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Article(id=1241060184131432882, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1241060178263601474, articleNumber=null, orderNo=null, doi=10.12347/j.ycyk.20231205001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1701705600000, receivedDateStr=2023-12-05, revisedDate=1703865600000, revisedDateStr=2023-12-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1773821405679, onlineDateStr=2026-03-18, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773821405679, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773821405679, creator=13701087609, updateTime=1773821405679, updator=13701087609, issue=Issue{id=1241060178263601474, tenantId=1146029695717560320, journalId=1238841944844054536, year='2024', volume='45', issue='2', pageStart='1', pageEnd='123', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773821404281, creator=13701087609, updateTime=1773821891324, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241062221128724613, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1241060178263601474, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241062221128724614, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1241060178263601474, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=89, endPage=98, ext={EN=ArticleExt(id=1241060184399868350, articleId=1241060184131432882, tenantId=1146029695717560320, journalId=1238841944844054536, language=EN, title=Overview of Radar High-speed Target Detection Algorithms Based on Long Time Accumulation, columnId=1239133500033528732, journalTitle=Journal of Telemetry, Tracking and Command, columnName=Radar and Countermeasures, runingTitle=null, highlight=null, articleAbstract=

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.

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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。

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尚尚（）
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徐环 1999年生，硕士。

尚尚 1982年生，博士，副教授，硕士生导师。

石依山 1999年生，硕士。

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徐环 1999年生，硕士。

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徐环 1999年生，硕士。

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尚尚 1982年生，博士，副教授，硕士生导师。

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尚尚 1982年生，博士，副教授，硕士生导师。

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石依山 1999年生，硕士。

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石依山 1999年生，硕士。

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长时间积累下雷达高速目标检测算法综述
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徐环 , 尚尚 , 石依山
遥测遥控 | 雷达与对抗 2024,45(2): 89-98
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遥测遥控 | 雷达与对抗 2024, 45(2): 89-98
长时间积累下雷达高速目标检测算法综述
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徐环, 尚尚, 石依山
作者信息
  • 江苏科技大学　海洋学院　镇江　212000

    • 徐环 1999年生，硕士。

    尚尚 1982年生，博士，副教授，硕士生导师。

    石依山 1999年生，硕士。

通讯作者:

Overview of Radar High-speed Target Detection Algorithms Based on Long Time Accumulation
Huan XU, Shang SHANG, Yishan SHI
Affiliations
  • Jiangsu University of Science and Technology, Zhenjiang Ocean College, 212000, China
doi: 10.12347/j.ycyk.20231205001
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摘要
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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。

长时间积累  /  距离走动  /  多普勒走动  /  目标检测

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.

