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.

Recently the effect of the angular spread caused by locally scattered signals in the vicinity of the mobile has received considerable attention. This paper proposes the Extended Array Mode Vector (EAMV) which represents the Instantaneous Angular Spread (IAS) as well as the Instantaneous Direction Of Arrival (IDOA) of the received signal at the Base Station (BS). Using the EAMV, MUSIC algorithm is generalized in order that it is possible to estimate both the IDOA and the IAS. In computer simulations, the estimates of the IDOA and the IAS in the fading situation are evaluated. The results show that the estimates for small angular spread agree well with the given values and demonstrate the validity of the proposed approach.

This paper proposes a 2.6 GHz low cost DBF array antenna system and reports its evaluation based on our experimental results. The proposed system is partially constructed by digital devices for the simplification of hardware, and employs some techniques for improving the resolution. The system is evaluated through the DOA estimation by the MUSIC algorithm inside a radio anechoic chamber. As a result, we found that the proposed system estimates the DOA with the highest accuracy at which MUSIC algorithm can perform. Moreover, this paper discusses the estimation errors. We also found that the estimation error is particularly affected from the inaccurate element interval.

In this paper, a new approach to adaptive direction-of-arrival (DOA) estimation based upon a database retrieval technique is proposed. In this method, angles and signal powers are quantized, and a set of true correlation vectors of the array antenna input vectors for various combinations of the quantized angles and signal powers is stored in a database. The k-d tree is then selected as the data structure to facilitate range searching. Estimated a correlation vector, range searching is performed to retrieve several correlation vectors close to it from the k-d tree. The DOA and the signal power are estimated by taking the weighted average of angles and powers associated with the retrieved correlation vectors. Unlike the other high-resolution methods, this method requires no eigenvalue computation, thus allowing a fast computation. It is shown through simulation results that the processing speed of the proposed method is much faster than that of the root-MUSIC that requires the eigenvalue decomposition.

In high-speed TDMA mobile communications, frequency-selective fading is a serious problem because a delay time difference between multipath signals is large in comparison with symbol duration. We have proposed a spatial-domain RAKE receiver using a multibeam adaptive antenna to reduce frequency-selective fading and to realize path-diversity. The multibeam adaptive antenna resolves multipath signals in the spatial domain, and combines array outputs. In this paper, we propose the application of MUSIC algorithm to estimation of the time delays of multipath signals to make the incident signals coincide with a common reference signal. Because the MUSIC algorithm can estimate the time delays accurately, the BER performance of the proposed scheme is improved. Furthermore, we propose weighting factors which easily realize the maximal-ratio combining.

A novel cumulant based MUSIC like DOA estimation algorithm for multicarrier modulation has been proposed in this paper. While the conventional MUSIC algorithm is not applicable to a correlation matrix calculated from received signals transmitted over the different carriers, the proposed algorithm can estimate the DOA of the signals with multicarrier modulation. The proposed algorithm does not require the sensor array responses for the frequency range of the interest and the initial phases of the carriers. With the proposed algorithm the number of signals whose DOA are estimated can be increased and the accuracy of the DOA estimation can be improved by employing larger number of carriers.

This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low computation time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.

An efficient algorithm is proposed to identify the active users and extracting their respective timing information in asynchronous direct sequence CDMA (DS-CDMA) communication system over Rayleigh fading channel. The joint identification and timing estimation algorithm is derived by performing discrete Fourier transform (DFT) on the observation vector and exploiting the uniqueness and nullity characteristics of the root-MUSIC test polynomial. The root-MUSIC based algorithm is shown to be asymptotically near-far resistant. Compared to the maximum a posteriori (MAP) or maximum likelihood (ML) based multiuser timing estimator, the complexity is greatly reduced by separating the multi-dimensional optimization problem into several polynomial rooting problems. Moreover, we characterize the dependence of system performance with respect to signature sequence length, number of active users, window size, desired user's signal-to-noise ratio (SNR) and crosscorrelation property of the code structure. The analytical results reveal that under the uncorrelated Rayleigh fading model, the root-MUSIC timing estimator tends to achieve the Cramer-Rao lower bound (CRLB) at interesting signature sequence length and desired user's SNR.

Recently , a short range millimeter wave or a microwave sensing system has been extensively studied to estimate a target position or a source location. It can be applied to indoor propagation analysis, carborne applications, etc. The application of the superresolution technique has been proposed to obtain a high resolution performance in the time domain or the spatial domain. However, the availability of the polarization synthesis in the receiving antennas has not been considered. In this paper, we use a pair of polarized swept frequency data and propose two modifications of the MUSIC algorithm to enhance the resolution of time delay. One modification is the correlation matrix formulation which relates to the total signal power, and the other is a polarization filtering applied to the correlation matrix. These modifications have advantages such that. 1)Reduction of the estimation problem to the delay time estimation only; 2)Easy implementation. Experimental results are illustrated to show the availability of the methods, and to confirm the high resolution performance compared with the conventional method.

