Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy. data analysis, it is rather rare and may be difficult to find because many of HSI-detected targets are relatively small and weak, such as anomalies [10]. Consequently, real-time target detection in HSI presents a great challenge and has become increasingly important.Currently, many algorithms have been developed for target detection in HSI, such as unsupervised fully constrained least squares (UFCLS) [11], automatic target-generation process based on an orthogonal subspace projector (ATGP-OSP) [12], and Reed-Xiaoli (RX) detector [13]. Among them, the performance of ATGP performs well in terms of detection accuracy and computational complexity according to the quantitative and comparative assessments [14,15]. However, despite the fact that automatic target detection has received increasing attention [16][17][18], two problems with real-time onboard applications need to be addressed. One is to continuously increase the scale of operations in the process of automatically iterating to update currently being found targets, which complicates the process of updating operator matrix. The other one is a large-scale matrix m...