In this paper, we analyze the convergence behavior of the CMA (Constant Modulus Algorithm) adaptive array working under the steepest decent method, and investigate how to achieve the highest possible output SINR (Signal to Interference plus Noise Ratio). In multipath radio environments, CMA sometimes suppresses the desired signal (stronger one) and selects to receive the interference (the weaker one) resulting in the low output SINR. This is one of the problems met in an optimization system under a nonlinear control equation where there are two or more local minima of the cost function and the final state depends on the initial condition. The study can be done only numerically by starting from various initial values. In our analysis of the CMA adaptive array in multipath radio environments, we will assume that there are two waves which are radiated from the same source and try to find out what conditions may affect the convergence behavior. In this process, we discover the effect of the neglected factor by the previous papers and revise the initial condition to guarantee the reception of the desirable wave. In conclusion, we propose the prescription of the initial weights of the array elements as well as the choice of preferable arrays.

We demonstrate a feasibility of a Beam Space CMA (Constant Modulus Algorithm) Adaptive array antenna by implementing a Digital Signal Processor (DSP) in ASICs using field programmable gate arrays (FPGA). The DSP can synthesize 16 multibeams and eliminate interference signals by CMA adaptive processing. The whole function was implemented in about 127,000 equivalent gates. Simple experimental results in a radio anechoic chamber have confirmed the basic function of BSCMA adaptive array antenna.

The MUSIC algorithm has proven to be an effective means of estimating parameters of multiple incoherent signals. Furthermore, the forward-backward (FB) spatial smoothing technique has been considered the best preprocessing method to decorrelate coherent signals. In this paper, we propose a novel preprocessing technique based upon ideas associated with the FB and adaptive spatial smoothing techniques and report on its superiority in numerical simulations.

Signal processing antennas have been studied not only for interference suppression but also for high-resolution estimation of radio environment such as directions-of-arrival of incident signals. These two applications are based on the common technique, that is, null steering. This tutorial paper reviews the MUSIC algorithm which is one of the typical high-resolution techniques. Examining the eigenvector beam patterns, we demonstrate that the high-resolution capability is realized by steering nulls. The considerations will be useful for understanding the high-resolution techniques in the signal processing antennas. We then describe a modified version of MUSIC (Root MUSIC). We show the performance and robustness of the method. Furthermore, we introduce radar target identification and two-dimensional radar target imaging. These study fields are new applications of the signal processing antennas, to which a great deal of attention has been devoted recently.

The tandem structure of a matched filter (MF) and a maximum likelihood sequence estimator (MLSE) using the Viterbi algorithm (VA) has been considered to be an optimal receiver for digital pulse-amplitude sequences in the presence of intersymbol interference (ISI) and additive white Gaussian noise (AWGN). An adaptive array antenna has the capability of filtering received signals in the spatial domain as well as in the temporal one. In this paper, we propose a receiver structure using an adaptive array antenna, a digital filter and the VA that is spatially and temporally optimal for multi-user detection in a direct sequence code division multiple access (DS/CDMA) environment. This receiver uses a tapped delay line (TDL) array antenna and the VA, which provides a maximum likelihood sequence estimate from the spatially and temporally whitened matched filter (ST-WMF) output. Performance of the proposed receiver is evaluated by theoretical analysis and computer simulations.

This paper addresses approaches to enhancement of performance of the CMA (Constant Modulus Algorithm) adaptive array antenna in multipath environments that characterize the mobile radio communications. The cost function of the CMA reveals that it has an AGC (Automatic Gain Control) procedure of holding the array output voltage at a constant value. Therefore, if the output voltage by the initial weights is different from the object value, then the CMA may suffer from slow convergence because suppression of the multipath waves is delayed by the AGC behavior. Our objective is to improve the convergence characteristics by adopting the differential CMA for the adaptive array algorithm. First, the basic performance of the differential CMA is clarified via computer simulation. Next, the differential CMA is incorporated into the eigen-beamspace system in which the eigenvectors of the correlation matrix of array inputs are used in the BFN (Beam Forming Network). This BFN creates the optimum orthogonal multibeams for radio environments and works helpfully as a preprocessor of the differential CMA. The computer simulation results have demonstrated that the differential CMA with the eigen-beamspace system has much better convergence characteristics than the conventional CMA with the element space system. Furthermore, a modified algorithm is introduced which gives the stable array output voltages after convergence, and it is confirmed that the algorithm can carry out more successful adaptation even if the radio environments are changed abruptly.

