WO2018014166A1 - 一种波束-多普勒通道自适应选择stap方法 - Google Patents

一种波束-多普勒通道自适应选择stap方法 Download PDF

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WO2018014166A1
WO2018014166A1 PCT/CN2016/090334 CN2016090334W WO2018014166A1 WO 2018014166 A1 WO2018014166 A1 WO 2018014166A1 CN 2016090334 W CN2016090334 W CN 2016090334W WO 2018014166 A1 WO2018014166 A1 WO 2018014166A1
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doppler
target
filter
space
time
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阳召成
朱轶昂
黄建军
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深圳大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target

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  • the invention relates to the field of radar signal processing, and in particular to a beam-Doppler channel adaptive selection STAP method.
  • STAP Space-time adaptive processing
  • auxiliary channel receiver ACR
  • JDL joint domain localized
  • STMB space-time multiple-beam
  • the object of the present invention is to provide a beam-Doppler channel adaptive selection STAP method, which aims to solve the beam-Doppler channel fixation existing in the existing beam-Doppler STAP technology, by array error The problem of clutter suppression and target detection performance degradation caused by actual factors.
  • the invention provides a beam-Doppler channel adaptive selection STAP method, which mainly comprises:
  • Data transformation step transforming array-pulse dimension sampling data into beam-Doppler domain data
  • Filter design steps By introducing sparse constraints, the space-time filter weight vector design problem is transformed into a sparse representation problem, and the filter weight vector is obtained by solving the sparse representation problem;
  • Target detection step constructing an adaptive matched filter detector by using the filter weight vector to implement clutter suppression and effective target detection.
  • the data transformation step specifically includes:
  • the filter design step specifically includes:
  • the space-time filter weight vector design problem is transformed into a sparse representation problem by introducing sparse constraints to space-time filter weight vectors.
  • d [d 1 , d 2 ,...,d L ] T
  • L is the total number of snapshots
  • p is the l p norm.
  • the target detecting step specifically includes:
  • An adaptive matched filter detector is constructed by using the filter weight vector, and the filter detector is used to implement clutter suppression and effective detection of the target.
  • the technical solution provided by the invention can realize the separation of the clutter subspace by using (1) the beam-Doppler domain sampling data and (2) the clutter subspace of the single beam-Doppler channel in the beam-Doppler domain.
  • the dimension is much smaller than the system freedom.
  • the array-pulse dimension sampling is transformed into beam-Doppler domain data.
  • the space-time filter weight vector design problem is transformed into a sparse representation problem by introducing sparse constraints.
  • the sparse representation problem is obtained by the filter weight vector, and then the filter weight vector is obtained by solving the sparse representation problem and the target detector is designed.
  • the clutter suppression and the target detection are performed. It can effectively suppress clutter when the filter training samples are limited.
  • the beam-Doppler channel can be adaptively selected to overcome the actual factors caused by array errors.
  • the performance degradation problem improves clutter suppression and target detection performance.
  • FIG. 1 is a flowchart of a method for adaptively selecting a STAP for a beam-Doppler channel according to an embodiment of the present invention
  • FIG. 2 is a diagram showing relationship between SCNR loss and number of training samples according to an embodiment of the present invention
  • 3 is a diagram showing relationship between SCNR loss and different target Doppler frequencies in an embodiment of the present invention
  • FIG. 4 is a diagram showing the relationship between detection probability and SCNR according to an embodiment of the present invention.
  • the invention is used in the field of radar signal processing, and provides a space-time adaptive processing (STAP) method based on sparse constraint-based beam-Doppler channel, which converts array-pulse dimension sampling into In the beam-Doppler domain data, the space-time filter weight vector design problem is transformed into a sparse representation problem by introducing sparse constraints. Then the filter weight vector is obtained by solving the sparse representation problem and the target detector is designed. Finally, clutter suppression and target detection are performed. It can effectively suppress clutter when the filter training samples are limited. Compared with the traditional beam-Doppler channel fixed STAP method, the beam-Doppler channel can be adaptively selected to overcome the actual factors caused by array errors. The performance degradation problem improves clutter suppression and target detection performance.
  • STAP space-time adaptive processing
  • a beam-Doppler channel adaptive selection STAP method provided by the present invention will be described in detail below.