Long-term accumulation  /  Range migration  /  Doppler frequency migration  /  Object detection
徐环, 尚尚, 石依山. 长时间积累下雷达高速目标检测算法综述. 遥测遥控, 2024 , 45 (2) : 89 -98 . DOI: 10.12347/j.ycyk.20231205001
Huan XU, Shang SHANG, Yishan SHI. Overview of Radar High-speed Target Detection Algorithms Based on Long Time Accumulation[J]. Journal of Telemetry, Tracking and Command, 2024 , 45 (2) : 89 -98 . DOI: 10.12347/j.ycyk.20231205001
正文
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0　引言
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在雷达目标检测中，根据传统定义，“弱目标”指的是回波淹没在杂波中的低信噪比的目标[1]。随着科技水平的提高，“弱目标”的定义也越来越广泛，概括起来，可以包含以下几类[2]：① 小尺寸目标，目标的雷达散射截面积(Radar Cross Section，RCS)较小，导致回波能量微弱；② 目标的尺寸可能并不小，但由于采用隐身技术，使得回波能量微弱；③ 处于强干扰或强噪声下，信杂比低的目标；④ 远距离目标，远距离引起电磁波强烈衰减，雷达接收回波强度小；⑤ 高速高机动目标，在雷达观测期间，目标的高速运动使其位置超出雷达可观测的最小距离单元，同时目标的高机动性也会在多普勒域造成频谱展宽，造成回波能量难以积累，因此也是“弱目标”。对“弱目标”而言，传统的信号处理方法已经难以有效积累此类目标的能量，无法实现目标检测的目的。
长期以来，国内外在“弱目标”检测方面进行了深入研究，涉及检测跟踪[3]、变换域检测[4]、微多普勒分析[5]以及杂波处理[6]等多个领域，并取得了丰富的研究成果。尽管如此，传统雷达在“弱目标”检测方面仍然存在一些问题：① 复杂的杂波环境增加了信号分析的难度，为“弱目标”探测研究带来了挑战；② 复杂的目标特性降低了检测效率与性能，给检测带来了较大的困难。
为了解决“弱目标”检测难题，提高雷达检测能力，脉冲雷达在凝视工作模式下可以通过多次发射脉冲并对回波进行累积处理以达到提高信噪比，实现对高速高机动目标检测的目的。脉冲积累的时长，一般取决于以下几个因素：① 目标的距离与速度，远距离目标与慢速目标需要更长的累积时间来获得足够大的信噪比；② 雷达系统的灵敏度与噪声系数，低灵敏度与高噪声系数的雷达系统可能需要更多的累积脉冲来弥补系统缺陷以实现检测；③ 目标的散射截面积，截面积越小的目标信噪比越低，所以需要更多的累积时长。④ 系统设计与性能要求，不同的设计需求也会影响累积时长。另外，对于累积的回波脉冲，根据是否利用回波的相位信息，长时间积累又分为相参的长时间积累[7]和非相参的长时间积累[8]。相比于非相参积累方式，相参积累可以获得更高的信噪比，并且积累增益随着积累时间正相关增长，但在积累时间内，会出现复杂距离走动和多普勒走动现象，影响积累效果，这也是长时间积累技术所要面临的问题。
针对长时间积累技术带来的问题，本文依据目标径向运动模型，把近年来解决“两跨”问题的方法分为径向匀速运动下、径向加速运动下、高阶复杂运动下三类，并对每一类进行综述、分析和总结，意在为后续通过长时间积累技术提高目标检测能力提供思路和参考。
1　“两跨”问题分析
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在脉冲积累提高信噪比时，目标的高速运动会使积累出现“两跨”问题。在距离域，脉压后回波信号的包络出现在不同的距离单元，发生了跨距离单元走动(Range Migration，RM)；在多普勒域，展宽的频谱引起了跨多普勒单元走动问题(Doppler Frequency Migration，DFM)。“两跨”问题产生的原因不同，需要具体分析。
1.1　距离走动分析
目标的回波信号建模是检测算法设计的前提，而回波信号的建模需要分析目标的运动模型及系统采用的信号波形。以发射线性调频信号(Linear Frequency Modulation，LFM)为例，脉冲积累后的回波信号为：
式中，A1为脉压后的信号幅度，为脉内快时间，tm为脉间慢时间，B为发射信号的带宽，fc为发射信号的载频，f为沿t̂做傅里叶变换后的频率，c为光速，Rm(tm)为目标的运动多项式。对于三种不同的运动模型，Rm(tm)可以分为三类：
其中，R0为初始距离，v、a、ai分别是目标的径向速度、径向加速度和高阶径向机动度。
由式(1)可知，对于脉压后的信号，目标速度增加以及积累时间增长会导致回波的包络不在一个距离单元内。假设距离门为c/2B，Tp是脉冲重复周期，根据积累期间目标的运动是否超出一个距离单元，将距离走动问题分由速度引起的线性距离走动、由加速度和高阶项引起的距离弯曲(高阶运动状态下跨距离单元及多普勒单元的原理与匀加速模型下相同，本文不分开讨论)，即：
