WO2022007573A1 - 低数据量相干信号doa估计方法、装置、设备及介质 - Google Patents

低数据量相干信号doa估计方法、装置、设备及介质 Download PDF

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WO2022007573A1
WO2022007573A1 PCT/CN2021/098993 CN2021098993W WO2022007573A1 WO 2022007573 A1 WO2022007573 A1 WO 2022007573A1 CN 2021098993 W CN2021098993 W CN 2021098993W WO 2022007573 A1 WO2022007573 A1 WO 2022007573A1
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signal
data
low
coherent
echo signal
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French (fr)
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黄磊
袁伟健
赵博
侯万幸
潘天伦
包为民
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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
    • 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
    • G01S13/88—Radar or analogous systems specially adapted for specific applications

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  • the present application relates to the fields of data acquisition and signal processing, and in particular, to a method, apparatus, device, and readable storage medium for estimating DOA of a low-data-amount coherent signal.
  • Millimeter-wave array radar has all-weather, all-day long-range, high-resolution detection capabilities, and is used in advanced driver assistance systems (ADAS), blind spot monitoring, assisted lane change, UAV altitude determination and blind inspection, unmanned ship navigation, human body
  • ADAS advanced driver assistance systems
  • ADAS advanced driver assistance systems
  • blind spot monitoring assisted lane change
  • UAV altitude determination and blind inspection
  • unmanned ship navigation human body
  • Many fields play an important role.
  • the burden of data acquisition, transmission and processing is also increasing.
  • the Nyquist sampling theorem to sample the echo signal of a radar channel with a bandwidth of 1G Hz, a sampling rate of 2G Hz/sec is required. If each signal sample is quantized to 16 bits, the ADC rate is 4GB/sec.
  • a medium-sized hard drive can be filled in one minute, and if it is an array of multiple receive channels, the radar will cause the ADC to sample a larger amount of data.
  • the main purpose of the present application is to provide a low-data-volume coherent signal DOA estimation method, device, device, and readable storage medium, aiming to solve the problem that the existing low-data-volume coherent signal DOA estimation algorithm has low accuracy and a large amount of data collected. technical issues.
  • the present application provides a method for estimating DOA for a coherent signal with a low data amount, and the method for estimating the DOA for a coherent signal with a low data amount includes the following steps:
  • the direction of arrival of the echo signal is determined based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal.
  • the step of performing single-frequency time-varying threshold one-bit quantization sampling on the echo signal to obtain the quantized and sampled echo signal includes:
  • One-bit quantization sampling is performed on the echo signal by using the sampling signal to obtain the quantized and sampled echo signal.
  • the step of determining the direction of arrival of the echo signal based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal includes:
  • the signal subspace and the noise subspace are constructed based on the eigenvectors corresponding to the eigenvalues obtained after decomposition;
  • the spatial spectrum peak search is performed using the signal subspace and the noise subspace to obtain the direction of arrival of the echo signal.
  • the step of constructing the signal subspace and the noise subspace based on the eigenvectors corresponding to the eigenvalues obtained after decomposition includes:
  • the eigenvectors corresponding to the eigenvalues obtained after the decomposition are sorted, and the signal subspace and the noise subspace are constructed based on the sorted eigenvectors.
  • the step of constructing the signal subspace and the noise subspace based on the sorted eigenvectors includes:
  • the eigenvectors with the same number of objects are selected as the signal subspace, and the other eigenvectors in the sorted eigenvalues are used as the noise subspace.
  • the step of using the signal subspace and the noise subspace to perform a spatial spectrum peak search to obtain the direction of arrival of the echo signal includes:
  • a peak value is obtained for the spatial spectral function, and the obtained peak value is used as the estimated value of the direction of arrival.
  • the antenna array of the millimeter-wave array radar is a uniform linear array, and the steps of transmitting the radar signal and receiving the echo signal corresponding to the radar signal reflected by the target object include:
  • the forward and backward smoothing covariance matrix is determined based on the covariance matrix of each forward subarray and the covariance matrix of the backward subarray.
