A new steepest descent linear adaptive algorithm, called the proportion-sign algorithm (PSA), is introduced and its performance analysis is presented when the signals are from zero-mean jointly stationary Gaussian processes. The PSA improves the convergence speed over the least mean square (LMS) algorithm without overly degrading the steady-state error performance and has the robustness to impulsive interference occurring in the desired response by adding a minimal amount of computational complexity. Computer simulations are presented that show these advantages of the PSA over the LMS algorithm and demonstrate a close match between theoretical and empirical results to verify our analysis.

The techniques for imaging optically opaque region using an electromagnetic wave radar are being developed. One important application of these techniques is the detection of buried pipes and cables. The image quality of subsurface radar often becomes low because the electromagnetic waves are affected by the attenuation and inhomogeneity of soil. Hence, a method which improves the quality of the radar images has been required. The migration method is utilized in reflective seismic processing and is derived based on the solution of the wave equation represented in spatial frequency domain. It is classified into the F-K and the phase-shift (P-S) migration method. The former is derived on the assumption that propagation velocity of the wave is uniform in the soil while the latter is assumed that the propagation velocity is varying depending on the depth from the ground surface. The P-S method gives relatively good quality images but it requires very long computation time. In this paper, we propose the block migration method in which the F-K method is applied to the divided image blocks with local propagation velocity. In order to solve a problem concerning the connection between the contiguous blocks we present two approaches which are the processings using the overlapped regions and the Lapped Orthogonal Transform (LOT). Some experimental results point out that the block migration method has a good capability of improving the image quality and the processing time using LOT becomes one tenth in comparison with the P-S method.

In this paper we propose a method for increasing the reliability in multiprocessor realization of lowpass and highpass FIR digital filters possessing a maximally flat magnitude response. This method is based on the use of array realization of the filter which has been proposed earlier by the authors. It is shown that if a processing module of the array functions erroneously, it is possible to exclude the module and still obtain a lowpass FIR filter. However, as a price we should tolerate a slight degradation in the magnitude response of the filter that is equivalent to a wider transition band. We also analyze the behavior of the filter when our proposed schemes are implemented on more than one module. The justification of our approach is based on that a slight degradation of the spectral characteristics of a filter may be well tolerated in most filtering applications and thus a graceful degradation in the frequency domain can sufficiently reduce the vulnerability to errors.

An adaptive LMS filtering system is proposed for computing the Discrete Walsh Transform (DWT). The signal to be transformed serves as the 'desired signal' for the adaptive filter, while a set of periodic Walsh sequences serve as the input signal vector for the adaptive filter. The weights of the adaptive filter provide the DWT. The given approach is more efficient in terms of the required computations and memory locations compared with the direct approach. In contract with existing Fast DWT algorithm, the proposed solution provides more flexibility as far as the signal block length is concerned. In other words, the proposed approach is not restricted to a block length N to be of power 2.

A fast Newton/LMS algorithm is proposed which uses an efficient inversion technique of input autocorrelation matrix when the periodic pseudo random sequence is used as the reference signal. The number of operations is greatly reduced and the computational results show fast convergence rate and low misadjustment error. And the application of the algorithm to the case of nonperiodic reference signal is described.

This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.

We developed an automatic data processing algorithm for a ground-probing radar which is essential in analyzing a large amount of data by a non-expert. Its aim is to obtain an optimum result that the conventional technique can give, without the assistance of an experienced operator. The algorithm is general except that it postulates the existence of at least one isolated target in the radar image. The raw images of underground objects are compressed in the vertical and the horizontal directions by using a pulse-compression filter and the aperture synthesis technique, respectively. The test function needed to configure the compression filter is automatically selected from the given image. The sensitivity of the compression filter is adjusted to minimize the magnitude of spurious responses. The propagation velocity needed to perform the aperture synthesis is determined by fitting a hyperbola to the selected echo trace. We verified the algorithm by applying it to the data obtained at two test sites with different magnitude of clutter echoes.

