WO2022134764A1 - 非高斯噪声下的雷达信号波形与目标角度联合估计方法 - Google Patents

非高斯噪声下的雷达信号波形与目标角度联合估计方法 Download PDF

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WO2022134764A1
WO2022134764A1 PCT/CN2021/123929 CN2021123929W WO2022134764A1 WO 2022134764 A1 WO2022134764 A1 WO 2022134764A1 CN 2021123929 W CN2021123929 W CN 2021123929W WO 2022134764 A1 WO2022134764 A1 WO 2022134764A1
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gaussian noise
radar
signal
target
objective function
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PCT/CN2021/123929
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English (en)
French (fr)
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李强
黄磊
黄敏
赵博
张沛昌
孙维泽
赵源
刘仕奇
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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
    • G01S7/411—Identification of targets based on measurements of radar reflectivity

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  • the invention relates to the technical field of electronic information, in particular to a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise.
  • the position of the target is determined by estimating the radar waveform or the speed of the target is determined by estimating the angle of the radar target, and the Gaussian noise environment is considered, and the non-Gaussian noise environment is not considered.
  • the method of the prior art The target cannot be accurately sensed.
  • the technical problem to be solved by the present invention is to provide a method for joint estimation of radar signal waveform and target angle under non-Gaussian noise, aiming at solving the problem of determining the target by studying radar waveform estimation in the prior art
  • the position of the target or the speed of the target is determined by the radar target angle estimation, and the Gaussian noise environment is considered, and the non-Gaussian noise environment is not considered, so the target cannot be accurately perceived.
  • an embodiment of the present invention provides a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, wherein the method includes:
  • the waveform and target angle of the echo signal are obtained.
  • the echo signal is generated in the following manner:
  • the radar array antenna transmits a number of pulse signals distributed in a preset angle range
  • the pulse signal acts on the target object after passing through the non-Gaussian noise channel, the pulse signal is reflected by the target object to generate a reflection signal;
  • the reflected signal returns to the radar array antenna after passing through the non-Gaussian noise channel to generate an echo signal.
  • performing optimization target processing on the echo signal according to the echo signal, and obtaining the optimization target processing result includes:
  • the second optimization objective function is solved to obtain variable values corresponding to the second optimization objective function.
  • the generating a second optimization objective function according to the first optimization objective function includes:
  • obtaining the second optimization objective function according to the constraint variable factor includes:
  • conditional constraints are performed on the first optimization objective function to obtain a conditional constraint objective function
  • an iterative re-weighting method is used to reconstruct the condition-constrained objective function to obtain a second optimized objective function.
  • the solving of the second optimization objective function to obtain a variable value corresponding to the second optimization objective function includes:
  • Lagrangian transformation is performed on the second optimization objective function to obtain a Lagrangian transformation function
  • the Lagrangian transform function is solved to obtain function variable values.
  • obtaining the function variable value by solving the Lagrangian transformation function includes:
  • the Lagrangian function is derived and the derivative is set to 0 to obtain a derivative function
  • the derivative function is solved to obtain the function variable value.
  • the obtaining the waveform and target angle of the echo signal according to the optimization target processing result includes:
  • variable value is analyzed to obtain the waveform of the echo signal and the target angle.
  • an embodiment of the present invention further provides an apparatus for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, wherein the apparatus includes:
  • an echo signal acquisition unit configured to acquire echo signals collected by the radar array antenna under non-Gaussian noise; wherein, the non-Gaussian noise is impulse noise and abnormal values of echo data;
  • an optimization target processing unit configured to perform optimization target processing on the echo signal according to the echo signal to obtain an optimization target processing result
  • the waveform and target angle acquisition unit of the echo signal is configured to obtain the waveform and target angle of the echo signal according to the processing result of the optimization target.
  • an embodiment of the present invention further provides an intelligent terminal including a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors
  • the one or more programs include a method for performing a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise as described in any one of the above.
  • an embodiment of the present invention further provides a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute any of the above A joint estimation method of radar signal waveform and target angle under non-Gaussian noise.
  • the embodiment of the present invention first obtains the echo signal collected by the radar array antenna under non-Gaussian noise; wherein, the non-Gaussian noise is impulse noise and the abnormal value of the echo data; and then according to the echo signal , perform optimization target processing on the echo signal, and obtain the optimization target processing result; finally, according to the optimization target processing result, obtain the waveform and target angle of the echo signal; it can be seen that in the embodiment of the present invention, the non-Gaussian method Under the background of noise, the radar signal waveform and the target angle can be estimated at the same time, which can accurately estimate the position of the target and obtain the imaging characteristics of the target.
  • FIG. 1 is a schematic flowchart of a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a principle of joint estimation of a radar signal waveform and a target angle according to an embodiment of the present invention.
  • FIG. 3 is a simulation diagram of the MSE of the estimated value of the radar echo signal according to the embodiment of the present invention changing with the number of iterations.
  • FIG. 4 is a schematic block diagram of an apparatus for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise according to an embodiment of the present invention.
