This paper presents a high resolution Direction-Of-Arrival (DOA) estimation method using unwrapped phase information of MUSIC-based noise subspace. Superresolution DOA estimation methods such as MUSIC, Root-MUSIC and ESPRIT methods are paid great attention because of their brilliant properties in estimating DOAs of incident signals. Those methods achieve high accuracy in estimating DOAs in a good propagation environment, but would fail to estimate DOAs in severe environments like low Signal-to-Noise Ratio (SNR), small number of snapshots, or when incident waves are coming from close angles. In MUSIC method, its spectrum is calculated based on the absolute value of the inner product between array response and noise eigenvectors, means that MUSIC employs only the amplitude characteristics and does not use any phase characteristics. Recalling that phase characteristics plays an important role in signal and image processing, we expect that DOA estimation accuracy could be further improved using phase information in addition to MUSIC spectrum. This paper develops a procedure to obtain an accurate spectrum for DOA estimation using unwrapped and differentiated phase information of MUSIC-based noise subspace. Performance of the proposed method is evaluated through computer simulation in comparison with some conventional estimation methods.

This paper proposes a fast method for the calculation of exponential B-splines sampled at regular intervals. This algorithm is based on a combination of FIR and IIR filters which enables a fast decomposition and reconstruction of a signal. When complex values are selected for the parameters of the exponentials, complex trigonometric functions are obtained. Only the real part of these functions are used for the interpolation of real signals, leading less bandlimited signals when they are compared with the polynomial B-spline counterparts. These characteristics were verified with 1-D and 2-D examples. This paper also discusses the effectiveness of exponential B-splines, when they are applied to image processing.

We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning.

DOA (Direction Of Arrival) estimation is a useful technique in various positioning applications including the DOA-based adaptive array antenna system. This paper presents a practical implementation of FPGA (Field Programmable Gate Array) based fast DOA estimator for wireless cellular basestation. This system incorporates spectral unitary MUSIC (MUltiple SIgnal Classification) algorithm, which is one of the representative super resolution DOA estimation techniques. This paper proposes a way of digital signal processor design suitable for FPGA and its real hardware implementation. In this system, all digital signal processing procedures are computed by the only fixed-point operation with finite word-length for fast processing and low power consumption. The performance will be assessed by hardware level simulations and experiments in a radio anechoic chamber.

This letter presents an efficient multichannel low-IF reception scheme that improves digital communication quality in the sense of BER performance. Created by simply adding cosine rolloff filters to the conventional multichannel receiver, the proposed receiver achieves much higher accuracy than the conventional one.

We study the correlation matrix element properties in array signal processing and apply them to a Direction-Of-Arrival (DOA) estimation problem of coherent or highly-correlated sources for a Uniform Linear Array (ULA). The proposed algorithm is generally based on the relation between the elements of the array correlation matrix and does not need an eigendecomposition, iteration, or angular peak-search. The performance of the proposed method was evaluated through a computer simulation.

In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.

This letter presents an efficient Signed-Power-of-Two (SPT) term allocation for filter coefficients in order to improve the BER characteristics of digital communication systems. The performance of the present allocation is evaluated by BER characteristics through digital modulation simulations and FPGA-based digital implementation.

This paper presents a simple but high resolution DOA estimation method using second-order differential of MUSIC spectrum. MUSIC method is paid attention as one of "Superresolution" DOA estimation methods because of their brilliant characteristics, however MUSIC also has the problem of estimation accuracy in severe environments like low SNR, small number of snapshots, or incident waves from closely-spaced angles. Especially the case of two or more incident waves from closely-spaced angles, MUSIC often fails in making spectrum peaks that leads inaccurate DOA estimation. We pay attention to the fact that the second-order differential of MUSIC spectrum makes negative peaks around the original DOAs even when MUSIC spectrum does not make peaks there. We try to estimate DOAs not by MUSIC spectrum but by the second-order differential of the MUSIC spectrum, and to find its peaks for being estimated DOAs. The performance of the present method is evaluated in compared with MUSIC and Root-MUSIC methods through computer simulations and experiments.

