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High-Throughput Hyperparameter-Free Sparse Source Location for Massive TDM-MIMO Radar: Algorithm and FPGA Implementation
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DOI：10.1109/TGRS.2023.3323517.png)
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摘要
En 中文
The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input-multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this article presents a subaperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method but also allows for computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on an XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (TX) and 16 receive (RX) antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.
Keyword:
SPICE
Hardware
Manganese
Real-time systems
Sparse matrices
MIMO radar
Field programmable gate arrays
Field-programmable gate array (FPGA)
sparse iterative covariance-based estimation (SPICE)
subaperture-recursive (SAR)
time-division-multiplexing multiple-input--multiple-output (TDM-MIMO)
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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