  • FIG. 5 is a schematic block diagram of an internal structure of an intelligent terminal provided by an embodiment of the present invention.
  • the present invention discloses a method for jointly estimating radar signal waveform and target angle under non-Gaussian noise. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.
  • the radar waveform estimation usually recovers the waveform of the echo signal by estimating the frequency and phase of the echo signal.
  • the target signal angle estimation usually adopts the spatial spectrum estimation method, such as the multiple signal classification method and the rotation invariant subspace method, by constructing the signal covariance matrix, and then obtaining the signal subspace or the noise subspace, and then estimating the target angle.
  • the existing technology does not estimate the radar waveform and the target angle at the same time, so the accurate position and imaging characteristics of the target cannot be obtained at the same time.
  • this embodiment provides a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, through which the exact position of the target and the imaging characteristics of the target can be estimated simultaneously.
  • the echo signals collected by the radar array antenna under non-Gaussian noise are first obtained to prepare for subsequent optimization processing; wherein, the non-Gaussian noise is impulse noise and abnormal values of echo data; then according to the echo signals wave signal, perform optimization target processing on the echo signal, and obtain the optimization target processing result, and the optimization target processing result includes the waveform and target angle information of the subsequent echo signal; according to the optimization target processing result, obtain the echo signal Signal waveform and target angle.
  • the imaging characteristic of the target can be obtained while accurately estimating the position of the target.
  • the environment of radar waveform propagation has two channel conditions: Gaussian noise and non-Gaussian noise.
  • Gaussian noise Most of the existing technologies are based on Gaussian noise channel conditions for radar waveform estimation and radar target angle estimation, but in practice, it is unavoidable. Therefore, it is also very important to study the channel situation of non-Gaussian noise.
  • the position of the target and the imaging characteristics of the target need to be extracted at the same time in practice, it is very important to jointly estimate the radar signal waveform and the target angle.
  • the system will first acquire the echo signal collected by the radar array antenna under non-Gaussian noise. Since the signal passes through the non-Gaussian noise channel, the received signal is mixed with noise, so the echo signal needs to be analyzed.
  • the waveform and target angle of the echo signal are obtained according to the optimization target processing result.
  • the Doppler frequency and phase can be obtained according to the waveform of the echo signal, and the moving speed of the target can be obtained by the Doppler frequency, and the position of the target can be obtained more accurately according to the phase obtained by the target angle and the waveform of the echo signal.
  • This embodiment provides a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, and the method can be applied to an intelligent terminal of electronic information. Specifically, as shown in Figure 1, the method includes:
  • Step S100 acquiring echo signals collected by the radar array antenna under non-Gaussian noise; wherein, the non-Gaussian noise is impulse noise and abnormal values of echo data.
  • the radar transmitting array antenna will first transmit ultrasonic signals, and the ultrasonic signals will return if they encounter obstacles when passing through the actual space, and the ultrasonic signals will be superimposed on the way back and forth when passing through the actual space, so the non-Gaussian noise Under the channel of , the echo signal collected by the radar array antenna is an echo signal containing non-Gaussian noise, and the echo signal is prepared for subsequent optimization processing.
  • the echo signal is generated in the following way: the radar array antenna transmits a number of pulse signals distributed in a preset angle range; when the pulse signal acts on the target object after passing through the non-Gaussian noise channel, the pulse signal is transmitted by the target object. reflection to generate a reflected signal; the reflected signal returns to the radar array antenna after passing through a non-Gaussian noise channel to generate an echo signal.
  • the radar transmitting array antenna adopts a uniform linear array antenna, the distance between adjacent array elements is d, and the number of antenna array elements is M.
  • M is 80, and each antenna element sends several pulse signals.
  • the pulse signal is an ultrasonic signal, and the pulse signal is distributed in a pulse signal of a preset angle range.
  • the preset angle ⁇ [-20°, 20°], when the pulse signal passes through the non-Gaussian noise channel When it acts on the object, it will be reflected by the target object, and the formed reflected signal will return to the radar receiving array antenna after passing through the non-Gaussian noise channel, that is, the echo signal.
  • This embodiment provides a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, and the method can be applied to an intelligent terminal of electronic information. Specifically, as shown in Figure 1, the method includes:
  • Step S200 performing optimization target processing on the echo signal according to the echo signal, to obtain an optimization target processing result
  • the echo signal since the echo signal has iterated non-Gaussian noise on the back-and-forth channel, it is necessary to perform optimization target processing on the echo signal to filter out the high-speed noise part in the echo signal.
  • performing the optimization target processing on the echo signal according to the echo signal to obtain the optimization target processing result includes the following steps:
  • Step S201 constructing a received signal model of the radar array antenna according to the echo signal
  • Step S202 constructing a first optimization objective function corresponding to the echo signal according to the echo signal and the received signal model
  • Step S203 generating a second optimization objective function according to the first optimization objective function
  • Step S204 Solve the second optimization objective function to obtain variable values corresponding to the second optimization objective function.