Conventional subband ADF's (adaptive digital filters) using filter banks have shown a degradation in performance because of the non-ideal nature of filters. To solve this problem, we propose a new type of subband ADF incorporating two types of analysis filter bank. In this paper, we show that we can design the optimum filter bank which minimizes the LMSE (least mean squared error). In other words, we can design a subband ADF with less MSE than that of conventional subband ADF's.

High power interference rejection characteristics of a sidelobe canceller which have not been well discussed yet are investigated through computer simulation and experiment in the real radio wave environment. To improve the high power interference rejection performance, a new method is considered. The performance of the method is also analyzed through computer simulation and experiment.

This paper proposes a new algorithm called the fast Projection algorithm, which reduces the computational complexity of the Projection algorithm from (p+1)L+O(p3) to 2L+20p (where L is the length of the estimation filter and p is the projection order.) This algorithm has properties that lie between those of NLMS and RLS, i.e. less computational complexity than RLS but much faster convergence than NLMS for input signals like speech. The reduction of computation consists of two parts. One concerns calculating the pre-filtering vector which originally took O(p3) operations. Our new algorithm computes the pre-filtering vector recursively with about 15p operations. The other reduction is accomplished by introducing an approximation vector of the estimation filter. Experimental results for speech input show that the convergence speed of the Projection algorithm approaches that of RLS as the projection order increases with only a slight extra calculation complexity beyond that of NLMS, which indicates the efficiency of the proposed fast Projection algorithm.

The main purpose of this paper is to give a new representation method of the convergence characteristics of the LMS algorithm using tap-input vectors. The described representation method is an extended version of the interpretation method based on the orthogonal projection. Using this new representation, we can express the convergence characteristics in terms of tap-input vectors instead of the eigenvalues of the input signal. From this representation, we consider a general method for improving the convergence speed.

Research in radar polarimetry is hampered by shortcomings of the conventional formulation of polarimetric backscatter concepts. In particular the correct form of the Sinclair backscatter matrix under changes of polarization bases is derived from the antenna voltage (energy transfer) equation yielding the erroneous impression that radar polarimetry is a mongrel between scattering behavior and network performance. The present contribution restores logical consistency in a natural way by introducing the concept of an antilinear backscatter operator. This approach decouples scattering process and network performance, illuminates matrix analytical properties of the radar backscatter matrix and highlights characteristic states of polarization.

This paper discusses the performance of non-coherent reception for M-ary spread-spectrum (M-ary/SS) signals in the presence of carrier frequency offset. In general, the M-ary/SS scheme is expected to be of higher spectral efficiency than the conventional DS/SS schemes, but its performance may be degraded by the carrier frequency offset. We, therefore, analyze the effect of carrier frequency offset on the performance of the non-coherent M-ary/SS system with orthogonal modulation using a set of sequences generated by the Hadamard matrix. As a result of the analysis, it has been found that the carrier frequency offset may cause a great deal of degradation in the performance, and that its effect has a distinctive property which is due to the characteristic of Hadamard matrix, at the same time. Making use of this property, we propose two schemes that can mitigate the effect of carrier frequency offset: one is based on choise of the code sequences, the other is on the error correcting code. The effectiveness of the schemes is evaluated in the terms of symbol-error-rates through analysis and computer simulation.

We propose an algorithm for image decomposition based on Hadamard functions, realized by answer-in-weights neural network, which has simple architecture and is explored with steepest decent method. This scheme saves memory consumption and it converges fast. Simulations with least mean square (LMS) and absolute mean (AM) errors on a 128128 image converge within 30 training epochs.