This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signal-to-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations.

This paper proposes a novel fast algorithm for the decomposition and reconstruction of two-dimensional (2-D) signals by box splines. The authors have already proposed an algorithm to calculate the discrete box splines which enables the fast reconstruction of 2-D signals (images) from box spline coefficients. The problem still remains in the decomposition process to derive the box spline coefficients from an input image. This paper first investigates the decomposition algorithm which consists of the truncated geometric series of the inverse filter and the steepest descent method with momentum (SDM). The reconstruction process is also developed to correspond to the enlargement of images. The proposed algorithm is tested for the expansion of several natural images. As a result, the peak signal-to-noise ratio (PSNR) of the reconstructed images became more than 50 dB, which can be considered as enough high level. Moreover, the property of box splines are discussed in comparison with 2-D (the tensor product of) B-splines.

This paper presents a framework of multimode fully digital receiver implementation using direct RF-to-digital conversion. In this architecture the entire band including multiple RF systems is directly converted to digital by a wideband high speed ADC, and the RF systems can be easily switched by only digital signal processing with the minimum analog RF components. The digital RF front-end consists of parallel processing blocks for parallel data streams considering practical ADC's configuration. The RF signals are converted into baseband through digital IF stage and the data rates are made down by two steps of decimation. In this paper, a principle investigation into a dualmode system implementation is presented for simplicity. The circuit resource and the robustness to the spurs (spurious outputs) of an NCO (numerically controlled oscillator) in the proposed design will be presented. The proposed architecture was implemented with an FPGA on the developed prototype system and the operations were also verified.

This paper presents a method of synthesizing covariance matrix elements of array input signal for high resolution 2-D Direction-Of-Arrival (DOA) estimation via antenna (sensor) switching. Antenna array generally has the same number of array elements and receiver modules which often leads large receiver hardware cost. Two of the authors have already studied a way of antenna switching to reduce receiver cost, but it can be applied only for periodic incident signals like sinusoid. In this paper, we propose two simple methods of DOA estimation from sparse data by synthesizing covariance matrix elements of array input signal via antenna switching, which can also be applied to DOA estimation of antiperiodic incident signals. Performance of the proposed approach is evaluated in detail through some computer simulation.

Computing the Eigen Value Decomposition (EVD) of a symmetric matrix is a frequently encountered problem in adaptive (or smart or software) antenna signal processing, for example, super resolution DOA (Direction Of Arrival) estimation algorithms such as MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique). In this paper the hardware architecture of the fast EVD processor of symmetric correlation matrices for the application of an adaptive antenna technology such as DOA estimation is proposed and the basic idea is also presented. Cyclic Jacobi method is well known for the simplest algorithm and easily implemented but its convergence time is slower than other factorization algorithm like QR-method. But if considering the fast parallel computation of the EVD with a hardware architecture like ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), the Jacobi method can be a appropriate solution, since it offers a quite higher degree of parallelism and easier implementation than other factorization algorithms. This paper computes the EVD using a Jacobi-type method, where the vector rotations and the angles of the rotations are obtained by CORDIC (COordinate Rotation DIgital Computer). The hardware architecture suitable for ASIC or FPGA with fixed-point arithmetic is presented. Because it consists of only shift and add operations, this hardware friendly feature provides easy and efficient implementation. In this paper, the computational load, the estimate of circuit scale and expected performance are discussed and the validation of fixed-point arithmetic for the practical application to MUSIC DOA estimation is examined.

This paper presents a novel automated microwave filter tuning method based on successive optimization of phase and amplitude characteristics. We develop an optimization procedure to determine how much the adjusting screws of a filter should be rotated. The proposed filter tuning method consists of two stages; coarse and fine tuning stages. In the first stage, called coarse tuning, the phase response error of the target filter is minimized so that the filter roughly approximates almost ideal bandpass characteristics. Then in the second stage, called fine tuning, two different amplitude response errors are minimized in turn and then the resulting filter well approximate the ideal characteristics. Performance of the proposed tuning procedure is evaluated through some experiments of actual filter tuning.

