traditional recursive least squares (RLS) algorithm, and is more computationally efficient than the conventional SWRLS algorithm including two Riccati equations. Furthermore, a computationally reduced version of the I-SWRLS algorithm is developed utilizing a shift property of the correlation matrix of input data. The resulting fast algorithm reduces the computational complexity from O(N2) to O(N) per iteration when the filter length (tap number) is N, but retains the same tracking performance as the original algorithm. This fast algorithm is much easier to implement than the existing SWC FTF algorithms.

The hyper H∞ filter derived in our previous work provides excellent convergence, tracking, and robust performances for linear time-varying system identification. Additionally, a fast algorithm of the hyper H∞ filter, called the fast H∞ filter, is successfully developed so that identification of linear system with impulse response of length N is performed at a computational complexity of O(N). The gain matrix of the fast filter is recursively calculated through estimating the forward and backward linear prediction coefficients of an input signal. This suggests that the fast H∞ filter may be applicable to linear prediction of the signal. On the other hand, an alternative fast version of the hyper H∞ filter, called the J-fast H∞ filter, is derived using a J-unitary array form, which is amenable to parallel processing. However, the J-fast H∞ filter explicitly includes no linear prediction of input signals in the algorithm. This work reveals that the forward and backward linear prediction coefficients and error powers of the input signal are indeed included in the recursive variables of the J-fast H∞ filter. These findings are verified by computer simulations.

The fast H∞ filter is developed by one of the authors, and its practical use in industries is expected. This paper derives a linear propagation model of numerical errors in the recursive variables of the fast H∞ filter, and then theoretically analyzes the stability of the filter. Based on the analyzed results, a numerical stabilization method of the fast H∞ filter is proposed with the error feedback control in the backward prediction. Also, the effectiveness of the stabilization method is verified using numerical examples.

A new method is proposed for estimating a single complex sinusoid and its parameters (frequency and amplitude) from measurements corrupted by white noise. This method is called the ECKF-SVD method, which is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables consist of the AR coefficient (a function of frequency) and a sample of the original signal. Proof of the stability is given in the case of a single sinusoid. Simulations demonstrate that the proposed ECKF-SVD method is effective for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the amplitude estimation by means of the ECKF-SVD method is also discussed.

A nonlinear harmonic estimator (NHE) is proposed for extracting a harmonic signal and its fundamental frequency in the presence of white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a multiple sinusoidal model with state-representation and then efficiently specializing it for the case of harmonic estimation. The effectiveness of the NHE is verified using computer simulations.

The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

In our previous work, the hyper H∞ filter is developed for tracking of unknown time-varying systems. Additionally, a fast algorithm, called the fast H∞ filter, of the hyper H∞ filter is derived on condition that the observation matrix has a shifting property. This algorithm has a computational complexity of O(N) where N is the dimension of the state vector. However, there still remains a possibility of deriving alternative forms of the hyper H∞ filter. In this work, a fast J-unitary form of the hyper H∞ filter is derived, providing a new H∞ fast algorithm, called the J-fast H∞ filter. The J-fast H∞ filter possesses a computational complexity of O(N), and the resulting algorithm is very amenable to parallel processing. The validity and performance of the derived algorithm are confirmed by computer simulations.

A nonlinear multiple complex sinusoidal estimator (NMSE) is proposed, as an extended and improved version with system noise of the single sinusoidal estimator previously presented by the author, for extracting multiple complex sinusoids in white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a noisy multiple complex sinusoidal model with state-representation, where the model becomes a nonlinear stochastic system. Proof of the stability is given by using a structure of the state-space signal model and Lyapunov techniques. Also, computer simulations demonstrate the effectiveness of the NMSE from various points of view.

A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.