  • the low-data-amount coherent signal DOA estimation device includes:
  • the transceiver module is used to transmit radar signals and receive echo signals corresponding to the radar signals reflected by the target;
  • a sampling module configured to perform single-frequency time-varying threshold one-bit quantization sampling on the echo signal to obtain a quantized and sampled echo signal
  • the processing module is configured to determine the direction of arrival of the echo signal based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal.
  • the present application also provides a device, the device is a millimeter wave array radar, and the device includes: a memory, a processor, and a memory, a processor, and a memory device that is stored on the memory and can run on the processor.
  • a low-data-volume coherent signal DOA estimation program when the low-data-volume coherent signal DOA estimation program is executed by the processor, implements the steps of any one of the above-mentioned low-data-volume coherent signal DOA estimation methods.
  • the present application also provides a readable storage medium, where a low-data-volume coherent signal DOA estimation program is stored, and the low-data-volume coherent signal DOA estimation program is executed by a processor At the same time, the steps of implementing the DOA estimation method for a low-data-amount coherent signal described in any one of the above are performed.
  • the present application transmits radar signals and receives echo signals reflected by the target object, and then performs single-frequency time-varying threshold one-bit quantization sampling on the echo signals to obtain quantized and sampled echo signals, and then based on forward and backward spatial smoothing
  • the algorithm and the quantized and sampled echo signal determine the direction of arrival of the echo signal.
  • the single-frequency time-varying threshold one-bit quantized sampling reduces the amount of data in the process of radar echo data acquisition, storage, transmission, and processing, and saves system costs.
  • the forward and backward spatial smoothing algorithm is adopted to convert the rank of covariance of coherent signals. Recovering to the number of target objects realizes decorrelation and improves the accuracy of DOA estimation of coherent signals with low data amount.
  • FIG. 1 is a schematic structural diagram of a device in a hardware operating environment involved in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for estimating a DOA based on a low-data-volume coherent signal of the present application;
  • FIG. 3 is a schematic diagram of a uniform linear array in an embodiment of a method for estimating a DOA based on a low-data-amount coherent signal of the present application;
  • FIG. 4 is a schematic diagram of a coherent LFMCW signal in an embodiment of a method for estimating a DOA based on a low-data-amount coherent signal;
  • FIG. 5 is a schematic diagram of a single-frequency time-varying threshold signal in an embodiment of a method for estimating a DOA based on a low-data-amount coherent signal of the present application;
  • FIG. 6 is a schematic diagram after quantization of a single-frequency time-varying threshold value of a single-bit sampling of an echo LFMCW signal in an embodiment of the DOA estimation method for a low-data-amount coherent signal of the present application;
  • FIG. 7 is a schematic diagram of forward and backward spatial smoothing in an embodiment of the DOA estimation method for coherent signals based on low data amount of the present application;
  • FIG. 8 is a schematic diagram of DOA estimation of a low-data-volume coherent signal with forward and backward spatial smoothing after single-frequency time-varying threshold 1-bit sampling quantization in an embodiment of the present application based on a low-data-volume coherent signal DOA estimation method;
  • FIG. 9 is a schematic diagram of comparison of DOA estimation results of coherent LFMCW signals with low data amount coherent signals in an embodiment of the DOA estimation method based on low data amount coherent signals of the present application;
  • FIG. 10 is a schematic flowchart of a second embodiment of a method for estimating DOA based on a low-data-volume coherent signal of the present application
  • FIG. 11 is a schematic diagram of functional modules of an embodiment of an apparatus for estimating a DOA based on a low-data-amount coherent signal of the present application.
  • FIG. 1 is a schematic structural diagram of a device in a hardware operating environment involved in the solution of the embodiment of the present application.
  • the device may include: a processor 1001 , such as a CPU, a network interface 1004 , a user interface 1003 , a memory 1005 , and a communication bus 1002 .
  • the communication bus 1002 is used to realize the connection and communication between these components.
  • the user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may include a standard wired interface and a wireless interface (eg, a WI-FI interface).
  • the memory 1005 may be high-speed RAM memory, or may be non-volatile memory, such as disk memory.
  • the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .
  • the device structure shown in FIG. 1 does not constitute a limitation on the device, and may include more or less components than the one shown, or combine some components, or arrange different components.
  • the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a DOA estimation program based on a low-data-volume coherent signal.