An approach to estimate the information of moving objects is described in terms of their kinetic and static properties such as 2D velocity, acceleration, position, and the size of each object for the features of motion snd shape. To obtain the information of motion/shape of multiple objects, an advanced contour matching scheme is developed, which includes the synthesis of edge images and the analysis of object shape with a high matching confidence as well as a low computation cost. The scheme is composed of three algorithms: a motion estimation by an iterative triple cross-correlation, an image synthesis by shifting and masking the object, and a shape analysis for determining the object size. Implementing fuzzy membership functions to the object shape, the scheme gets improved in accuracy of capturing motion and shape of multiple moving objects. Experimental result shows that the proposed method is valid for several walking men in real scene.

In this paper, a new structure which is useful for the detection of multiple sinusoids is presented. The proposed structure is based on the direct form second-order IIR notch filter using simplified adaptive algorithm. It has been shown that the convergence characteristics of the proposed structure are much improved compared with the previously proposed structure. A cascaded adaptive notch filter using the proposed second-order section is also shown. It takes multiple sinusoids corrupted by white Gaussian noise and produces the individual sinusoids at each of the outputs. The results of computer simulation are shown which confirm the theoretical prediction.

The plane wave diffraction by a two-dimensional parallel-plate waveguide cavity with partial material loading is rigorously analyzed for both the E and the H polarization using the Wiener-Hopf technique. Introducing the Fourier transform for the scattered field and applying boundary conditions in the transform domain, the problem is formulated in terms of the simultaneous Wiener-Hopf equations satisfied by the unknown spectral functions. The Wiener-Hopf equations are solved exactly via the factorization and decomposition procedure leading to the formal solution, which involves branch-cut integrals with unknown integrands as well as infinite series with unknown coefficients. Applying rigorous asymptotics with the aid of the edge condition, the approximate solution to the Wiener-Hopf equations is derived in the form suitable for numerical computations. The scattered field inside and outside the cavity is evaluated by taking the inverse Fourier transform together with the use of the saddle point method. Numerical examples of the radar cross section are presented for various physical parameters, and the far field backscattering characteristics of the cavity are discussed in detail. Some comparisons with a high-frequency technique are also given to validate the present method.

Reduction of the complexity of the NLMS algorithm has received attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to the average of the absolute values of the input samples, has been proposed. The convergence analysis of the processing cost reduction method has been derived from a low-pass filter expression. However, in this analysis the effect of the weignt vector components whose adaptations are skipped is not considered in terms of the direction of the gradient estimation vector. In this paper, we use an arbitrary value instead of the average of the absolute values of the input samples as a threshold level, and we derive the convergence characteristics of the processing cost reduction method with arbitrary threshold level for zero-mean white Gaussian samples. From the analytical results, it is shown that the range of the gain constant to insure convergence and the misadjustment are independent of the threshold level. Moreover, it is shown that the convergence rate is a function of the threshold level as well as the gain constant. When the gain constant is small, the processing cost is reduced by using a large threshold level without a large degradation of the convergence rate.

This paper reviews recent progress in adaptive signal processing techniques for digital mobile radio communications. In Radio Signal Processing (RSP) , digital signal processing is becoming more important because it makes it relatively easy to develop sophisticated adaptive processing techniques, Adaptive signal processing is especially important for carrier signal processing in RSP. Its main objective is to realize optimal or near-optimal radio signal transmission. Application environments of adaptive signal processing in mobile radio are clarified. Adaptive equalization is discussed in detail with the focus on adaptive MLSE based on the blind algorithm. Demodulation performance examples obtained by simulations and experiments are introduced, which demonstrates the recent advances in this field. Next, new trends in adaptive array processing, interference cancelling, and orthogonalization processing are reviewed. Finally, the three automatic calibration techniques that are based on adaptive signal processing are described for realizing high precision transmission devices.

