长时间积累下雷达高速目标检测算法综述

隐身技术与高速飞行器的发展给雷达探测目标带来了严峻挑战,而长时间积累技术就是在信号处理领域帮助雷达进行目标检测时提高信噪比的一种有效方法。对于高速高机动目标而言,信号在长时间累积中会在距离域和多普勒域引起跨距离单元走动和跨多普勒单元走动问题。“两跨”问题会严重影响积累增益,传统的MTD（Moving Target Detection,运动目标检测）方法已经无法对信号进行能量累积。为了解决“两跨”问题,提高雷达对高速目标的探测性能,近些年已涌现很多方法,本文将依据目标运动模型把长时间积累下分为径向匀速运动下的长时间积累、径向加速运动下的长时间积累、径向高阶复杂运动下的长时间积累三类进行分析,对相应模型下的“两跨”问题与解决方法进行综述和总结,意在为后续研究提供参考。

Overview of Radar High-speed Target Detection Algorithms Based on Long Time Accumulation

The development of stealth technology and high-speed aircraft has brought serious challenges to radar target detection, and the long time accumulation technology is an effective method to improve the signal-to-noise ratio (SNR) in the field of signal processing. For high speed and high maneuvering targets, signal accumulation over a long period of time will cause the problem of moving across the range unit and moving across the Doppler unit. The "two-migration" problem will seriously affect the accumulation gain, and the traditional MTD (Moving Target Detection) method has been unable to accumulate the energy of the signal. In order to solve the "two-migration" problem and improve radar detection performance of high-speed targets, many methods have emerged in recent years. Based on the target motion model, this paper will analyze long-term accumulation into three categories: long-term accumulation under uniform radial motion, long-term accumulation under accelerated radial motion, and long-term accumulation under complex radial high-order motion. This paper summarizes and presents the "two-migration" problems and solutions under the corresponding model, aiming to provide reference for subsequent research.