  • the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server; the user interface 1003 is mainly used to connect to the client and perform data communication with the client; and the processor 1001 can be used for The DOA estimation procedure based on the low data volume coherent signal stored in the memory 1005 is called.
  • the device includes: a memory 1005, a processor 1001, and a low-data-amount coherent signal-based DOA estimation program stored on the memory 1005 and executable on the processor 1001, wherein the processor 1001 calls When the low-data-volume coherent signal-based DOA estimation program stored in the memory 1005 is executed, the steps of the low-data-volume coherent signal-based DOA estimation method provided by the various embodiments of the present application are executed.
  • FIG. 2 is a schematic flowchart of the first embodiment of the method for estimating a DOA for a coherent signal with a low data amount in the present application.
  • This embodiment of the present application provides an embodiment of a method for estimating a DOA of a coherent signal with a low data amount. It should be noted that, although a logical sequence is shown in the flowchart, in some cases, it may be performed in a sequence different from that here. steps shown or described.
  • the low-data-amount coherent signal DOA estimation method includes:
  • Step S100 transmitting a radar signal, and receiving an echo signal corresponding to the radar signal reflected by the target;
  • the radar finds targets by radio and determines their spatial positions. All-digital array antenna radars whose receiving and transmitting beams are digitally formed are called digital array radars.
  • Digital array radars generally consist of antenna arrays, digital transmit/receive (T/R) components, clocks, data transmission systems, and digital processors. composition.
  • the radar is divided into two categories: pulse radar and continuous wave radar according to the type of the transmitted signal. Conventional pulse radar transmits periodic high-frequency pulses, and continuous wave radar transmits continuous wave signals.
  • the radar of this application is a millimeter wave array radar, and the antenna array is a uniform linear array. As shown in Figure 3, the distance d between the two antenna elements is equal, and the linear frequency modulated continuous wave signal is used, which is abbreviated as LFMCW. .
  • the time delays of the echo signals reflected back in the reflected signal are the same, which will cause the echo signals to appear coherent signals at the same frequency due to the same reflection time.
  • the existing low-data-amount coherent signal DOA estimation algorithms such as traditional MUSIC, ESPRIT and other signal subspace algorithms, cannot effectively distinguish the echo signal DOA, where DOA is the electronic, communication, radar, It is an industry term in research fields such as sonar.
  • the low-data-volume coherent signal DOA estimation method proposed in this application reduces the radar echo data through single-frequency time-varying threshold one-bit quantization sampling, reduces the data volume in the process of data acquisition, storage, transmission, processing, etc., and saves the system cost.
  • the forward and backward spatial smoothing algorithm is adopted to improve the accuracy of DOA estimation of coherent signals with low data amount.
  • the millimeter-wave array radar uses linear frequency modulation continuous wave LFMCW to transmit radar signals, and the reflected signal X corresponding to the radar signal reflected by the target can be simply expressed as:
  • A is the corresponding matrix of the array
  • S is the echo signal
  • N is the array noise signal.
  • the reflected signal X(t) can be expressed as:
  • M represents the number of uniform array elements
  • N represents the number of snapshots
  • K represents the number of targets.
  • the requirement for the number of sources is K ⁇ M
  • A [a( ⁇ 1 ), a( ⁇ 2 ),...a( ⁇ K )] M ⁇ K
  • N [n 1 (t),n 2 (t),...n M (t)] M ⁇ N T
  • phase difference between the two array elements of the echo signal of the k-th target whose incoming wave direction is ⁇ K is:
  • d represents the array element spacing in a uniform linear array
  • ⁇ represents the wavelength of the chirp signal emitted by the array radar
  • ⁇ c/f c
  • c represents the speed of light
  • f c represents the center frequency of the chirp signal emitted by the array radar.
  • Step S200 performing single-frequency time-varying threshold one-bit quantization sampling on the echo signal to obtain a quantized and sampled echo signal;
  • step S200 includes:
  • Step S210 determining the frequency of the sampling signal based on the frequency of the echo signal, and determining the amplitude of the sampling signal based on the amplitude of the echo signal;
  • Step S220 Perform one-bit quantization sampling on the echo signal by using the sampling signal to obtain the quantized and sampled echo signal.