This paper presents an adaptive MLSE (Maximum Likelihood Sequence Estimator) suitable for TDMA cellular systems. The proposed MLSE has two special features such as handling wide dynamic range signals without analogue gain controls and fast channel tracking capability. In order to handle wide dynamic range signals without conventional AGCs (Automatic Gain Controller), the proposed MLSE uses envelope components of received signals obtained from a non-linear log-amplifier module which has wide log-linear gain characteristics. By using digital signal processing technique, the log-converted envelope components are normalized and converted to linear values which conventional adaptive MLSEs can handle. As a channel tracking algorithm of the channel estimator, the proposed MLSE adopts a QT-LMS (Quick-Tracking Least Mean Square) algorithm, which is obtained by modifying LMS algorithm to enable a faster tracking capability. The algorithm has a fast tracking capability with low complexity and is suitable for implementation in a fixed-point digital signal processor. The performances of the MLSE have been evaluated through experiments in TDMA cellular environments with π/4-shifted QPSK, 24.3k symbol/sec. It is shown that, under conditions of 65dB amplitude variations and 80Hz Doppler frequency, the MLSE successfully achieves less than 3% B.E.R., which is required for digital cellular systems.

Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.

An adaptive array antenna can be considered as a useful tool of combating with fading in mobile communications. We can directly obtain the optimal weight coefficients without updating in temporal sampling, if the arrival angles and signal-to-noise ratio (SNR) of the desired and the undesired signals can be accurately estimated. The Maximum Entropy Method (MEM) can estimate the arrival angles, and the SNR from spatially sampled signals by an array antenna more precisely than the Discrete Fourier Transform (DFT). Therefore, this paper proposes and investigates an adaptive array antenna based on spatial spectral estimation using MEM. We call it MEM array. In order to reduce complexity for implementation, we also propose a modified algorithm using temporal updating as well. Furthermore, we propose a method of both improving estimation accuracy and reducing the number of antenna elements. In the method, the arrival angles can be approximately estimated by using temporal sampling instead of spatial sampling. Computer simulations evaluate MEM array in comparison with DFT array and LMS array, and show improvement owing to its modified algorithm and performance of the improved method.

An adaptive method analyzing analytic speech signals is proposed in this paper. The method decreases the errors of finite precision on calculation in a method with real coefficients. It is shown from the results of experiments that the proposed method is more useful than adaptive methods with real coefficients.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

A parallel quicksort algorithm in Ada is proposed and analyzed, its computational complexities are derived, and its performance profile is determined by simulation.

This paper proposes using an adaptive array in a base station for signal reception and transmission in order to increase the spectral efficiency without decreasing the cell radius. The adaptive array controls the directivity pattern of the base station to reduce co-channel interference during reception; the same array pattern is applied during transmission to prevent unnecessary illumination. Computer simulation results show that the cluster size can be reduced to one with time division duplexing (TDD), indicating that we can reuse the same frequency group at all cells. Thus, the improvement in spectral efficiency is as much as 16 fold that of an omni-antenna. Moreover, load sharing, which is expected to improve the channel utilization for unbalanced load situations, is available by cell overlapping. Frequency division duplexing (FDD) requires a weight adjust function to be applied for transmission since the difference in frequency between signal reception and transmission causes null positioning error. However, simple LMS-adjusting can provide a cluster size of one as well as cell overlapping when the frequency deference is 5%.

This paper proposes Coherent-HYBrid Direct-Sequence Fast-Frequency-Hopping (CHYB-DS-FFH) CDMA with Adaptive Interference Cancelling (AIC) for cellular mobile communications. The features of CHYB-DS-FFH are symbol-by-symbol frequency diversity and low chip-rate DS multiplexing both of which are based on a coherent FFH modulation and demodulation scheme. The combination of coherent FFH, space diversity, and AIC is very effective for reducing the performance degradation due to interference. Computer simulations demonstrate BER performance of a 2 hop 500-kHz-interval frequency hopping system using () a linear canceller or () a nonlinear canceller. Both systems employ the two branch space diversity reception of 10kb/s QPSK with FFH over a 1MHz system bandwidth. In quasi-static channels, the average BER performance is 10-2 with average Eb/N0 less than 8dB. In dynamic fading channels under full interference conditions, CHYB-DS-FFH with the linear adaptive interference canceller realizes a BER of 10-2 at the average Eb/N0 of 15dB with maximum Doppler frequency fD of 5Hz, whereas CHYB-DS-FFH with the non-linear adaptive interference canceller achieves the same BER at the average Eb/N0 of 15dB with fD, equal to 30Hz.