  • a received signal model of the radar array antenna is constructed.
  • the radar receiving array antenna has a distance between adjacent array elements of d, the number of antenna elements is M, and receives L echoes.
  • Each echo signal is sampled in the time domain to obtain K sampling points.
  • the radar scanning angle interval is defined as ⁇ , which is divided into J angles at equal intervals, and the signal model Y received by the radar array antenna can be expressed as:
  • X is the unknown echo sampling signal, which is a J ⁇ K-dimensional matrix with a row sparse structure, which contains L actual echo signals, and other parts have no data.
  • the signal Y [y(1) y(2)...y(K)] received by the array antenna is an M ⁇ K-dimensional matrix,
  • N is non-Gaussian noise
  • a( ⁇ j ) is the steering vector with angle ⁇ j , expressed as:
  • ⁇ represents the radar signal wavelength
  • the purpose of the present invention is to accurately restore the radar echo sampling signal according to the data collected by the array antenna, thereby estimating the radar incident angle and the radar echo signal waveform.
  • the invention simultaneously considers the non-Gaussian noise background condition and the spatial sparse characteristic of the radar echo sampling signal, and establishes the first optimization objective function
  • ⁇ represents the regularization factor
  • 2 , 1 represents norm, defined as
  • the generating the second optimization objective function according to the first optimization objective function includes the following steps: obtaining a constraint variable factor; wherein the constraint variable factor includes information for constraining the function; The constraint variable factor is described above, and the second optimization objective function is obtained. get constraint variable factor Then the first optimization objective function is transformed into the following second optimization objective function
  • the obtaining of the second optimization objective function according to the constraint variable factor includes the following steps: performing conditional constraints on the first optimization objective function according to the constraint variable factor to obtain a conditional constraint objective function; according to the spatial sparse characteristic of the radar echo sampling signal, the iterative re-weighting method is used to reconstruct the conditional constraint objective function to obtain the second optimization objective function.
  • I is an M ⁇ M dimensional identity matrix.
  • the iterative re-weighting technique is used to improve the second optimization objective function in the above formula, which is expressed as
  • Z i represents the data of the i-th row of matrix Z
  • 2 represents Norm
  • the solving of the second optimization objective function to obtain the variable value corresponding to the second optimization objective function includes the following steps: pulling the second optimization objective function
  • the Lagrangian transformation function is obtained by the Grange transformation; the Lagrangian function is derived and the derivative is set to 0 to obtain the derivative function; the derivative function is solved to obtain the function variable value.
  • the Lagrangian method is used to solve the above formula, and after introducing the Lagrangian variable ⁇ , the formula
  • the Lagrangian form of is expressed as:
  • Tr represents the trace of the matrix
  • ( ⁇ ) H represents the transpose operation
  • ⁇ is a small constant value greater than 0, in order to prevent the denominator of the above formula from being 0.
  • This embodiment provides a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, and the method can be applied to an intelligent terminal of electronic information. Specifically, as shown in Figure 1, the method includes:
  • Step S300 Obtain the waveform and target angle of the echo signal according to the processing result of the optimization target.
  • the waveform and target angle of the echo signal can be obtained according to a certain adaptive algorithm or calculation method according to the variable value of the optimization function after the optimization target processing is performed on the echo signal.
  • obtaining the waveform and target angle of the echo signal according to the optimization target processing result includes the following steps:
  • Step S301 according to the principle of joint estimation of radar signal waveform and target angle, analyze the variable value to obtain the waveform of the echo signal and the target angle.
  • Radar waveform estimation usually restores the waveform of the echo signal by estimating the frequency and phase of the echo signal.
  • the target signal angle estimation usually adopts the spatial spectrum estimation method, such as the multiple signal classification method and the rotation invariant subspace method, by constructing the signal covariance matrix, and then obtaining the signal subspace or the noise subspace, and then estimating the target angle. Therefore, by analyzing the variable value of this optimization function according to the principle of joint estimation of radar signal waveform and target angle, the waveform of the echo signal and the target angle can be obtained.
  • the first J lines in the variable Z are the radar echo sampling signal X to be estimated. According to Fig. 2, each row of X corresponds to an angle of the radar scanning interval ⁇ .
  • the angle corresponding to the row in which the data exists is the incident angle of the radar echo signal.
  • the data in this row is the sampling value of the radar echo signal, that is, the waveform of the radar echo signal. Therefore, according to the recovered X, the incident angle and waveform of the radar echo signal can be obtained at the same time.
  • MSE mean square error
  • an embodiment of the present invention provides an apparatus for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise.
  • the apparatus includes an echo signal acquisition unit 401, an optimized target processing unit 402, and a waveform of the echo signal. and the target angle obtaining unit 403, wherein:
  • An echo signal acquisition unit 401 configured to acquire echo signals collected by a radar array antenna under non-Gaussian noise; wherein, the non-Gaussian noise is impulse noise and abnormal values of echo data;
  • the waveform and target angle acquisition unit 403 of the echo signal is configured to obtain the waveform and target angle of the echo signal according to the processing result of the optimization target.