① 2vMTp≤c/2B Þv≤c/4MTpB时，没有产生距离走动，为常规运动目标；
② 2vMTp>c/2B且a(MTp)2≤c/2BÞv>c/4MTpB且a≤c/2B(MTp)2时，只产生距离走动；
③ 2vMTp≤c/2B且a(MTp)2>c/2BÞv≤c/4MTpB且a>c/2B(MTp)2时，只产生距离弯曲；
④ 2vMTp>c/2B且a(MTp)2>c/2BÞv>c/4MTpB且a>c/2B(MTp)2时，既产生距离走动也产生距离弯曲；距离走动分析如图1所示。
1.2　多普勒走动分析
脉冲雷达利用多普勒效应进行测速，跨多普勒单元走动会出现在目标非匀速运动情况下。由于速度的微小变化可以引起相位的急剧变化，尽管加速度在距离域未引起距离走动，但在多普勒域已经使频谱展宽，走动超出多普勒单元，引起了跨多普勒单元走动问题。
对(1)式沿快时间做FFT，得到回波信号在频域的形式为：
其中，A2是变换后的幅度，f是对应的距离频域。目标运动的复杂性使得信号在慢时间维度是非线性的，式(2)中的指数表示目标的线性频移的多普勒频率为：
已知一个多普勒单元大小为1/MTp，根据目标的多普勒相位是否跨多普勒单元来判断是否产生了多普勒走动问题，即：
①且λ/4(MTp)2且g≤λ/2(MTp)3时，未产生多普勒走动；
②或λ/4(MTp)2或g>λ/2(MTp)3时，产生了多普勒走动；多普勒走动如图2所示。
综上所述，根据目标是否产生距离走动和多普勒走动，目标的检测方法处理流程如图3所示。
2　方法分类
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2.1　径向匀速运动下的长时间积累算法
对于匀速运动状态下的目标而言，主要考虑由径向速度引起的距离走动问题。具体方法有速度搜索补偿法、一阶Keystone算法与RFT(Radon Flourier Transform)算法。
速度搜索补偿法：脉压后的回波信号包络因分散在不同的距离单元内而导致积累能量分散，无法很好的累积。速度搜索补偿法的思想就是通过对速度进行搜索，在时域对每次回波信号进行2vmTp/c距离的移位或在频域构建的滤波器对每次回波进行相位相消实现相位解耦，从而消除距离走动。速度搜索补偿具体的实现方法为在时域的包络插值移位法[9]和在频域的频域校正补偿法[10,11]。两种方法中，基于频域的相位补偿运算量更小，但是这两种方法需要对速度进行检索，在不知道速度的情况下，不可避免地增加了算法的时间复杂度，并且算法在低信噪比的环境下效果并不理想。
Keystone方法[12]： Keystone变换本质上就是通过对原始回波数据进行快时间频率的重采样处理，从而实现距离修正。Keystone方法在被用于距离走动补偿后，便很快被应用在脉冲多普勒雷达中[13]，并且它可以在速度信息未知的条件下实现距离走动修正。但是对于低脉冲重复频率雷达而言，较低的脉冲重复频率也会导致速度模糊，在这种情况下，通过Keystone算法修正距离走动则需要目标的多普勒模糊数信息。因此，Keystone算法也与搜索多普勒模糊数的方法结合起来实现对目标的距离校正与积累[14]。在具体实现Keystone算法时[15]，有sinc函数插值法、DFT(离散傅里叶变换)-IFFT(逆快速傅里叶变换)法、CZT(Chirp-Z Transform，线性调频Z变换)法和CZT-IFFT法[16]，其中CZT-IFFT方法的运算量最小。基于一阶Keystone算法的距离修正效果如图4所示。
RFT算法：RFT算法是一种搜索积分类算法。在文献[17]中，首次提出了基于Radon-Fourier变换的方法来实现雷达回波脉冲积累。RFT算法通过联合搜索距离与径向速度以建立一个包含目标距离与速度的参数空间，并通过积分将回波脉冲投影到此空间形成一个“点”，最亮的“点”则对应着目标的参数信息。然而，RFT中对速度与距离的二维联合搜索积分，极大地增加了运算量与时间，并且RFT也会引起了盲速旁瓣的问题。为了解决RFT搜索量大的弊端，有基于CZT的快速RFT算法[18]和搜索残差速度的方法。除此之外，还有基于快时间频域非均匀FFT与变系数DFT来实现RFT[19]。RFT算法积累效果如图5所示。
除此之外，也有一些不同于Keystone与RFT的算法被提出用于距离走动修正。比如文献[20]提出了一种时频翻转(Time Reverse Frequency Reverse，TRFR)的方法来实现距离修正。该方法无需多普勒信息和参数搜索就可以快速实现能量积累，方法简单。文献[21]中提出了一种NAR-MTD(New Axis Rotates-Moving Target Detection)方法，NAR-MTD首先选择二维压缩信号的图像中心作为坐标系原点，然后简化轴旋转关系，以减少坐标变换引起的旋转误差，并将距离走动校正到初始范围位置。虽然这些方法实现了距离修正，但在低信噪比背景下效果不理想并且适用性较差。
2.2　径向匀变速运动下的长时间积累算法