  • FIG. 1 is a flowchart of a method for adaptively selecting a STAP for a beam-Doppler channel according to an embodiment of the present invention.
  • step S1 the data transformation step: transforming the array-pulse dimension sample data into beam-Doppler domain data.
  • the separation of the clutter subspace can be achieved by using the beam-Doppler domain sampling data.
  • a pulse Doppler positive side view airborne radar antenna is a uniform linear array comprising M receiving array elements, the radar transmitting N pulses in a coherent processing unit, wherein the data transformation Step S1 specifically includes:
  • f s,t , f d,t are the target airspace beam frequency and time domain beam frequency, respectively, and their corresponding target space-time steering vectors are
  • v s (f s,i ) [1,exp(j2 ⁇ f s,j ),...,exp(j2 ⁇ (N-1)f s , j )] T .
  • step S2 the filter design step: converts the space-time filter weight vector design problem into a sparse representation problem by introducing a sparse constraint, and obtains a filter weight vector by solving the sparse representation problem.
  • the dimension of the clutter subspace of a single beam-Doppler channel is much smaller than the idea of system degrees of freedom.
  • the filter design step S2 specifically includes:
  • the space-time filter weight vector design problem is transformed into a sparse representation problem by introducing sparse constraints to space-time filter weight vectors.
  • d [d 1 , d 2 ,...,d L ] T
  • L is the total number of snapshots
  • p is the l p norm.
  • a sparse representation algorithm in the filter design step is solved using a sparse recovery algorithm to obtain a filter weight vector and pass the filter weight vector.
  • a sparse recovery algorithm (such as FOCUSS algorithm) is used to solve the sparse representation problem in the problem transformation step S2.
  • the FOCUSS algorithm knows that the solution can be divided into two steps:
  • step S3 the target detecting step constructs an adaptive matched filter detector by using the filter weight vector to implement clutter suppression and target effective detection.
  • the target detecting step S3 specifically includes:
  • An adaptive matched filter detector is constructed by using the filter weight vector, and the filter detector is used to implement clutter suppression and effective detection of the target.
  • a reference adaptive adaptive filter (AMF) method is adopted to design the detector as Where ⁇ is the detection threshold, ⁇ is a positive constant factor, H 0 means no target, and H 1 means the target appears.
  • AMF adaptive adaptive filter
  • the invention provides a beam-Doppler channel adaptive selection STAP method, which transforms array-pulse dimension sampling into beam-Doppler domain data, and converts the space-time filter weight vector design problem into a sparse constraint into The problem of sparse representation is obtained by solving the sparse representation problem and obtaining the filter weight vector and designing the target detector. Finally, clutter suppression and target detection are performed. It can effectively suppress clutter when the filter training samples are limited. Compared with the traditional beam-Doppler channel fixed STAP method, the beam-Doppler channel can be adaptively selected to overcome the actual factors caused by array errors. The performance degradation problem improves clutter suppression and target detection performance.
  • the present invention (SCBDS-STAP) has faster convergence than the JDL, STMB, and Sparsity-aware beamformer methods, and is close to optimal output performance, as shown in FIG.
  • the present invention is for a smaller target Doppler frequency in the case of considering whether or not there is an error, respectively.
  • SCBDS-STAP is superior to other algorithms (such as JDL, STMB, sparse filter, etc.) in output performance, that is, the present invention (SCBDS-STAP) is more suitable for detecting low-speed moving targets, as shown in FIG.
  • the detection probability (PD) of the present invention (SCBDS-STAP) for the target is higher than that of the other three methods (such as JDL, STMB, and sparse filter method), as shown in FIG. 4, respectively.
  • the invention converts the array-pulse dimension sampling into beam-Doppler domain data, and introduces the sparse constraint to transform the space-time filter weight vector design problem into a sparse representation problem, and obtains the filter weight by solving the sparse representation problem.
  • Vector then obtain the filter weight vector by solving the sparse representation problem and design the target detector, then perform clutter suppression and target detection.
  • the invention can be applied to the field of motion platform radar clutter suppression and moving target detection to improve the radar system's clutter suppression level and target detection capability.
  • each unit included is only divided according to functional logic, but is not limited to the above division, as long as the corresponding function can be implemented; in addition, the specific name of each functional unit is also They are only used to facilitate mutual differentiation and are not intended to limit the scope of the present invention.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
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  • Radar Systems Or Details Thereof (AREA)