  • the present invention also provides an intelligent terminal, the principle block diagram of which may be shown in FIG. 5 .
  • the intelligent terminal includes a processor, a memory, a network interface, a display screen, and a temperature sensor connected through a system bus.
  • the processor of the intelligent terminal is used to provide computing and control capabilities.
  • the memory of the intelligent terminal includes a non-volatile storage medium and an internal memory.
  • the nonvolatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium.
  • the network interface of the intelligent terminal is used for communicating with external terminals through network connection.
  • the display screen of the smart terminal may be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the smart terminal is pre-set inside the smart terminal to detect the operating temperature of the internal equipment.
  • FIG. 5 is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the intelligent terminal to which the solution of the present invention is applied.
  • the specific intelligent terminal may include There are more or fewer components than shown in the figures, or some components are combined, or have a different arrangement of components.
  • an intelligent terminal includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors
  • One or more programs contain instructions to:
  • the waveform and target angle of the echo signal are obtained.
  • Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • the present invention discloses a method for jointly estimating a radar signal waveform and a target angle under non-Gaussian noise, an intelligent terminal, and a storage medium, the method comprising: acquiring echoes collected by a radar array antenna under non-Gaussian noise signal; wherein, the non-Gaussian noise is impulse noise and an abnormal value of echo data; according to the echo signal, perform optimization target processing on the echo signal to obtain an optimization target processing result; according to the optimization target processing result , to obtain the waveform and target angle of the echo signal.
  • the imaging characteristic of the target can be obtained while accurately estimating the position of the target.
  • the present invention discloses a method for jointly estimating radar signal waveform and target angle under non-Gaussian noise. It should be understood that the application of the present invention is not limited to the above examples. According to the above description, improvements or changes should be made, and all such improvements and changes should fall within the protection scope of the appended claims of the present invention.

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Abstract