对径向匀变速的运动目标，需要考虑其在脉冲积累后出现的距离走动、距离弯曲与多普勒走动问题，现有的算法大体可以分为补偿类和搜索积分类，也就是二阶Keystone[22,23](Second order Keystone Transform，SKT)和RFT及其改进算法。
对补偿类算法而言，二阶Keystone变换可以通过对信号的重采样实现加速度a与距离频域f的解耦合来消除距离弯曲。但是二阶Keystone算法无法修正多普勒走动，并且由速度v引起的距离走动也未消除，只是走动量为原来的一半[24]。因此，二阶Keystone变换常与其他方法结合以解决匀变速目标的脉冲积累问题。在修正多普勒走动方面，主要的思路是通过估计加速度构建补偿相位进行校正[22]，实现的方法有通过Dechirp方法构建补偿相位[25]和投影到变换域的方法进行参数估计，如FrFT[26,27](Fractional Fourier Transform)、LVD[28](Lv's Distribution)、DCFT[29-31](Discrete Chirp-Fourier Transform)等。文献[31]中将位置旋转变换(Modified Location Rotation Transform，MLRT)与IDCFT相结合实现信号积累，对脉压信号做MLRT来校正残差线性距离偏移，并通过IDCFT实现相干积分和运动参数估计，但是该方法忽略了由加速度引起的距离弯曲。在文献[32]中，作者提出了一种改进LVD方法来累积能量。首先通过LVD准确估计加速度，然后引入周期离散傅里叶变换(DFT)以修改LVD来估计无模糊质心频率，最后采用缩放和乘积运算来消除耦合并抑制光栅波瓣。另外，还有将二阶Keystone变换、修正坐标轴旋转(Modified Axis Rotate，MAR)与线性正则变换(Linear Canonical Transform，LCT)的方法结合[33]来实现目标检测和运动参数估计，该算法利用SKT消除距离曲率，然后通过MAR校正线性距离走动。最后将目标能量集中在一个距离单元中后，提取信号并通过LCT完成能量积分。基于二阶Keystone变换的算法虽然实现简单，但是对于速度模糊的目标也同样需要先验多普勒模糊数信息，而估计多普勒模糊数的过程会增加计算时间，降低效率。
对积分搜索类算法，一般为改进的RFT及其方法，其思想就是利用RFT向高维空间扩展，如二阶RFT[34]、RFRFT[35] (Radon-Fractional Ambiguity Function，RFRAF)。文献[27]、[36]中借助RFT和MTD算法的思想，通过RFT算法搜索得到目标的距离与速度信息之后，借助FrFT实现径向匀加速运动目标的长时间相参积累。文献[37]提出了一种结合脉冲压缩和Radon分数傅里叶变换(Pulse Compression and Radon Fractional Fourier Transform，PC-RFRFT)的长时间相干积分方法，它利用信号之间的相关性对脉冲内多普勒频移、脉冲间多普勒频移和加速度同步进行补偿。文献[38]中提出了一种基于模广义Radon-Fourier变换的相干积分方法，不仅校正了跨距离单元走动和跨多普勒单元走动，还解决了距离模糊问题。文献[39]中提出了一种尺度Radon-Fourier变换(Scaled Radon Fourier Transform ，SCRFT)来解决跨单元中的尺度效应问题。基于RFT变换的算法可以有效地解决在脉冲积累时出现的各种问题，但是RFT类算法不可避免地面临算法搜索量大、时间成本高的问题。
除了上述两类方法，还有一些将Keystone与RFT结合的方法，比如，在文献[39]中，先对回波在距离维度做二阶Keystone 变换，接着借助Dechirp 方法估计目标的径向加速度并进行相位补偿，最后通过RFT算法对目标进行积累。文献[40]中也提出了基于二阶Keystone变换与改进分数阶Radon 变换(Modified Fractional Radon Transform, MFRRT)的目标检测方法来实现目标检测。总之，二阶Keystone类和改进RFT类算法虽然解决了匀变速目标的长时间积累问题，但是它们仍然具有局限性，这是因为在现实情况下，目标往往不是以匀速或者匀变速状态运动，而是以更复杂的状态运动，此时，基于匀速与匀变速状态的跨距离单元与跨多普勒单元修正算法已不再适用。一种基于SKT与RFT结合的目标检测算法的积累效果如图6所示。
2.3　径向高阶复杂运动状态下的长时间积累算法
相比于匀速运动或匀变速运动目标，此类方法针对的是运动形式更为复杂的长时间积累检测问题，需要解决的是更复杂的距离弯曲、距离走动与多普勒走动。一般来说，这类算法分为两类。
一类是基于参数搜索的算法，其中最为经典的就是广义RFT算法(Generalized Radon-Fourier Transform, GRFT)[41]。GRFT通过对目标进行高维度的联合搜索积分来实现对目标参数的估计，虽然它可以在低信噪比的环境下表现出优越的性能，但是随着目标机动性的提高，GRFT算法的搜索复杂度也在同比增长，巨大的计算复杂度大大降低了其实用性。随后，许稼等人又提出了基于粒子群的算法来快速实现GRFT以减小计算量[42]，虽然这个方法可以在一定程度上减小了搜索复杂度，但是由于对初始参数设置敏感，容易陷入局部最优。基于GRFT方法的长时间积累如图7所示。
