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

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

High-resolution Direction-of-Arrival estimation

Direction of Arrival (DOA) estimation is considered one of the most crucial problems in array signal processing, with considerable research efforts for developing efficient and effective direction-finding algorithms, especially in the transportation industry, where the demand for an effective, real-time, and accurate DOA algorithm is increasing. However, challenges must be addressed before real-world deployment can be realised. Firstly, there is the requirement for fast computational time for real-time detection. Secondly, there is a demand for high-resolution and accurate DOA estimation. In this thesis, two state-of-the-art DOA estimation algorithms are proposed and evaluated to address the challenges. Firstly, a novel covariance matrix reconstruction approach for single snapshot DOA estimation (CbSS) was proposed. CbSS was developed by exploiting the relationship between the theoretical and sample covariance matrices to reduce estimation error for a single snapshot scenario. CbSS can resolve accurate DOAs without requiring lengthy peak searching computational time by computationally changing the received sample covariance matrix. Simulation results have verified that the CbSS technique yields the highest DOA estimation accuracy by up to 25.5% compared to existing methods such as root-MUSIC and the Partial Relaxation approach. Furthermore, CbSS presents negligible bias when compared to the existing techniques in a wide range of scenarios, such as in multiple uncorrelated and coherent signal source environments. Secondly, an adaptive diagonal-loading technique was proposed to improve DOA estimation accuracy without requiring a high computational load by integrating a modified novel and adaptive diagonal-loading method (DLT-DOA) to further improve estimation accuracy. An in-depth simulation performance analysis was conducted to address the challenges, with a comparison against existing state-of-the-art DOA estimation techniques such as EPUMA and MODEX. Simulation results verify that the DLT-DOA technique performs up to 8.5% higher DOA estimation performance in terms of estimation accuracy compared to existing methods with significantly lower computational time. On this basis, the two novel DOA estimation techniques are recommended for usage in real-world scenarios where fast computational time and high estimation accuracy are expected. Further research is needed to identify other factors that could further optimize the algorithms to meet different demands