Parallel Factor-Based Model for Two-Dimensional Direction Estimation

Two-dimensional (2D) Direction-of-Arrivals (DOA) estimation for elevation and azimuth angles assuming noncoherent, mixture of coherent and noncoherent, and coherent sources using extended three parallel uniform linear arrays (ULAs) is proposed. Most of the existing schemes have drawbacks in estimating 2D DOA for multiple narrowband incident sources as follows: use of large number of snapshots, estimation failure problem for elevation and azimuth angles in the range of typical mobile communication, and estimation of coherent sources. Moreover, the DOA estimation for multiple sources requires complex pair-matching methods. The algorithm proposed in this paper is based on first-order data matrix to overcome these problems. The main contributions of the proposed method are as follows: (1) it avoids estimation failure problem using a new antenna configuration and estimates elevation and azimuth angles for coherent sources; (2) it reduces the estimation complexity by constructing Toeplitz data matrices, which are based on a single or few snapshots; (3) it derives parallel factor (PARAFAC) model to avoid pair-matching problems between multiple sources. Simulation results demonstrate the effectiveness of the proposed algorithm

LDL Decomposition-based Real-time FPGA Implementation of DOA Estimation

An FPGA implementation and real-time experimental verification of proposed direction of arrival (DOA) estimation algorithm employing LDL factorization are presented in this paper. The proposed algorithm is implemented on a Xilinx FPGA using LabVIEW software and its real-time experimental verification is performed using National Instruments (NI) PXI platform. The proposed method has several advantages over well-known methods which are based on either eigenvalue decomposition (EVD) or singular value decomposition (SVD). It provides faster execution since LDL factorization requires O(n3/6) number of operations whereas EVD requires O(n3). Results from Matlab simulations and real-time experiments demonstrate the effectiveness of the proposed method. Successful FPGA compilation reports show low resource usage and faster computation time for LDL-based method compared with QRbased implementations. Performance comparison is done in terms of estimation accuracy, FPGA processing time and resource utilization

Matrix Decomposition Methods for Efficient Hardware Implementation of DOA Estimation Algorithms: A Performance Comparison

Matrix operations form the core of array signal processing algorithms such as those required for direction of arrival (DOA) angle estimation of radio frequency signals incident on an antenna array. In this paper, we present a performance comparison of matrix decomposition methods for efficient FPGA hardware implementation of DOA estimation algorithms. These methods are very important in subspace-based DOA estimation algorithms as they are used for signal space extraction. DOA estimation algorithms employing LU, LDL, Cholesky, and QR decomposition methods are implemented on a Xilinx Virtex-5 FPGA. These DOA estimation algorithms are simulated in LabVIEW as well as experimentally validated in real-time on a prototype testbed constructed using Universal Software Radio Peripheral (USRP) Software Defined Radio (SDR) platform from National Instruments. Performance comparison of these algorithms is made in terms of resources consumption, computation speed, and estimation accuracy

FPGA-Based Hardware Implementation of Computationally Efficient Multi-Source DOA Estimation Algorithms

ABSTRACT Hardware implementation of proposed direction of arrival (DOA) estimation algorithms based on Cholesky and LDL decomposition is presented in this paper. The proposed algorithms are implemented for execution on an FPGA (field programmable gate array) as well as a PC (running LabVIEW) for multiple non-coherent sources located in the far-field region of a uniform linear array (ULA). Prototype testbeds built using National Instruments (NI) Universal Software Radio Peripheral (USRP) software defined radio (SDR) platform and Xilinx Virtex-5 FPGA are originally constructed for the experimental validation of the proposed algorithms. Results from LabVIEW simulations and real-time hardware experiments demonstrate the effectiveness of the proposed algorithms. Specifically, the implementation of proposed algorithms on a Xilinx Virtex-5 FPGA using LabVIEW software clarifies their efficiency in terms of computation time and resource utilization, which make them suitable for real-time practical applications. Moreover, performance comparison with QR decomposition-based DOA algorithms as well as similar FPGA-based implementations reported in the literature is conducted in terms of estimation accuracy, computation speed, and FPGA resources consumed

FPGA Hardware Implementation of DOA Estimation Algorithm Employing LU Decomposition