一种非高斯噪声下的雷达信号波形与目标角度联合估计方法、智能终端、非临时性计算机可读存储介质，该方法包括：获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，非高斯噪声为脉冲噪声和回波数据异常值（S100）；根据回波信号，对回波信号进行优化目标处理，得到优化目标处理结果（S200）；根据优化目标处理结果，得到回波信号的波形和目标角度（S300）。该方法通过在非高斯噪声背景下同时估算出雷达信号波形与目标角度，能准确估计目标的位置的同时，得到目标的成像特性。

Description

非高斯噪声下的雷达信号波形与目标角度联合估计方法 技术领域
本发明涉及电子信息技术领域，尤其涉及的是一种非高斯噪声下的雷达信号波形与目标角度联合估计方法。
背景技术
现有技术都是通过究雷达波形估计来确定目标的位置或通过雷达目标角度估计来确定目标的速度，并且都是考虑的高斯噪声的环境，没有考虑非高斯噪声的环境，现有技术的方法无法对目标进行精确感知。
因此，现有技术还有待改进和发展。
发明内容
本发明要解决的技术问题在于，针对现有技术的上述缺陷，提供一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，旨在解决现有技术中通过究雷达波形估计来确定目标的位置或通过雷达目标角度估计来确定目标的速度，并且都是考虑的高斯噪声的环境，没有考虑非高斯噪声的环境，无法对目标进行精确感知的问题。
本发明解决问题所采用的技术方案如下：
第一方面，本发明实施例提供一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，其中，所述方法包括：
获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；
根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
在一种实现方式中，其中，所述回波信号生成方式为：
雷达阵列天线发射若干分布于预设角度范围的脉冲信号；
当所述脉冲信号经过非高斯噪声信道后作用于目标物体时，所述脉冲信号被所述目标物体反射，生成反射信号；
所述反射信号经过非高斯噪声信道后返回至雷达阵列天线，生成回波信号。
在一种实现方式中，其中，所述根据所述回波信号，对所述回波信号进行优化目标 处理，得到优化目标处理结果包括：
根据所述回波信号，构建雷达阵列天线的接收信号模型；
根据所述回波信号和所述接收信号模型，构建与所述回波信号对应的第一优化目标函数；
根据所述第一优化目标函数，生成第二优化目标函数；
求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值。
在一种实现方式中，其中，所述根据所述第一优化目标函数，生成第二优化目标函数包括：
获取约束变量因子；其中，所述约束变量因子包含对函数进行约束的信息；
根据所述约束变量因子，得到第二优化目标函数。
在一种实现方式中，其中，所述根据所述约束变量因子，得到第二优化目标函数包括：
根据所述约束变量因子，对所述第一优化目标函数进行条件约束，得到条件约束目标函数；
根据雷达回波采样信号的空域稀疏特性，采用迭代重加权方法重建所述条件约束目标函数，得到第二优化目标函数。
在一种实现方式中，其中，所述求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值包括：
将所述第二优化目标函数进行拉格朗日变换，得到拉格朗日变换函数；
求解所述拉格朗日变换函数，得到函数变量值。
在一种实现方式中，其中，所述求解所述拉格朗日变换函数，得到函数变量值包括：
对所述拉格朗日函数进行求导并将导数置0，得到导数函数；
求解所述导数函数，得到函数变量值。
在一种实现方式中，其中，所述根据所述优化目标处理结果，得到所述回波信号的波形和目标角度包括：
根据雷达信号波形与目标角度联合估计原理，解析所述变量值，得到所述回波信号的波形和目标角度。
第二方面，本发明实施例还提供一种非高斯噪声下的雷达信号波形与目标角度联合估计装置，其中，所述装置包括：
回波信号获取单元，用于获取非高斯噪声下的雷达阵列天线采集的回波信号；其中， 所述非高斯噪声为脉冲噪声和回波数据异常值；
优化目标处理单元，用于根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
回波信号的波形和目标角度获取单元，用于根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
第三方面，本发明实施例还提供一种智能终端，包括有存储器，以及一个或者一个以上的程序，其中一个或者一个以上程序存储于存储器中，且经配置以由一个或者一个以上处理器执行所述一个或者一个以上程序包含用于执行如上述任意一项所述的一种非高斯噪声下的雷达信号波形与目标角度联合估计方法。
第四方面，本发明实施例还提供一种非临时性计算机可读存储介质，当所述存储介质中的指令由电子设备的处理器执行时，使得电子设备能够执行如上述中任意一项所述的一种非高斯噪声下的雷达信号波形与目标角度联合估计方法。
本发明的有益效果：本发明实施例首先获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；然后根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；最后根据所述优化目标处理结果，得到所述回波信号的波形和目标角度；可见，本发明实施例中通过在非高斯噪声背景下同时估算出雷达信号波形与目标角度，能准确估计目标的位置的同时，得到目标的成像特性。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例提供的一种非高斯噪声下的雷达信号波形与目标角度联合估计方法流程示意图。
图2为本发明实施例提供的雷达信号波形与目标角度联合估计原理示意图。
图3为本发明实施例提供的雷达回波信号估计值的MSE随着迭代次数变化的仿真图。
图4为本发明实施例提供的一种非高斯噪声下的雷达信号波形与目标角度联合估计装置的原理框图。
图5为本发明实施例提供的智能终端的内部结构原理框图。
具体实施方式
本发明公开了一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，为使本发明的目的、技术方案及效果更加清楚、明确，以下参照附图并举实施例对本发明进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
本技术领域技术人员可以理解，除非特意声明，这里使用的单数形式“一”、“一个”、“所述”和“该”也可包括复数形式。应该进一步理解的是，本发明的说明书中使用的措辞“包括”是指存在所述特征、整数、步骤、操作、元件和/或组件，但是并不排除存在或添加一个或多个其他特征、整数、步骤、操作、元件、组件和/或它们的组。应该理解，当我们称元件被“连接”或“耦接”到另一元件时，它可以直接连接或耦接到其他元件，或者也可以存在中间元件。此外，这里使用的“连接”或“耦接”可以包括无线连接或无线耦接。这里使用的措辞“和/或”包括一个或更多个相关联的列出项的全部或任一单元和全部组合。
