A Real-Time FPGA Implementation of the LCMV Algorithm for Target Classification in Hyperspectral Images Using LDL Decomposition

Since the advent of air and space-borne flight, remotely sensed images of the Earth’s surface have changed how the world is perceived: meteorology, navigation, surveillance, and environmental sciences are some of the areas, in which technology has fundamentally changed. Hyperspectral images extend the possibilities even further by capturing wavelengths outside the visible spectrum. This is particularly valuable for target detection, often performed using drones, satellites, or other unmanned aerial vehicles (UAVs). In many applications such as surveillance and security, it is critical to perform this detection in real-time. Response times can be optimized by performing these calculations on-board, which introduces additional power, radiation tolerance, and resource constraints. To that end, embedded devices such as field-programmable gate arrays (FPGAs) are ideal candidates, as their reprogrammable logic allows for a great degree of acceleration while having low power consumption. In this article, a real-time FPGA implementation of the linearly constrained minimum variance (LCMV) algorithm for target classification is presented. First, an analysis is performed to explore the most parallelizable and accurate numerical methods that fit within the resource constraints of FPGAs, and finally selecting LDL decomposition. Then, hardware modules for LCMV classification are designed in SystemVerilog, focusing on incremental correlation matrix computation and a solver for LDL matrix decomposition. The proposed implementation on a radiation-tolerant Xilinx XQRKU060 is compared to an embedded CUDA-accelerated graphics processing unit (GPU) and an embedded central processing unit (CPU), showing turnaround time improvements of
$7.25\times $
and
$825\times $
, respectively. It runs
$15\times $
faster than real-time, with sufficient margin to accommodate the next generation of hyperspectral sensors.