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.

This paper presents a simple 3-D array configuration for high-resolution 2-D Direction-Of-Arrival (DOA) estimation. Planar array structures like Uniform Rectangular Array (URA) or Uniform Circular Array (UCA) often well estimate azimuth angle but cannot well estimate elevation angle because of short antenna aperture in elevation direction. One may put more number of array elements to improve elevation angle estimation accuracy, however it will require very large hardware and software cost. This paper presents a simple 3-D array structure for high-resolution 2-D DOA estimation only by modifying the height of some array elements in a planar array. Based on the analysis of Cramer-Rao Lower Bound (CRLB) formulation and its dependency on the height of array elements, we develop a simple 3-D array structure which improves elevation angle estimation accuracy while preserving azimuth angle estimation accuracy.