In many efforts to increase the efficiency of power amplifiers of mobile terminals, compensation of nonlinear distortion based on an adaptive predistortion method has performed an important role. In the course of basic evaluation of a method using a look-up table (LUT) and a method using an approximation for compensation of nonlinear distortion, a newly developed method using approximation and a ROM type LUT with a small-sized memory has been proposed to overcome barriers to practical application and disadvantages associated with the LUT method. Experimental trials of the proposed method were applied to narrow-band digital modulation systems. As a result, the proposed method was found to provide a satisfactory capability of compensating nonlinear distortion, with next adjacent channel interference of less than -55 dBc. The proposed method has advantages such as a small memory size and excellent RF performance, and is expected to occupy an important position in many adaptive predistortion methods.

Event-related are the kind of signals that are time-related to a given event. In this work, we will study the effect of bias and overlapping noise on Fourier linear combiner (FLC)-based filters, and its implication on filtering event-related signals. We found that the bias alters the weights behaviour, and therefore the filter output, and we discuss solutions to the problem of spectral overlap.

A noble blind equalization algorithm (BEA) using a single/multilevel modulus is proposed. According to the residual intersymbol interference (ISI) level of the equalizer output, the new algorithm adopts relevantly a single modulus or a multilevel modulus to form its cost function. Moreover, since the proposed approach separates complex two-dimensional signal into in-phase and quadrature components, and forms the error signals for each component, it has inherently the capability of phase recovery. Hence, it improves the performances of steady-state and recovers the phase rotation without any degradation of transient property. Simulation results confirm the effectiveness of the new approach.

An optical array imaging system has been presented in previous articles. In this system, first, the object is illuminated with laser light sequentially from each of the array elements and the reflected field is detected as interferogram. The interferograms obtained are then spatially heterodyne-detected on a computer to extract the signal components, that is, array data. Then, the eigenvector of the largest eigenvalue is derived by applying the power method to the array data and it is beam-steered to get images of the object. The algorithm gives good images for most objects, but it fails to work for some objects. It was shown that using a subset of the array data may solve the problem, but that finding the corresponding optimum subaperture is quite time-consuming. In this paper, first, the integral equation describing the system is solved for a general class of object, to make clear the conditions for the eigenvector to form a sharp beam. Second, the imaging algorithm is sped up to a great degree by optimizing only the illuminating aperture in a coarse fashion. Third, the rate of convergence of the power method is adaptively estimated in the algorithm to make the eigenvector derivation reliable. Finally the improved algorithm is investigated using both computer-generated and experimentally obtained array data.

This paper proposes an adaptive coding rate and process gain control technique with channel activation function to realize a CDMA based radio subsystem for multi-media communication services that include two types of media, i.e., fixed size data such as the computer data and still image, and constant bit rate data such as voice and video. The proposed system achieves high throughput data transmission for the fixed size data by controlling the process gain and coding rate according to the variation of the channel. Moreover, to adopt the constant bit rate data, the proposed system also employs a channel activation technique. Computer simulation confirms that the proposed system is very effective for multi-media communication services.

The numerical properties of the recursive least squares (RLS) algorithm and its fast versions have been extensively studied. However, very few investigations are reported concerning the numerical behavior of the predictor based least squares (PLS) algorithms that provide the same least squares solutions as the RLS algorithm. This paper presents a comparative study on the numerical performances of the RLS and the backward PLS (BPLS) algorithms. Theoretical analysis of three main instability sources reported in the literature, including the overrange of the conversion factor, the loss of symmetry and the loss of positive definiteness of the inverse correlation matrix, has been done under a finite-precision arithmetic. Simulation results have confirmed the validity of our analysis. The results show that three main instability sources encountered in the RLS algorithm do not exist in the BPLS algorithm. Consequently, the BPLS algorithm provides a much more stable and robust numerical performance compared with the RLS algorithm.

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.

In this paper, a block implementation of high-speed IIR adaptive noise canceller is proposed. First, the block difference equation of an IIR filter is derived by the difference equation for high-speed signal processing. It is shown that the computational complexity for updating the coefficients of IIR adaptive filter can be reduced by using the relations between the elements of coefficient matrices of block difference equation. Secondly, the block implementation of IIR adaptive noise canceller is proposed in which the convergence rate is increased by successively adjusting filter Q-factors. Finally, the usefulness of proposed block implementation is verified by the computer simulations.

A new structure for adaptive AR spectral estimation based on multi-band decomposition of the linear prediction error is introduced and the mathematical background for the soulution of the related adaptive filtering problem is derived. The presented structure gives rise to AR spectral estimates that represent the true underlying spectrum with better fidelity than conventional LS methods by allowing an arbitrary trade-off between variance of spectral estimates and tracking ability of the estimator along the frequency spectrum. The linear prediction error is decomposed through a filter bank and components of each band are analyzed by different window lengths, allowing long windows to track slowly varying signals and short windows to observe fastly varying components. The correlation matrix of the input signal is shown to satisfy both time-update and order-update properties for rectangular windowing functions, and an RLS algorithm based on each property is presented. Adaptive forward and backward relations are used to derive a mathematical framework that serves as a basis for the design of fast RLS alogorithms. Also, computer experiments comparing the performance of conventional and the proposed multi-band methods are depicted and discussed.

The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.

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.

