Study on the Data Storage Technology of Mini-Airborne Radar Based on Machine Learning
This addresses a domain-specific bottleneck for mini-airborne radar systems, offering an incremental improvement in storage efficiency.
The paper tackled the problem of long file management time in mini-airborne radar data storage systems, which limits data storage speed, by proposing a machine learning-based method that adapts to different data rates and scenarios, resulting in an extremely low ratio of file management time to actual data writing time.
The data rate of airborne radar is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the radar data. Data storage systems with good seismic performance usually use NAND Flash as storage medium, and there is a widespread problem of long file management time, which seriously affects the data storage speed, especially under the limitation of platform miniaturization. To solve this problem, a data storage method based on machine learning is proposed for mini-airborne radar. The storage training model is established based on machine learning, and could process various kinds of radar data. The file management methods are classified and determined using the model, and then are applied to the storage of radar data. To verify the performance of the proposed method, a test was carried out on the data storage system of a mini-airborne radar. The experimental results show that the method based on machine learning can form various data storage methods adapted to different data rates and application scenarios. The ratio of the file management time to the actual data writing time is extremely low.