In the design of 3-D filter detecting Linear Trajectory Signal (LTS), there may be paid little attention to the noise rejective characteristics. In this paper, we treat the noise rejection ability of the filter detecting LTS having margins both in its velocity and direction.

In this report, we propose a tracking algorithm of speaker direction using microphones located at vertices of an equilateral triangle. The method realizes tracking by minimizing a performance index that consists of the cross spectra at three different microphone pairs in the triangular array. We adopt the steepest descent method to minimize it, and for guaranteeing global convergence to the correct direction with high accuracy, we alter the performance index during the adaptation depending on the convergence state. Through some computer simulation and experiments in a real acoustic environment, we show the effectiveness of the proposed method.

In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.

The blind separation problem of analog modulated radio signals and their DOA (Direction Of Arrival) estimation problem are considered. These problems are very important in radio surveillance. Based on the idea of Carlos and Takada [1], the following two methods are proposed for an Independent Component Analysis (ICA) based radio surveillance system. The first method is concerned with the improvement of DOA estimation accuracy after signal separation by ICA. Another method treats separation and DOA estimation in multipath environment. The effectiveness of the proposed methods is proved by computer simulation.

This paper addresses the problem to extract moving object from the moving background in the mixed domain (MixeD), which makes it possible to carry the filtering in one dimension. Since the velocities of moving object and background are necessary for moving object extraction, we first estimate the velocities based on the appropriate spatial frequency point selection method in the MixeD. Then an optimal filter used for 1-D signal filtering is designed. By filtering 1-D signals over all spatial frequencies, signals with certain velocity vector are extracted, while the extracted image is obtained by applying the 2-D IDFT to the filtered signals. The simulation results show that the method can extract moving object based both on the correctly estimated velocity and the proposed optimal 1-D filter.

The proposed DOA (Direction Of Arrival) estimation method by integrating the frequency array data generated from microphone pairs in an equilateral-triangular microphone array is extended here. The method uses four microphones located at the apices of regular tetrahedron to enable to estimate the elevation angle from the array plane as well. Furthermore, we introduce an idea for separate estimation of azimuth and elevation to reduce the computational loads.

A method of planar curve classification, which is invariant to rotation, scaling and translation using the zerocrossings representation of wavelet transform was introduced. The description of the object is represented by taking a ratio between its two adjacent boundary points so it is invariant to object rotation, translation and size. Transforming this signal to zero-crossings representation using wavelet transform, the minimum distance between the object and model while shifting the signals each other, can be used as classification parameter.

We present an extended quarter-plane lattice model for generating two-dimensional (2-D) autoregressive fields. This work is a generalization of the extended lattice filter of diagonal form (ELDF) developed by Ertuzun et al. The proposed model represents a wider class of 2-D AR fields than conventional lattice models. Several examples are presented to demonstrate the applicability of the proposed model. Furthermore, the proposed structure is compared with other conventional lattice filters based on the computation of their entropy values.

The MUSIC (Multiple Signal Classification) technique with the circular array can estimate both elevational and azimuthal direction-of-arrival (DOA). This conventional method can not distinguish coherent signals, therefore, it can not estimate proper DOA in the presence of coherent signals. On the other hand, limited as to uniformly spaced linear arrays, the spatial smoothing technique is shown to be effective approach in decorrelating coherent signals. This scheme can not be applied directly to the nonlinear arrays. To overcome the coherent signal nonseparation problem in the nonlinear arrays, the approach using a linear interpolation technique has been proposed. However, this approach provides DOA estimates in one dimensional. In our proposed method, we use not only a linear interpolation technique for the circular array but also the symmetry of the circular array. The computer simulation is performed to demonstrate the usefulness of our method. As its result shows, the method can perform well even in the presence of coherent signals.

An aspect of the diverse developments of digital signal processing (DSP) over the last decade are summarized. The current progress of some core fields from the widespread fields are treated in this paper. The selected fields are filter design, wavelet theory and filter bank, adaptive signal processing, nonlinear filters, multidimensional signal processing, intelligent signal processing, and digital signal processor. Through the overview of recent research activities, the interdisciplinary character of the DSP should be proved. Some challenging research direction is described in the last section.

The present paper examines a two-dimensional (2-D) joint-process lattice estimator and its implementation for image restoration. The gradient adaptive lattice (GAL) algorithm is used to update the filter coefficients. The proposed adaptive lattice estimator can represent a wider class of 2-D FIR systems than the conventional 2-D lattice models. Furthermore, its structure possesses orthogonality between the backward prediction errors. These results in superior convergence and tracking properties versus the transversal and other 2-D adaptive lattice estimators. The validity of the proposed model for image restoration is evaluated through computer simulations. In the examples, the implementation of the proposed lattice estimator as 2-D adaptive noise cancellator (ANC) and 2-D adaptive line enhancer (ALE) is considered.