本技术领域技术人员可以理解，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)，具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语，应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非像这里一样被特定定义，否则不会用理想化或过于正式的含义来解释。
由于现有技术中，只有雷达波形估计或者目标角度估计，雷达波形估计通常是通过估计出回波信号的频率和相位，从而恢复出回波信号的波形。目标信号角度估计通常是采用空间谱估计方法，如多重信号分类方法和旋转不变子空间方法，通过构建信号协方差矩阵，然后获得信号子空间或噪声子空间，进而估计出目标角度。但是现有技术没有同时对雷达波形和目标角度进行同时估计，也就无法同时得到目标的准确位置和成像特性。
为了解决现有技术的问题，本实施例提供了一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，通过上述方法可以同时估计目标的准确位置和目标的成像特性。具体实施时，先获取非高斯噪声下的雷达阵列天线采集的回波信号，为后续进行优化处理做准备；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；接着根据所述回波信 号，对所述回波信号进行优化目标处理，得到优化目标处理结果，优化目标处理结果包含了后续回波信号的波形和目标角度信息；根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。本发明实施例通过在非高斯噪声背景下同时估算出雷达信号波形与目标角度，能准确估计目标的位置的同时，得到目标的成像特性。
举例说明
在实际中，雷达波形传播的环境有高斯噪声和非高斯噪声两种信道情况，现有技术多数都是基于高斯噪声信道情况来进行雷达波形估计和雷达目标角度估计，但是在实际中，不可避免的出现脉冲噪声、回波数据异常值等非高斯噪声的信道情况，因此，对非高斯噪声的信道情况的研究也十分重要。此外，由于实际中也需要同时提取目标的位置和目标成像特性，故对雷达信号波形与目标角度联合估计就显得很重要。在本实施例中，系统会先获取非高斯噪声下的雷达阵列天线采集的回波信号，由于是信号经过了非高斯噪声的信道，接收的信号混合进了噪声，故需要对回波信号进行优化目标处理，在根据优化目标处理结果，得到回波信号的波形和目标角度。这样，根据回波信号的波形就可以得到多普勒频率和相位，多普勒频率又可以得到目标的移动速度，根据目标角度和回波信号的波形得到的相位可以更加精准的得到目标的位置。
示例性方法
本实施例提供一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，该方法可以应用于电子信息的智能终端。具体如图1所示，所述方法包括：
步骤S100、获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值。
具体地，雷达发射阵列天线会先发射超声波信号，超声波信号在经过实际空间时如果碰到障碍物就会返回，并且，超声波信号在经过实际空间时来回的路上都会叠加噪声，故在非高斯噪声的信道下，雷达阵列天线采集的回波信号是包含了非高斯噪声的回波信号，回波信号为后续进行优化处理做准备。
所述回波信号生成方式为：雷达阵列天线发射若干分布于预设角度范围的脉冲信号；当所述脉冲信号经过非高斯噪声信道后作用于目标物体时，所述脉冲信号被所述目标物体反射，生成反射信号；所述反射信号经过非高斯噪声信道后返回至雷达阵列天线，生成回波信号。
具体地，雷达发射阵列天线采用均匀线性阵列天线，相邻阵元间距为d，天线阵元数目为M，在本实施例中，M为80，每个天线阵元发送若干脉冲信号，在本实施例中， 脉冲信号为超声波信号，脉冲信号分布于预设角度范围的脉冲信号，在本实施例中，预设角度Θ＝[-20°，20°],当脉冲信号经过非高斯噪声信道后作用于物体时，就会被目标物体所反射，形成的反射信号再经过非高斯噪声信道后会返回到雷达接收阵列天线，也即回波信号。
本实施例提供一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，该方法可以应用于电子信息的智能终端。具体如图1所示，所述方法包括：
步骤S200、根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
具体地，由于回波信号已经迭代了来回信道上的非高斯噪声，因此，需要对回波信号进行优化目标处理，将回波信号中的高速噪声部分滤除。
为了采用较低的计算复杂度来得到优化目标处理结果，所述根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果包括如下步骤：
步骤S201、根据所述回波信号，构建雷达阵列天线的接收信号模型；
步骤S202、根据所述回波信号和所述接收信号模型，构建与所述回波信号对应的第一优化目标函数；
步骤S203、根据所述第一优化目标函数，生成第二优化目标函数；
步骤S204、求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值。
具体地，根据所述回波信号，构建雷达阵列天线的接收信号模型，在本实施例中，雷达接收阵列天线，相邻阵元间距为d，天线阵元数目为M，接收到L个回波信号，对每个回波信号进行时域采样得到K个采样点。雷达扫描角度区间定义为Θ，被等间隔分成J个角度则雷达阵列天线接收到的信号模型Y可以表示为：
Y＝AX+N。
上式中，X是未知的回波采样信号，它是具有行稀疏结构的J×K维矩阵，其中包含L个实际回波信号，其他部分无数据。阵列天线接收到的信号Y＝[y(1) y(2)…y(K)]为M×K维矩阵，
y(k)，i＝1，2，…，K，表示第k个采样时刻天线阵列采集到的包含噪声的数据，N为非高斯噪声，为M×N维矩阵。A为M×J维导向矢量矩阵，具体可以表示为A＝[a(θ 1) a(θ 2)…a(θ J)]，其中，
a(θ j)是角度为θ j的导向矢量，表示为：
Figure PCTCN2021123929-appb-000001
上式中λ表示雷达信号波长。
本发明的目的是根据阵列天线所采集到的数据，准确恢复雷达回波采样信号，从而估计出雷达入射角度和雷达回波信号波形。本发明同时考虑非高斯噪声背景条件和雷达回波采样信号的空域稀疏特性，建立第一优化目标函数
Figure PCTCN2021123929-appb-000002
