Processing math: 100%
Hardware–Algorithm Codesigned Low-Latency and Resource-Efficient OMP Accelerator for DOA Estimation on FPGA | IEEE Journals & Magazine | IEEE Xplore

Hardware–Algorithm Codesigned Low-Latency and Resource-Efficient OMP Accelerator for DOA Estimation on FPGA


Abstract:

This article introduces an algorithm-hardware codesign optimized for low-latency and resource-efficient direction-of-arrival (DOA) estimation, employing a refined orthogo...Show More

Abstract:

This article introduces an algorithm-hardware codesign optimized for low-latency and resource-efficient direction-of-arrival (DOA) estimation, employing a refined orthogonal matching pursuit (OMP) algorithm adept at handling the complexities of multisource detection, particularly in scenarios with closely spaced signal sources. At the algorithmic level, this approach incorporates a secondary correction mechanism (SCM) into the traditional OMP algorithm, significantly improving estimation accuracy and robustness. On the hardware front, a bespoke OMP accelerator has been developed, featuring a reconfigurable generic processing element (PE) array that supports various computational modes and leverages multilevel spectral peak search strategy and pipelining techniques to enhance computational efficiency. Experimental evaluations reveal that the proposed system achieves a root mean square error (RMSE) for DOA estimation of less than 0.3° in multisource conditions with a signal-to-noise ratio (SNR) of 20 dB. In addition, the deployment of the OMP accelerator on a Zynq XC7Z020 development board utilizes modest logic resources: 5.49k LUTs, 3.28k FFs, 11.5 BRAMs, and 32 DSPs. Furthermore, the design achieves a computational latency of 2.83~\mu \text { s} for single-source estimation with eight antennas. This achievement reflects a reduction of approximately 17.8% in LUTs, 56.3% in FFs, and 5.7% in DSPs compared to current leading-edge technologies after normalization all while maintaining competitive estimation accuracy and favorable estimation rates.
Page(s): 421 - 434
Date of Publication: 26 September 2024

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