A radio propagation experiment at the Okinawa Radio Observatory of the Communications Research Laboratory is investigating the feasibility of calibrating the spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission by using the path-averaged rainfall rate estimated from rain attenuation. Because this estimated rainfall rate has errors due to the spatial inhomogeneity of rainfall rate and the variability of raindrop size distribution, we used distrometer data to evaluate both of these errors by computer simulation.

The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.

An important aspect of traffic safety is the development of aids that extend the driver's time and motion perception. One promising candidate is the compact, lightweight millimeter-wave FM-CW radar now being widely studied. Although the homodyne FM-CW radar is well known form its simplicity, it has a relatively low S/N ratio. This paper describes the principles behind our newly-developed heterodyne FM-CW radar and it's evaluation results. The heterodyne FM-CE radar generates sidebands by switching a front-end amplifier and also uses the heterodyne detection technique for gaining sensor sensitivity. The heterodyne FM-CW radar's signal to noise ratio was 19.5 dB better than previously designed homodyne FM-CW radar.

The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.

This paper describes how to effectively use discriminative training based on Minimum Classification Error (MCE) criterion for a small amount of data in order to attain the highest level of recognition performance. This method is a combination of MCE training and Vector-Field-Smoothed Bayesian learning called MAP/VFS, which combines maximum a posteriori (MAP) estimation with Vector Field Smoothing (VFS). In the proposed method, MAP/VFS can significantly enhance MCE training in the robustness of acoustic modeling. In model training, MCE training is performed using the MAP/VFS-trained model as an initial model. The same data are used in both trainings. For speaker adaptation using several dozen training words, the proposed method has been experimentally proven to be very effective. For 50-word training data, recognition errors are drastically reduced by 47% compared with 16.5% when using only MCE. This high rate, in which 39% is due to MAP, an additional 4% is due to VFS, and a further improvement of 4% is due to MCE, can be attained by enhancing MCE training capability by MAP/VFS.

In this paper stochastic aradient adaptive filters using the Sign or Sign-Sign Algorithm are analyzed based upon general assumptions on the reference signal, additive noise and particularly jointly distributed tap errors. A set of difference equations for calculating the convergence process of the mean and covariance of the tap errors is derived with integrals involving characteristic function and its derivative of the tap error distribution. Examples of echo canceller convergence with jointly Gaussian distributed tap errors show an excellent agreement between the empirical results and the theory.

This paper presents a method for simply estimating characteristics of signals received by a millimeter wave car radar. In this method, the substitution of a radar target with a set of scattering points is introduced to take account of the phenomenon that only a part of the target is irradiated with the radio wave from the radar antenna with a sharp beam; the phenomenon is peculiar to the car radar which operates in a compact range. The positions of these scattering points and the RCS values for the scattering points are appropriately determined on the basis of a measured RCS image for the target. The RCS image means a spatial distribution of RCS values on the surface of the target. In addition, influence of the ground, which is a dominant clutter in car radar environments, and characteristics of the car radar hardware can be included in the estimation method. The estimated characteristics of the signal received by the car radar are compared with the measured ones under typical cases in the car radar environments. The comparison verifies not only that the received signal characteristics are well estimated even when the range is rather short but also that the substitution of the target with scattering points is valid. The proposed method can realize the estimation of the received signal characteristics. Furthermore, the method can be developed into a computer simulation for evaluating the target detection performance of the car radar.

We propose a method for accomplishing accurate RCS (Radar Cross Section ) images of a car in a compact range. It is an improved method based on an ISAR (Inverse Synthetic Aperture Radar) technique. To obtain accurate RCS values, an idea of an image correction function for the Fourier transform used in the ISAR processing is introduced. The role of the image correction function is to compensate the difference of the propagation loss as to the different scattering points on a target. As a result, `sensitivity' of imaging in the compact range is kept uniform. Hamming window is suitable for the Fourier transform to accomplish RCS images because of its low sidelobe level and the sharpness of a mainlobe. When hamming window is adopted, the spatial resolution is approximately twice the size of granularity which is determined by the ISAR parameters. To verify the improvement of the RCS images obtained by means of our method, several numerical target models are employed. The results of the investigation show that uniformity of `sensitivity' for obtained RCS images is achieved in the compact range and accurate images with the resolution of twice the size of granularity are accomplished without blurs or distortions in the unambiguous area. RCS images for rear aspects of a passenger car are investigated with the spatial resolution of 50 mm in the 60 GHz band. The RCS image varies with the aspect angle of the car and the specular reflection occurs for the millimeter wave. When the curvature on the car edge is small, a blurred RCS image is observed. The reason is that a scattering center of the specular reflection moves so widely that it can't be regarded as a fixed point. This causes elongation of the RCS image. A peak value in the dominant area for each aspect angle is less the 0 dBsm and no remarkable areas where the RCS value exceeds-20 dBsm is found any more on the car except such the dominant area.

This paper describes an adaptive neural vector quantization algorithm with a deleting approach of weight (reference) vectors. We call the algorithm an adaptive learning and self-deleting algorithm. At the beginning, we introduce an improved topological neighborhood and an adaptive vector quantization algorithm with little depending on initial values of weight vectors. Then we present the adaptive learning and self-deleting algorithm. The algorithm is represented as the following descriptions: At first, many weight vectors are prepared, and the algorithm is processed with Kohonen's self-organizing feature map. Next, weight vectors are deleted sequentially to the fixed number of them, and the algorithm processed with competitive learning. At the end, we discuss algorithms with neighborhood relations compared with the proposed one. The proposed algorithm is also good in the case of a poor initialization of weight vectors. Experimental results are given to show the effectiveness of the proposed algorithm.