上式中，γ表示正则化因子，||·|| 2，1表示
Figure PCTCN2021123929-appb-000003
范数，定义为
Figure PCTCN2021123929-appb-000004
由于上式采用CVX优化工具箱来求解，计算复杂度高，因此，将所述第一优化目标函数转化为第二优化目标函数，
在一种实现方式中，所述根据所述第一优化目标函数，生成第二优化目标函数包括如下步骤：获取约束变量因子；其中，所述约束变量因子包含对函数进行约束的信息；根据所述约束变量因子，得到第二优化目标函数。获取约束变量因子
Figure PCTCN2021123929-appb-000005
则第一优化目标函数转化为如下第二优化目标函数
Figure PCTCN2021123929-appb-000006
为了得到第二优化目标函数，所述根据所述约束变量因子，得到第二优化目标函数包括如下步骤：根据所述约束变量因子，对所述第一优化目标函数进行条件约束，得到条件约束目标函数；根据雷达回波采样信号的空域稀疏特性，采用迭代重加权方法重建所述条件约束目标函数，得到第二优化目标函数。
具体地，将
Figure PCTCN2021123929-appb-000007
表示为
Figure PCTCN2021123929-appb-000008
上式中，I为M×M维单位矩阵。
为了表示方便，令
Figure PCTCN2021123929-appb-000009
为P×K维矩阵，P＝(J+M)，B＝[A γI]，为M×P维矩阵，所以上式优化问题可以简化为
Figure PCTCN2021123929-appb-000010
由于雷达回波采样信号的空域稀疏特性，为了获得更准确且更稀疏的解，采用迭代重加权技术对上式中的第二优化目标函数进行改进，表示为
Figure PCTCN2021123929-appb-000011
上式中，Z i表示矩阵Z的第i行数据，||·|| 2表示
Figure PCTCN2021123929-appb-000012
范数，h i为加权向量h中的第i个元素，i＝1，2，…，P，其中，h为P×1维加权向量。
为了得到第二优化目标函数对应的变量值，所述求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值包括如下步骤：将所述第二优化目标函数进行拉格朗日变换，得到拉格朗日变换函数；对所述拉格朗日函数进行求导并将导数置0，得到导数函数；求解所述导数函数，得到函数变量值。
实际中，采用拉格朗日方法对上式进行求解，引入拉格朗日变量Λ后，则式
Figure PCTCN2021123929-appb-000013
的拉格朗日形式表达为：
Figure PCTCN2021123929-appb-000014
上式中，Tr表示矩阵的迹，(·) H表示转置操作。根据矩阵的迹理论，上式可以表示为：
Figure PCTCN2021123929-appb-000015
为了求出变量Z，令上式对变量Z进行求导且令导数等于0，即：
Figure PCTCN2021123929-appb-000016
上式中，Q为对角矩阵，对角线上第i个元素表示为
Figure PCTCN2021123929-appb-000017
其中，h i表示为
Figure PCTCN2021123929-appb-000018
上式中，μ为大于0的较小常数值，为了防止上式分母为0。
根据
Figure PCTCN2021123929-appb-000019
式可以得到：Z＝Q -1B HΛ
然后根据
Figure PCTCN2021123929-appb-000020
的约束条件：Y＝BZ，可以计算出拉格朗日变量Λ为
Λ＝(BQ -1B H) -1Y；
将上式代入到Z＝Q -1B HΛ，就可以得到
Z＝Q -1B H(BQ -1B H) -1Y。
本实施例提供一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，该方法可以应用于电子信息的智能终端。具体如图1所示，所述方法包括：
步骤S300、根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
根据对所述回波信号进行优化目标处理后的优化函数变量值，根据某种自适应算法或计算方法就可以得到所述回波信号的波形和目标角度。
为了得到所述回波信号的波形和目标角度，所述根据所述优化目标处理结果，得到所述回波信号的波形和目标角度包括如下步骤：
步骤S301、根据雷达信号波形与目标角度联合估计原理，解析所述变量值，得到所述回波信号的波形和目标角度。
雷达波形估计通常是通过估计出回波信号的频率和相位，从而恢复出回波信号的波形。目标信号角度估计通常是采用空间谱估计方法，如多重信号分类方法和旋转不变子空间方法，通过构建信号协方差矩阵，然后获得信号子空间或噪声子空间，进而估计出目标角度。因此，根据雷达信号波形与目标角度联合估计原理对这个优化函数变量值进行解析，就可以得到回波信号的波形和目标角度。具体地，根据公式
Figure PCTCN2021123929-appb-000021
可知，变量Z中的前J行即为待估计的雷达回波采样信号X。根据图2可知，X的每一行刚好对应雷达扫描区间Θ的一个角度。根据X的稀疏结构，其中存在数据的行所对应的角度，即为雷达回波信号的入射角度。此外，该行的数据就是雷达回波信号的采样值，即为雷达回波信号的波形。所以，根据所恢复的X，便 可以同时得到雷达回波信号的入射角度和波形。
为了验证本方案在具体实施时的良好估计性能，在本实施例中，设置均匀线性天线阵列具有80个天线阵元，相邻阵元间距d为雷达回波信号半波长，雷达扫描角度区间为Θ＝[-20°，20°]，以1°等间隔划分，即J＝41，假定有L＝4个目标回波信号，入射角度在Θ区间随机分布，信号采样点K＝50，正则化因子γ＝0.1，噪声采用混合高斯模型，信噪比为20dB，公式
Figure PCTCN2021123929-appb-000022
中参数μ＝0.1。由于本发明需要采用迭代算法，初始化参数Q为单位矩阵。附图3给出了雷达回波信号估计值X的均方误差(Mean Square Error,MSE)随着迭代次数变化的仿真图，从图中可以看出，随着迭代次数的增加，雷达回波信号估计值X的MSE逐渐降低。当迭代次数到第7次时，MSE值收敛至1×10 -4，对雷达信号波形与目标角度具有很好估计性能。
示例性设备
如图4中所示，本发明实施例提供一种非高斯噪声下的雷达信号波形与目标角度联合估计装置，该装置包括回波信号获取单元401，优化目标处理单元402，回波信号的波形和目标角度获取单元403，其中：
回波信号获取单元401，用于获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；