In this paper, authors present their work on field-programmable gate array (FPGA) hardware implementation of proposed direction of arrival estimation algorithms employing LU factorization. Both L and U matrices were considered in computing the angle estimates. Hardware implementation was done on a Virtex-5 FPGA and its experimental verification was performed using National Instruments PXI platform which provides hardware modules for data acquisition, RF down-conversion, digitization, etc. A uniform linear array consisting of four antenna elements was deployed at the receiver. LabVIEW FPGA modules with high throughput math functions were used for implementing the proposed algorithms. MATLAB simulations of the proposed algorithms were also performed to validate the efficacy of the proposed algorithms prior to hardware implementation of the same. Both MATLAB simulation and experimental verification establish the superiority of the proposed methods over existing methods reported in the literature, such as QR decomposition-based implementations. FPGA compilation results report low resource usage and faster computation time compared with the QR-based hardware implementation. Performance comparison in terms of estimation accuracy, percentage resource utilization, and processing time is also presented for different data and matrix sizes

FPGA-based real-time implementation for direction-of-arrival estimation

Direction-of-arrival (DOA) estimation of radio signals is of utmost importance in many commercial and military applications. In this study, the authors propose an efficient field-programmable gate array (FPGA) architecture for implementing a recently published DOA estimation algorithm. This algorithm estimates DOAs by making use of QR decomposition of the received data matrix of four- and eight-element uniform linear antenna arrays. The hardware implementation has been thoroughly analysed and experimentally validated by building a real-time prototype of the DOA estimation algorithm. The experimental results show good agreement between DOA estimates obtained by the prototype and true values

A unitary MUSIC-like algorithm for coherent sources

This paper proposes a method for direction of arrival (DOA) estimation which can be applied in case of both non-coherent and coherent sources. In comparison to the well-known subspace algorithms such as MUSIC, the proposed method has several advantages. First, in contrast to MUSIC, no forward/backward spatial smoothing for the covariance matrix is needed in the case of coherent sources. Second, the proposed method is more suitable for realtime implementation since it only requires one or a few snapshots in order to provide an accurate DOA estimation, whereas MUSIC requires a large number of snapshots. Third, the proposed method exploits the eigenvalue decomposition (EVD) of a real-valued covariance matrix thereby reducing the computational cost by at least a factor of four. Simulation results show that the proposed method can estimate the DOAs of the incident sources with high accuracy even when the sources are coherent. © 2007 IEEE

A fast algorithm for direction of arrival estimation in multipath environments

A new spectral direction of arrival (DOA) estimation algorithm is proposed that can rapidly estimate the DOA of non-coherent as well as coherent incident signals. As such the algorithm is effective for DOA estimation in multi-path environments. The proposed method constructs a data model based on a Hermitian Toeplitz matrix whose rank is related to the DOA of incoming signals and is not affected if the incoming sources are highly correlated. The data is rearranged in such a way that extends the dimensionality of the noise space. Consequently, the signal and noise spaces can be estimated more accurately. The proposed method has several advantages over the well-known classical subspace algorithms such as MUSIC and ESPRIT, as well as the Matrix Pencil (MP) method. In particular, the proposed method is suitable for real-time applications since it does not require multiple snapshots in order to estimate the DOA\u27s. Moreover, no forward/backward spatial smoothing of the covariance matrix is needed, resulting in reduced computational complexity. Finally, the proposed method can estimate the DOA of coherent sources. The simulation results verify that the proposed method outperforms the MUSIC, ESPRIT and Matrix Pencil algorithms

Unitary Root MUSIC and Unitary MUSIC with Real-Valued Rank Revealing Triangular Factorization

This paper presents two methods to estimate the two dimensional (2-D) direction of arrival (DOA) for coherent and non-coherent sources. The proposed methods have many advantages over existing schemes. First, they construct the data from a single snapshot in a Toeplitz form, whose rank is directly related to the DOA of signals, whether the signals are coherent or not; hence, the algorithm does not require any forward/backward spatial smoothing. Second, the two proposed methods can rapidly estimate the 2-D DOAs of incident signals without requiring singular value decomposition (SVD) or eigen value decomposition (EVD), even in the case of coherent signals and a single snapshot The two methods are: (1) orthogonal projection real-valued rank revealing QR factorization (OP-RRRQR), and (2) orthogonal projection real-valued rank revealing LU factorization (OP-RRRLU). The proposed methods reduce computational complexity and the cost at least by a factor of four by applying a unitary transformation, to the complex Toeplitz form to real data without forming the covariance matrix. The proposed algorithms employ the unitary root MUSIC and unitary MUSIC using cross array configuration to estimate the 2-D DOA azimuth and elevation angles without using the extensive 2-D MUSIC search. Hence, they can reduce the computational load and cost significantly and can be applied in real-time radar/sonar and commercial wireless systems. The simulation results show that the proposed algorithms can efficiently estimate the 2-D DOAs from different sources. © 2007 IEEE