In this letter, we introduce a predictor based least square (PLS) algorithm. By involving both order- and time-update recursions, the PLS algorithm is found to have a more stable performance compared with the stable version (Version II) of the RLS algorithm shown in Ref.[1]. Nevertheless, the computational requirement is about 50% of that of the RLS algorithm. As an application, the PLS algorithm can be applied to the fast Newton transversal filters (FNTF). The FNTF algorithms suffer from the numerical instability problem if the quantities used for extending the gain vector are computed by using the fast RLS algorithms. By combing the PLS and the FNTF algorithms, we obtain a much more stable performance and a simple algorithm formulation.

Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.

A generalized surface scattering radar equation for a near-nadir-looking pencil beam radar, which covers both beam-limited and pulse-limited regions, is derived. This equation is a generalization of the commonly used nadir-pointing beam-limited radar equation taking both antenna beam and pulse wave form weighting functions into account, and is convenient for the calculation of radar received power and scattering cross-section of the surface.

This paper describes modifications to a previously proposed 8-kb/s 4-ms-delay CELP speech coding algorithm with a view to improving the speech quality while maintaining low delay and only moderately increasing complexity. The modifications are intended to improve the effectiveness of interframe pitch lag prediction and the sub-optimality level of the excitation coding to the backward adapted synthesis filter by using delayed decision and joint optimization techniques. Results of subjective listening tests using Japanese speech indicate that the coded speech quality is significantly superior to that of the 8-kb/s VSELP coder which has a 20-ms delay. A method that reduces the computational complexity of closed-loop 3-tap pitch prediction with no perceptible degradation in speech quality is proposed, based on representing the pitch-tap vector as the product of a scalar pitch gain and a normalized shape codevector.

This paper describes a spatial and temporal multipath channel model which is useful in array antenna environments for mobile radio communications. From this model, a no distortion criterion, that is an extension of the Nyquist criterion, is derived for equalization in both spatial and temporal domains. An adaptive tapped-delay-line (TDL) array antenna is used as a tool for equalization in both spatial and temporal domains. Several criterion for such spatial and temporal equalization such as ZF (Zero Forcing) and MSE (Mean Square Error), are available to update the weights and tap coefficients. In this paper, we discuss the optimum weights based on the ZF criterion in both spatial and temporal domains. Since the ZF criterion satisfies the Nyquist criterion in case of noise free, this paper applies the ZF criterion for the spatial and temporal equalization as a simple case. The Z transform is applied to represent the spatial and temporal model of the multipath channel and to derive the optimal weights of the TDL array antenna. However, in some cases the optimal antenna weights cannot be decided uniquely. Therefore, the effect on the equalization errors due to a finite number of antenna elements and tap coefficients can be shown numerically by computer simulations.

This paper proposes a modified normalized LMS algorithm based on a long-term average of the reference input signal power. The reference input signal power for normalization is estimated by using two leaky integrators with a short and a long time constants. Computer simulation results compare the performance of the proposed algorithm with some previosuly proposed adaptive-step algorithms. The proposed algorithm converges faster than the conventional adaptive-step algorithms. Almost 30dB of the ERLE (Echo Return Loss Enhancement), which is comparable to the conventional algorithms, is achieved in noisy environments.

This paper proposes an unsupervised speaker adaptation method using an all-phoneme ergodic Hidden Markov Network" that combines allophonic (context-dependent phone) acoustic models with stochastic language constraints. Hidden Markov Network (HMnet) for allophone modeling and allophonic bigram probabilities derived from a large text database are combined to yield a single large ergodic HMM which represents arbitrary speech signals in a particular language so that the model parameters can be re-estimated using text-unknown speech samples with the Baum-Welch algorithm. When combined with the Vector Field Smoothing (VFS) technique, unsupervised speaker adaptation can be effectively performed. This method experimentally gave better performances compared with our previous unsupervised adaptation method which used conventional phonetic HMMs and phoneme bigram probabilities especially when the amount of training data was small.