优化目标处理单元402，用于根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
回波信号的波形和目标角度获取单元403，用于根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
基于上述实施例，本发明还提供了一种智能终端，其原理框图可以如图5所示。该智能终端包括通过系统总线连接的处理器、存储器、网络接口、显示屏、温度传感器。其中，该智能终端的处理器用于提供计算和控制能力。该智能终端的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该智能终端的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种非高斯噪声下的雷达信号波形与目标角度联合估计方法。该智能终端的显示屏可以是液晶显示屏或者电子墨水显示屏，该智能终端的温度传感器是预先在智能终端内部设置，用于检测内部设备的运行温度。
本领域技术人员可以理解，图5中的原理图，仅仅是与本发明方案相关的部分结构的框图，并不构成对本发明方案所应用于其上的智能终端的限定，具体的智能终端可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种智能终端，包括有存储器，以及一个或者一个以上的程序，其中一个或者一个以上程序存储于存储器中，且经配置以由一个或者一个以上处理器执行所述一个或者一个以上程序包含用于进行以下操作的指令：
获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；
根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本发明所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
综上所述，本发明公开了一种非高斯噪声下的雷达信号波形与目标角度联合估计方法、智能终端、存储介质，所述方法包括：获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。本发明实施例通过在非高斯噪声背景下同时估算出雷达信号波形与目标角度，能准确估计目标的位置的同时，得到目标的成像特性。
应当理解的是，本发明公开了一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，应当理解的是，本发明的应用不限于上述的举例，对本领域普通技术人员来说，可以根据上述说明加以改进或变换，所有这些改进和变换都应属于本发明所附权利要求 的保护范围。

Claims (10)

  1. 一种非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述方法包括：
    获取非高斯噪声下的雷达阵列天线采集的回波信号；其中，所述非高斯噪声为脉冲噪声和回波数据异常值；
    根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果；
    根据所述优化目标处理结果，得到所述回波信号的波形和目标角度。
  2. 根据权利要求1所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述回波信号生成方式为：
    雷达阵列天线发射若干分布于预设角度范围的脉冲信号；
    当所述脉冲信号经过非高斯噪声信道后作用于目标物体时，所述脉冲信号被所述目标物体反射，生成反射信号；
    所述反射信号经过非高斯噪声信道后返回至雷达阵列天线，生成回波信号。
  3. 根据权利要求2所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述根据所述回波信号，对所述回波信号进行优化目标处理，得到优化目标处理结果包括：
    根据所述回波信号，构建雷达阵列天线的接收信号模型；
    根据所述回波信号和所述接收信号模型，构建与所述回波信号对应的第一优化目标函数；
    根据所述第一优化目标函数，生成第二优化目标函数；
    求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值。
  4. 根据权利要求3所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述根据所述第一优化目标函数，生成第二优化目标函数包括：
    获取约束变量因子；其中，所述约束变量因子包含对函数进行约束的信息；
    根据所述约束变量因子，得到第二优化目标函数。
  5. 根据权利要求4所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述根据所述约束变量因子，得到第二优化目标函数包括：
    根据所述约束变量因子，对所述第一优化目标函数进行条件约束，得到条件约束目标函数；
    根据雷达回波采样信号的空域稀疏特性，采用迭代重加权方法重建所述条件约束目标函数，得到第二优化目标函数。
  6. 根据权利要求5所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述求解所述第二优化目标函数，得到与所述第二优化目标函数对应的变量值包括：
    将所述第二优化目标函数进行拉格朗日变换，得到拉格朗日变换函数；
    求解所述拉格朗日变换函数，得到函数变量值。
  7. 根据权利要求6所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述求解所述拉格朗日变换函数，得到函数变量值包括：
    对所述拉格朗日函数进行求导并将导数置0，得到导数函数；
    求解所述导数函数，得到函数变量值。
  8. 根据权利要求7所述的非高斯噪声下的雷达信号波形与目标角度联合估计方法，其特征在于，所述根据所述优化目标处理结果，得到所述回波信号的波形和目标角度包括：
    根据雷达信号波形与目标角度联合估计原理，解析所述变量值，得到所述回波信号的波形和目标角度。
  9. 一种智能终端，其特征在于，包括有存储器，以及一个或者一个以上的程序，其中一个或者一个以上程序存储于存储器中，且经配置以由一个或者一个以上处理器执行所述一个或者一个以上程序包含用于执行如权利要求1-8中任意一项所述的方法。
  10. 一种非临时性计算机可读存储介质，其特征在于，当所述存储介质中的指令由电子设备的处理器执行时，使得电子设备能够执行如权利要求1-8中任意一项所述的方法。
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