In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

A robust moving object velocity estimation method in the mixed domain (MixeD) is proposed. By obtaining phase, that is, normalizing the 1-D complex sinusoid signals with their magnitudes, the velocity estimations of moving objects with conditions such as object rotation, shape and graylevel variation have been accomplished. Based on the appropriate spatial frequency selection, which choose the points where the signals are less influenced by the background and noise, the spectra of these 1-D temporal complex signals in selected points are estimated by FFT. The simulation results show that velocity vectors have been correctly estimated.

In this paper, we propose a DOA (Direction Of Arrival) estimation method of speech signal using three microphones. The angular resolution of the method is almost uniform with respect to DOA. Our previous DOA estimation method using the frequency-domain array data for a pair of microphones achieves high precision estimation. However, its resolution degrades as the propagating direction being apart from the array broadside. In the method presented here, we utilize three microphones located at vertices of equilateral triangle and integrate the frequency-domain array data for three pairs of microphones. For the estimation scheme, the subspace analysis for the integrated frequency array data is proposed. Through both computer simulations and experiments in a real acoustical environment, we show the efficiency of the proposed method.

This paper proposes a direction-of-arrival (DOA) estimation method of multiple speech sources from a stereophonic mixture in an underdetermined case where the number of sources exceeds the number of sensors. The method relies on the sparseness of speech signals in time-frequency (T-F) domain representation which means multiple independent speakers have a small overlap. At first, a selection of T-F cells bearing reliable spatial information is proposed by an introduced reliability index which is defined by the estimated interaural phase difference at each T-F cell. Then, a statistical error propagation model between the phase difference at T-F cell and its consequent DOA is introduced. By employing this model and the sparseness in T-F domain the DOA estimation problem is altered to obtaining local peaks of probability density function of DOA. Finally the kernel density estimator approach based on the proposed statistical model is applied. The performance of the proposed method is assessed by conducted experiments. Our method outperforms others both in accuracy for real observed data and in robustness for simulation with additional diffused noise.

To estimate moving object velocity in an image sequence is useful for a variety of applications, such as velocity measurement, computer vision and monitoring systems. An effective way is to approach it in the transform/spatiotemporal mixed domain (MixeD), which transforms the 3-D signal processing problem into 1-D complex signal processing. But it remains a problem how to select several spatial frequency points in the MixeD which may influence the accuracy of velocity estimation and object detection. In this paper, a spatial frequency selection method has been proposed, which can choose the appropriate spatial frequency points out of a number of points in MixeD automatically. So the velocity estimation problem can be addressed by solving the coupled equations established over two selected appropriate points in 2-D spatial frequency domain other than searching for the spectral energy plane over a number of points selected by experience. In this method, evaluation functions corresponding to image sequence with one moving object and two moving objects are established firstly, and the selection is then achieved by using the established evaluation functions together with a threshold. The simulation results show that the proposed method is effective on the appropriate spatial frequency selection.

This paper presents two types of two-dimensional (2-D) adaptive beamforming algorithm which have high rate of convergence. One is a linearly constrained minimum variance (LCMV) beamforming algorithm which minimizes the average output power of a beamformer, and the other is a generalized sidelobe canceler (GSC) algorithm which generalizes the notion of a linear constraint by using the multiple linear constraints. In both algorithms, we apply a 2-D lattice filter to an adaptive filtering since the 2-D lattice filter provides excellent properties compared to a transversal filter. In order to evaluate the validity of the algorithm, we perform computer simulations. The experimental results show that the algorithm can reject interference signals while maintaining the direction of desired signal, and can improve convergent performance.

This paper proposes a new angular measurement system to a moving target in the presence of clutter. We apply MUSIC (MUltiple SIgnal Classification) to the outputs of a Doppler filter bank consisting of quadrature mirror filter (QMF). The comparison between QMF and the short time Fourier transform (STFT) as a preprocessor of MUSIC is also discussed. DOA estimation performance by QMF-MUSIC is nearly equal to that of STFT-MUSIC. On the other hand, QMF-MUSIC overcomes STFT-MUSIC in the aspect of computational cost. In a specific example in this paper, the proposal QMF bank by Daubechies (4th order) wavelet requires 80% fewer the number of multiplications and 25% fewer the number of additions than the FFT-based STFT filter bank.

In the adaptive lattice estimation process, it is well known that the convergence speed of the successive stage is affected by the estimation errors of reflection coefficients in its preceding stages. In this paper, we propose block estimation methods of two-dimensional (2-D) adaptive lattice filter. The convergence speed of the proposed algorithm is significantly enhanced by improving the adaptive performance of preceding stages. Furthermore, this process can be simply realized. The modeling of 2-D AR field and texture image are demonstrated through computer simulations.