Abstract

一种波束-多普勒通道自适应选择STAP方法，其中，该方法包括：数据变换步骤（S1），滤波器设计步骤（S2）以及目标检测步骤（S3）。该方法能够在滤波器训练样本受限时，通过对波束-多普勒通道的稀疏自适应选择，实现小样本条件下的滤波器设计，进而通过构造目标检测器，达到有效抑制杂波与提高目标检测性能。

Description

一种波束-多普勒通道自适应选择STAP方法 技术领域
本发明涉及雷达信号处理领域，尤其涉及一种波束-多普勒通道自适应选择STAP方法。
背景技术
空时自适应处理(space-time adaptive processing，STAP)是提高机载雷达检测运动目标性能的关键技术，但该技术却面临着滤波器训练样本受限的挑战，而且该挑战在非均匀杂波环境下更为严峻。近十年来，该技术已取得了一定发展，如已提出的降维(reduced dimension)STAP方法，降秩(reduced rank)STAP方法，模型参数化(model-based)STAP方法，基于知识的(knowledge-aided)STAP方法，基于稀疏恢复的STAP方法等等。
就降维STAP方法而言，如辅助通道法(auxiliary channel receiver，ACR)，局域联合处理方法(joint domain localized，JDL)和空时多波束(space-time multiple-beam，STMB)方法，但这些方法在设计空时滤波器时所选取的波束-多普勒通道都是固定的，而不是最优的。同时，在阵列误差存在时，由于杂波谱扩展导致杂波子空间增大，而波束-多普勒通道固定，从而引起性能下降。
发明内容
有鉴于此，本发明的目的在于提供一种波束-多普勒通道自适应选择STAP方法，旨在解决现有波束-多普勒STAP技术中存在的波束-多普勒通道固定，由阵列误差等实际因素引起的杂波抑制与目标检测性能下降的问题。
本发明提出一种波束-多普勒通道自适应选择STAP方法，主要包括：
数据变换步骤：将阵列-脉冲维采样数据变换为波束-多普勒域数据；
滤波器设计步骤：通过引入稀疏约束，将空时滤波器权矢量设计问题转化为稀疏表示问题，并通过求解该稀疏表示问题而得到滤波器权矢量；
目标检测步骤：利用所述滤波器权矢量构造自适应匹配滤波检测器，实现杂波抑制与目标有效检测。
优选的，所述数据变换步骤具体包括：
构造NM×NM维的转换矩阵T＝[sTaux]，将阵列-脉冲维的空时快拍x转换到波束-多普勒域中，从而得到波束-多普勒域的NM×1维矢量数据
Figure PCTCN2016090334-appb-000001
其中，
Figure PCTCN2016090334-appb-000002
，fs，t、fd，t分别为目标空域波束频率与时域波束频率，且其对应的目标空时导向矢量为
Figure PCTCN2016090334-appb-000003
其中vd(fd，i)与vs(fs，j)分别为时域导向矢量与空域导向矢量，即vd(fd，i)＝[1，exp(j2πfd，i)，…，exp(j2π(N-1)fd，i)]T，vs(fs，i)＝[1，exp(j2πfs，j)，…，exp(j2π(N-1)fs，j)]T。
优选的，所述滤波器设计步骤具体包括：
通过引入稀疏约束至空时滤波器权矢量，将空时滤波器权矢量设计问题转化为稀疏表示问题
Figure PCTCN2016090334-appb-000004
其中，d＝[d1，d2，…，dL]T
Figure PCTCN2016090334-appb-000005
Figure PCTCN2016090334-appb-000006
为不含目标的训练样本集中的第l个空时快拍数据，dl＝sHxl表示为假设的目标所在的波束-多普勒通信号，L为总的快拍数，||·||p为lp范数。
优选的，所述目标检测步骤具体包括：
利用所述滤波器权矢量构造自适应匹配滤波检测器，并利用所述滤波检测器实现杂波抑制与目标的有效检测。
本发明提供的技术方案，利用(1)波束-多普勒域采样数据能够实现杂波子空间的分离和(2)在波束-多普勒域中，单个波束-多普勒通道的杂波子空间的维度远小于系统自由度这两种思想，将阵列-脉冲维采样转化为波束-多普勒域数据，通过引入稀疏约束将空时滤波器权矢量设计问题转化为稀疏表示问题，并通过求解该稀疏表示问题而得到滤波器权矢量，再通过求解该稀疏表示问题而得到滤波器权矢量并设计目标检测器，最后进行杂波抑制与目标检测。能够在滤波器训练样本受限时有效抑制杂波，相比传统波束-多普勒通道固定的STAP方法，能够实现波束-多普勒通道的自适应选择，进而克服由阵列误差等实际因素引起的性能下降问题，提高杂波抑制与目标检测性能。
附图说明
图1为本发明一实施方式中波束-多普勒通道自适应选择STAP方法流程图；
图2为本发明一实施方式中SCNR损失与训练样本数关系图；
图3为本发明一实施方式中SCNR损失与不同目标多普勒频率的关系图；
图4为本发明一实施方式中检测概率与SCNR的关系图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明使用于雷达信号处理领域，提供了一种基于稀疏约束的波束-多普勒通道自适应选择STAP(space-time adaptive processing，空时自适应处理)方法，将阵列-脉冲维采样转化为波束-多普勒域数据，通过引入稀疏约束将空时滤波器权矢量设计问题转化为稀疏表示的问题，再通过求解该稀疏表示问题而得到滤波器权矢量并设计目标检测器。最后进行杂波抑制与目标检测。能够在滤波器训练样本受限时有效抑制杂波，相比传统波束-多普勒通道固定的STAP方法，能够实现波束-多普勒通道的自适应选择，进而克服由阵列误差等实际因素引起的性能下降问题，提高杂波抑制与目标检测性能。
以下将对本发明所提供的一种波束-多普勒通道自适应选择STAP方法进行详细说明。
请参阅图1，为本发明一实施方式中波束-多普勒通道自适应选择STAP方法流程图。
在步骤S1中，数据变换步骤：将阵列-脉冲维采样数据变换为波束-多普勒域数据。