An approximate scheme for decomposing and reconstructing a continuous-time signal as a linear combination of the B-splines is studied. It is an oversampling discrete-time implementation derived by substituting the multifold RRS functions for the B-splines. The RRS functions are multifold discrete convolution of the sampled rectangular functions. Analysis of the scheme yields conditions for the circuit parameters to assure stability and required precision. A design example is presented that makes the error less than 1% in the supremal norm by the oversampling ratio of 512. Its numerical simulation is also presented.

This paper presents a scheme for accurately detecting the number of incident waves arriving at array antennas. The array antenna and MIMO techniques are developing as 4th generation mobile communication systems and wireless LAN technologies, and the accurate estimation of the propagation environment is becoming more important. This paper emphasizes the accurate detection of the number of incident waves; one of the important characteristics in multidirectional communication. There are some recent papers on accurate detection but they have problems of estimation accuracy or computational cost in severe environment like low SNR, small number of snapshots or waves with close angles. Hence, AIC and MDL methods based on statistics and information theory are still often used. In this paper, we propose an accurate estimation method of the number of arrival signals using the orthogonality of subspaces derived from preliminary estimation of signal subspace. The proposed method accurately estimates the number of signals also in severe environments where AIC and MDL methods can hardly estimate. We evaluate the performance of these methods through some computer simulation and experiments in anechoic chamber.

This paper presents a method to optimize 2-D sparse array configurations along with a technique to interpolate holes to accurately estimate the direction of arrival (DOA). Conventional 2-D sparse arrays are often defined using a closed-form representation and have the property that they can create hole-free difference co-arrays that can estimate DOAs of incident signals that outnumber the physical elements. However, this property restricts the array configuration to a limited structure and results in a significant mutual coupling effect between consecutive sensors. In this paper, we introduce an optimization-based method for designing 2-D sparse arrays that enhances flexibility of array configuration as well as DOA estimation accuracy. We also propose a method to interpolate holes in 2-D co-arrays by nuclear norm minimization (NNM) that permits holes and to extend array aperture to further enhance DOA estimation accuracy. The performance of the proposed optimum arrays is evaluated through numerical examples.

This paper presents a radio-based real-time moving object tracking method based on Kalman filtering using a phase-difference compensation technique and a non-uniform pulse transmission scheme. Conventional Kalman-based tracking methods often require time, amplitude, phase information and their derivatives for each receiver antenna; however, their location estimation accuracy does not become good even with many transmitting pulses. The presented method employs relative phase-difference information and a non-uniform pulse generation scheme, which can greatly reduce the number of transmitting pulses while preserving the tracking accuracy. Its performance is evaluated in comparison with that of conventional methods.

In this paper, a new array geometry is proposed which is capable of performing underdetermined Direction-Of-Arrival (DOA) estimation for the circular array configuration. DOA estimation is a classical problem and one of the most important techniques in array signal processing as it has applications in wireless and mobile communications, acoustics, and seismic sensing. We consider the problem of estimating DOAs in the case when we have more sources than the number of physical sensors where the resolution must be maintained. The proposed array geometry called Nested Sparse Circular Array (NSCA) is an extension of the two level nested linear array obtained by nesting two sub-circular arrays and one element is placed at the origin. In order to extend the array aperture, a Khatri-Rao (KR) approach is applied to the proposed NSCA which yields the virtual array structure. To utilize the increase in the degrees of freedom (DOFs) that this new array provides, a subspace based approach (MUSIC) for DOA estimation and l1-based optimization approach is extended to estimate DOAs using NSCA. Simulations show that better performance for underdetermined DOA estimation is achieved using the proposed array geometry.