另一种为降阶类算法。相比于搜索类算法，降阶类算法不需要遍历搜索来确定目标参数，极大减少了算法的计算量，比如ACCF(Adjacent Cross Correlation Function, 相邻互相关函数)[43]、SAF-SFT(Symmetric Autocorrelation Function-Scaled Fourier Transform，对称自相关函数-变尺度傅里叶变换)[44]和TDST(Three-Dimensional Scaled Transform, 三维缩放变换函数)[45]等，此类方法通过变换操作，保留了目标运动系数的同时降低运动阶数以达到目标检测的目的，是一种以一定的抗噪声性能损失为代价获得了较低的计算复杂度的方法。在文献[46]、[47]中，作者提出了一种ACCF-LVD的方法，该方法通过采用相邻相关运算来实现包络对齐并降低多普勒走动的阶数，然后应用LVD实现相干积分和参数估计。而在文献[48]中，作者提出了一种修正LVD(Modified Lv's Distribution, MLVD)方法来估计目标参数，修正的LVD方法可以估计模糊速度，但是这种方法无法估计加速度与精确的三阶速度。之后，有学者在MLVD的基础上提出了乘积缩放周期修正LV分布(Product-Scaling Periodic-Modified Lv's Distribution, PSPMLVD)[49]，它与逆缩放周期离散傅里叶变换(Inverse-scaling Periodic Discrete Fourier Transformation，ISPDFT)相结合，不仅扩展了估计范围，还抑制了旁瓣，提高了算法在低信噪比环境下的检测性能和检测精度。另外，文献[50]中也提出了一种基于全距离带宽反转函数(Full-Range Bandwidth Reversal Function，FRBRF)和缩放积分三线性自相关函数(Scaled Integrated Trilinear Autocorrelation Function，SITAF)的方法，该方法能够在计算成本、抗噪声性能和检测能力之间取得良好的平衡。基于ACCF-LVD方法进行目标检测的效果如图8所示。
总之，尽管学者们已经提出了各种方法来解决高机动目标长时间积累中产生的问题，但这些方法仍存在一些缺陷。比如当目标运动状态阶数升高时，RFT类算法的运算量急剧增加，而类似于LVD，通过在变换域处理信号来进行参数估计的方法也不再适用。
综上所述，对脉冲雷达进行脉冲积累过程中出现的问题，目前已有许多研究，并取得了较好的成果，但仍然存在一些难题需要进一步研究，例如：如何解决非径向运动条件下高速复杂目标的检测问题。由于高速目标没有足够的真实探测数据，如何解决算法在真实目标检测中的可行性问题，如何降低算法的复杂度，减轻运算量，提高算法效率等。
3　结束语
收起
长时间积累技术是提高信噪比的一种有效方法。本文根据目标运动模型，对解决脉冲积累中产生的“两跨”问题的方法进行分类，分析和总结了各类方法的特点。同时，文中还指出了现存方法的一些不足之处：Keystone算法虽然可以在速度未知的条件下对目标进行距离校正，但是对速度模糊的目标需要知道多普勒模糊数，而且Keystone类算法无法修正跨多普勒单元的问题，无法应用于高阶复杂运动目的的检测。RFT类算法需要对参数进行搜索，尽管有一些方法可以降低搜索量，但运算量仍然较大，而且RFT方法的盲速旁瓣问题也需要解决。其他方法如时频分析法、Dechirp法等，也存在算法复杂度高、估计不精确的不足。以上这些难点都需要广大学者不断研究与实验，努力突破。
基金
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  • 国家自然科学基金项目(61801196)
  • 江苏省研究生科研与实践创新计划项目(SJCX23_2138)
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2024年第45卷第2期
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doi: 10.12347/j.ycyk.20231205001
  • 接收时间：2023-12-05
  • 首发时间：2026-03-18
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  • 收稿日期：2023-12-05
  • 修回日期：2023-12-30
基金
国家自然科学基金项目(61801196)
江苏省研究生科研与实践创新计划项目(SJCX23_2138)
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    江苏科技大学　海洋学院　镇江　212000

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