It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.

We propose the Kernel MUSIC algorithm as an improvement over the conventional MUSIC algorithm. This algorithm is based on the orthogonality between the image and kernel space of an Hermitian mapping constructed from the received data. Spatial smoothing, needed to apply the MUSIC algorithm to coherent signals, is interpreted as constructing procedure of the Hermitian mapping into the subspace spanned by the constituent vectors of the received data. We also propose a new spatial smoothing technique which can remove the redundancy included in the image space of the mapping and discuss that the removal of redundancy is essential for improvement of resolution. By computer simulation, we show advantages of the Kernel MUSIC algorithm over the conventional one, that is, the reduction of processing time and improvement of resolution. Finally, we apply the Kernel MUSIC algorithm to the Laser Microvision, an optical misroscope we are developing, and verify that this algorithm has about two times higher resolution than that of the Fourier transform method.

A new superresolution technique is proposed for high-resolution estimation of the scattering analysis. For complicated multipath propagation environment, it is not enough to estimate only the delay-times of the signals. Some other information should be required to identify the signal path. The proposed method can estimate the frequency characteristic of each signal in addition to its delay-time. One method called modified (Root) MUSIC algorithm is known as a technique that can treat both of the parameters (frequency characteristic and delay-time). However, the method is based on some approximations in the signal decorrelation, that sometimes make problems. Therefore, further modification should be needed to apply the method to the complicated scattering analysis. In this paper, we propose to apply a time-domain null filtering scheme to reduce some of the dominant signal components. It can be shown by a simple experiment that the new technique can enhance estimation accuracy of the frequency characteristic in the Root-MUSIC algorithm.

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.

Analyzing multipath propagation structure is important to develop anti-fading techniques for high-speed digital radio systems. Several techniques have been employed to measure delay profiles and/or arrival angles. This paper presents a simultaneous estimation method of delay times and arrival angles of indoor multipath waves. We obtain frequency-domain data at different receiving antenna positions using a network analyzer. We estimate the propagation parameters by means of a two-dimensional MUSIC algorithm. In order to obtain reliable results, a two-dimensional discrete inverse Fourier transform and a gating technique are employed before the MUSIC algorithm. Simulation and experimental results show that the proposed method can estimate the propagation parameters properly.

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.

A new superresolution technique is proposed for antenna pattern measurements. Unwanted reflected signals often impinge on the antenna when we measure it outdoors. A time-domain superresolution technique (a MUSIC algorithm) has been proposed to eliminate the unwanted signal for a narrow pass-band antenna. The MUSIC algorithm needs many snapshots to obtain a correlation matrix. This is not preferable for antenna pattern measurements because it takes a long time to obtain the data. In this paper, we propose to reduce a noise component (stochastic quantity) using the FFT and gating techniques before we apply the MUSIC. The new technique needs a few snapshots and saves the measurement time.

A superresolution technique is considered for use in antenna gain measurements. A modification of the MUSIC algorithm is employed to resolve incident signals separately in the time domain. The modification involves preprocessing the received data using a spatial scheme prior to applying the MUSIC algorithm. Interference rejection in the antenna measurements using the fast Fourier transform (FFT) based techniques have been realized by a recently developed vector network analyzer, and its availability has been reported in the literature. However, response resolution in the time domain of these conventional techniques is limited by the antenna bandwidth. The MUSIC algorithm has the advantage of being able to eliminate unwanted responses when performing antenna measurements in situations where the antenna band-width is too narrow to support FFT based techniques. In this paper, experimental results of antenna gain measurements in a multipath environment show the accuracy and resolving power of this technique.

A high resolution iterative algorithm for estimating the direction-of-arrival of multiple wide band sources is proposed in this paper. For equally spaced array structure, two Unitary Transform based approaches are proposed in frequency domain for signal subspace processing in both coherent multipath and incoherent environment. Given a priori knowledge of the initial estimates of DOA, with proper spatial prefiltering to separate multiple groups of closely spaced sources, our proposed algorithm is shown to have high resolution capability even in coherent multipath environment without reducing the angular resolution, compared with the use of subarray. Compared with the conventional algorithm, the performance by the proposed algorithm is shown by the simulations to be improved under low Signal to Noise Ratio (SNR) while the performance is not degraded under high SNR. Moreover the computation burden involved in the eigencomputation is largely reduced by introducing the Pesudo-Hermitian matrix approximation.