在本实施方式中，利用波束-多普勒域采样数据能够实现杂波子空间的分离。
在本实施方式中，假设一脉冲多普勒正侧视机载雷达天线为均匀线阵，包含M个接收阵元，该雷达在一个相干处理单元内发射N个脉冲，其中，所述数据变换步骤S1具体包括：
构造NM×NM维的转换矩阵T＝[sTaux]，将阵列-脉冲维的空时快拍x转换到波束-多普勒域中，从而得到波束-多普勒域的NM×1维矢量数据
Figure PCTCN2016090334-appb-000007
其中，
Figure PCTCN2016090334-appb-000008
fs，t、fd，t分别为目标空域波束频率与时域波束频率，且其对应的目标空时导向矢量为
Figure PCTCN2016090334-appb-000009
其中vd(fd，i)与vs(fs，j)分别为时域导向矢量与空域导向矢量，即vd(fd，i)＝[1，exp(j2πfd，i)，…，exp(j2π(N-1)fd，i)]T，vs(fs，i)＝[1，exp(j2πfs，j)，…，exp(j2π(N-1)fs，j)]T。
在步骤S2中，滤波器设计步骤：通过引入稀疏约束，将空时滤波器权矢量设计问题转化为稀疏表示问题，并通过求解该稀疏表示问题而得到滤波器权矢量。
在本实施方式中，利用在波束-多普勒域中，单个波束-多普勒通道的杂波子空间的维度远小于系统自由度的思想。
在本实施方式中，所述滤波器设计步骤S2具体包括：
通过引入稀疏约束至空时滤波器权矢量，将空时滤波器权矢量设计问题转化为稀疏表示问题
Figure PCTCN2016090334-appb-000010
，其中，d＝[d1，d2，…，dL]T
Figure PCTCN2016090334-appb-000011
Figure PCTCN2016090334-appb-000012
为不含目标的训练样本集中的第l个空时快拍数据，dl＝sHxl表示为假设的目标所在的波束-多普勒通信号，L为总的快拍数，||·||p为lp范数。
在本实施方式中，采用稀疏恢复算法求解所述滤波器设计步骤中的稀疏表示问题，从而得到滤波器权矢量并通过所述滤波器权矢量。
采用稀疏恢复算法(如FOCUSS算法)求解所述问题转化步骤S2中的稀疏表示问题，由FOCUSS算法可知，求解可分为两步：
Figure PCTCN2016090334-appb-000013
Figure PCTCN2016090334-appb-000014
其中，(A)+＝AH(AAH)-1为矩阵A的伪逆，q≥0为迭代次数，滤波器权矢量中的所有元素都可以用非零值来初使化，当滤波器满足某个中止条件时，迭代中止。例如，当迭代次数达到预设值qmax时，或权矢量前后的相对变化量
Figure PCTCN2016090334-appb-000015
足够小时，迭代中止。最后得到滤波器权矢量
Figure PCTCN2016090334-appb-000016
在步骤S3中，目标检测步骤：利用所述滤波器权矢量构造自适应匹配滤波检测器，实现杂波抑制与目标有效检测。
在本实施方式中，所述目标检测步骤S3具体包括：
利用所述滤波器权矢量构造自适应匹配滤波检测器，并利用所述滤波检测器实现杂波抑制与目标的有效检测。
在本实施方式中，采用参考自适应匹配滤波(Adaptive matched filter，AMF)方法，设计检测器为
Figure PCTCN2016090334-appb-000017
其中η为检测门限，
Figure PCTCN2016090334-appb-000018
δ为正的常量因子，H0表示没有目标，H1表示目标出现。
本发明提供的一种波束-多普勒通道自适应选择STAP方法，将阵列-脉冲维采样变换化为波束-多普勒域数据，通过引入稀疏约束将空时滤波器权矢量设计问题转化为稀疏表示的问题，再通过求解该稀疏表示问题而得到滤波器权矢量并设计目标检测器。最后进行杂波抑制与目标检测。能够在滤波器训练样本受限时有效抑制杂波，相比传统波束-多普勒通道固定的STAP方法，能够实现波束-多普勒通道的自适应选择，进而克服由阵列误差等实际因素引起的性能下降问题，提高杂波抑制与目标检测性能。
以下通过将本发明(SCBDS-STAP)与JDL、STMB、稀疏滤波器(Sparsity-aware beamformer)方法进行对比来说明本发明的有益效果。
本发明(SCBDS-STAP)与JDL、STMB、稀疏滤波器(Sparsity-aware beamformer)方法相比，具有更快的收敛性，而且接近最优输出性能，如图2所示。
在分别考虑有无误差的情况下，对于较小的目标多普勒频率而言，本发明 (SCBDS-STAP)比其它算法(如JDL、STMB、稀疏滤波器等等)的输出性能更优，即本发明(SCBDS-STAP)更适合检测低速运动目标，如图3所示。
在分别考虑有无误差的情况下，本发明(SCBDS-STAP)对目标的检测概率(PD)要高于其它三种方法(如JDL、STMB、稀疏滤波器方法)，如图4所示。
本发明将阵列-脉冲维采样转化为波束-多普勒域数据，通过引入稀疏约束，将空时滤波器权矢量设计问题转化为稀疏表示问题，并通过求解该稀疏表示问题而得到滤波器权矢量，再通过求解该稀疏表示问题而得到滤波器权矢量并设计目标检测器，然后进行杂波抑制与目标检测。本发明可以应用于运动平台雷达杂波抑制与运动目标检测领域，以提高雷达系统杂波抑制水平与目标检测能力。
值得注意的是，上述实施例中，所包括的各个单元只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本发明的保护范围。
另外，本领域普通技术人员可以理解实现上述各实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，相应的程序可以存储于一计算机可读取存储介质中，所述的存储介质，如ROM/RAM、磁盘或光盘等。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明 的保护范围之内。

