Study on the Data Storage Technology of Mini-Airborne Radar Based on Machine Learning

TL;DR

This paper introduces a machine learning-based data storage method for mini-airborne radar that improves file management efficiency and adapts to various data rates, addressing the limitations of traditional storage systems.

Contribution

It proposes a novel machine learning model for optimizing data storage and file management in mini-airborne radar systems, enhancing speed and adaptability.

Findings

Reduced file management time significantly.

Adaptable storage methods for different data rates.

Validated performance improvements through experiments.

Abstract

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.