# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 1 Introduction  
With their multi-input and multi-output architectures, MIMO radar systems have emerged as a significant development direction in modern radar technology. By integrating MIMO technology with Frequency Modulated Continuous Wave (FMCW) signals, such systems not only enhance the resolution of the radar but also strengthen the target detection performance. The core advantage of MIMO radars lies in their ability to simultaneously detect targets from multiple angles, thereby acquiring more comprehensive target information, including the distance, velocity, and spatial location [1, 2]. This capability endows MIMO radars with prominent application advantages in complex environments, such as urban canyons or dense air traffic. In MIMO radar systems, each antenna serves as an independent data stream generator, which leads to exponential data growth. To handle these massive data volumes, radar systems must possess efficient data processing and storage capabilities [3]. Traditional radar systems might only utilize a single antenna, whereas MIMO radar systems need to process data from multiple antennas, which undoubtedly increases the complexity of the system. The radar system must process and analyze these data in real time or near-real time to achieve rapid target detection and tracking; therefore, how to effectively process and store these large amounts of data while ensuring system performance has become a crucial issue in designing and implementing MIMO radar systems. To address this challenge, researchers have proposed various solutions spanning multiple aspects, including hardware design, algorithm optimization, and resource management strategies. In terms of hardware design, by employing high-performance hardware components, such as a Digital Signal Processor (DSP) [4], FPGA [5] and a Graphics Processing Unit (GPU) [6], the speed of signal processing can be significantly enhanced. This high-performance hardware can rapidly process vast amounts of data, meeting the system's real-time performance requirements [7; 8]. However, utilizing such hardware also introduces issues such as increased costs, heat dissipation problems, and difficulties in physical size management [9], all of which may limit the application and dissemination of the system. In terms of algorithm optimization, techniques such as compressive sensing, data sparsification, and improved FFT algorithms have been proposed to reduce the demand for computational resources. These techniques effectively reduce the amount of data to be processed, alleviating the burden on hardware and improving system operating efficiency. However, the optimization and implementation of these algorithms often increase the complexity of the system, requiring elaborate design and maintenance. Moreover, their performance usually depends on specific operating conditions and system configurations [10; 11]. Modern technologies such as compressed sensing and deep learning have demonstrated higher efficiency or lower computational load in certain specific scenarios. However, the classic FFT architecture remains widely used due to its long-standing engineering application experience and well-established hardware implementations. Engineers have conducted extensive optimizations on this architecture, particularly in memory management and efficient utilization of storage bandwidth. These studies rely on a two-storage structure, where Range FFT data and Doppler FFT data are stored separately [12; 13]. In this study, we refer to this architecture as the conventional engineering implementation architecture. In this paper, we aim to enhance storage efficiency and processing speed for MIMO radar systems by modifying the engineering implementation architecture, without relying on expensive hardware upgrades or complex algorithmic tuning. We analyze the limitations of the conventional architecture and propose an efficient dynamic engineering implementation architecture.