Claims (4)

  1. 一种波束-多普勒通道自适应选择STAP方法，其特征在于，所述方法包括：
    数据变换步骤：将阵列-脉冲维采样数据变换为波束-多普勒域数据；
    滤波器设计步骤：通过引入稀疏约束，将空时滤波器权矢量设计问题转化为稀疏表示问题，并通过求解该稀疏表示问题而得到滤波器权矢量；
    目标检测步骤：利用所述滤波器权矢量构造自适应匹配滤波检测器，实现杂波抑制与目标有效检测。
  2. 如权利要求1所述的波束-多普勒通道自适应选择STAP方法，其特征在于，所述数据变换步骤具体包括：
    构造NM×NM维的转换矩阵T＝[sTaux]，将阵列-脉冲维的空时快拍x转换到波束-多普勒域中，从而得到波束-多普勒域的NM×1维矢量数据
    Figure PCTCN2016090334-appb-100001
    其中，
    Figure PCTCN2016090334-appb-100002
    fs，t、fd，t分别为目标空域波束频率与时域波束频率，且其对应的目标空时导向矢量为
    Figure PCTCN2016090334-appb-100003
    其中vd(fd，i)与vs(fs，j)分别为时域导向矢量与空域导向矢量，即vd(fd，i)＝[1，exp(j2πfd，i)，…，exp(j2π(N-1)fd，i)]T，vs(fs，i)＝[1，exp(j2πfs，j)，…，exp(j2π(N-1)fs，j)]T。
  3. 如权利要求2所述的波束-多普勒通道自适应选择STAP方法，其特征在 于，所述滤波器设计步骤具体包括：
    通过引入稀疏约束至空时滤波器权矢量，将空时滤波器权矢量设计问题转化为稀疏表示问题
    Figure PCTCN2016090334-appb-100004
    其中，d＝[d1，d2，…，dL]T
    Figure PCTCN2016090334-appb-100005
    Figure PCTCN2016090334-appb-100006
    为不含目标的训练样本集中的第l个空时快拍数据，dl＝sHxl表示为假设的目标所在的波束-多普勒通信号，L为总的快拍数，||·||p为lp范数。
  4. 如权利要求3所述的波束-多普勒通道自适应选择STAP方法，其特征在于，所述目标检测步骤具体包括：
    利用所述滤波器权矢量构造自适应匹配滤波检测器，并利用所述滤波检测器实现杂波抑制与目标的有效检测。
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