  • the echo signal reflected by the target after the time delay ⁇ is represented by S(t):
  • A is the maximum amplitude value of the target echo signal
  • B is the signal bandwidth
  • T is the signal pulse width
  • f c is the center frequency
  • ⁇ is the time delay of the echo signal relative to the transmitted signal. If ⁇ are consistent, a co-frequency coherent LFMCW signal will be generated. Configure parameters according to Table 1, and refer to FIG. 4 to obtain a coherent LFMCW signal.
  • the coherent LFMCW echo signal of each channel of the millimeter-wave array radar is quantized and sampled by a single-frequency time-varying threshold, and the echo data is quantized into one-bit sampling data by comparing the echo data with the time-varying threshold.
  • Signal processing echo data bit width to simplify the system and improve efficiency.
  • the traditional one-bit quantization compares the signal with the 0 threshold, which will cause nonlinear distortion of the relative amplitude of the signal and affect the quality of radar signal processing.
  • the random time-varying threshold can retain the amplitude information, it will introduce additional noise-like interference.
  • the single-frequency time-varying threshold can effectively retain the relative amplitude information lost in the one-bit sampling and quantization, while avoiding the introduction of noise-like interference, and effectively improve the low-data-amount coherent signal DOA of the coherent LFMCW signal under one-bit sampling and quantization. estimated quality.
  • sampling threshold is a single-frequency signal with a frequency of f 0 and an amplitude of A s, and the expression is:
  • the initial phase of the threshold is the initial phase of the threshold.
  • a fixed initial phase can be used to minimize the threshold storage parameters, or a random initial phase can be used to reduce the coherence characteristics of the time-varying threshold.
  • the single-frequency time-varying threshold can be directly generated by an analog oscillator, and its cost and complexity are far less than the random time-varying threshold that requires pre-calculation, high-precision storage, and real-time table lookup reproduction.
  • the parameter settings of the single-frequency time-varying threshold function are shown in Table 2, and the phase is set to a random initial phase, in order to reduce the coherence characteristics of the time-varying threshold, and the resulting time-varying threshold function is shown in Figure 5.
  • a single-frequency signal used for one-bit sampling and quantization its amplitude A s can be set as the average value of the amplitude of the sampling signal; its frequency f 0 is generally less than 0.5 times the bandwidth of the LFMCW signal, mainly to reduce quantization.
  • the amount of data after sampling because the value of f 0 is the same as that of A s , it will affect the amount of data after quantization and sampling.
  • the larger the frequency f 0, the larger the amount of data, which will affect the equipment.
  • the value of f 0 should be set as small as possible, generally less than 0.5 times the bandwidth.
  • Step S300 Determine the direction of arrival of the echo signal based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal.
  • step S300 includes:
  • Step S310 constructing a covariance matrix based on the quantized and sampled echo signals, and performing eigenvalue decomposition on the covariance matrix;
  • Step S320 constructing a signal subspace and a noise subspace based on the eigenvectors corresponding to the eigenvalues obtained after decomposition;
  • step S320 includes:
  • Step S321 Sort the eigenvectors corresponding to the eigenvalues obtained after the decomposition, and construct the signal subspace and the noise subspace based on the sorted eigenvectors.
  • step S321 includes: selecting the same eigenvalues as the number of objects from the sorted eigenvalues as the signal subspace, and using other eigenvalues in the sorted eigenvalues as the noise subspace.
  • the spatial smoothing technique is an effective method to deal with coherent or strongly correlated signals.
  • the basic idea is that the equidistant linear array is divided into several overlapping sub-arrays, and the sub-array covariance matrices can be added and averaged to replace the original ones.
  • the covariance matrix R in the sense.
  • R b and R f are conjugate inverse order matrix, and there is a conjugate inverse order invariance between them, so the forward and backward smooth covariance matrix can be defined as:
  • the quantized and sampled echo signals are input into the forward and backward smoothed covariance matrix for eigenvalue decomposition.
  • the eigenvalue decomposition is performed on the forward and backward smooth covariance matrix, and the expression is as follows:
  • the eigenvectors corresponding to the largest eigenvalue ⁇ equal to the number of signals K are regarded as U 1 , U 2 ,..., U K as the signal subspace U s , and the remaining (MK)
  • the eigenvectors U K+1 , U K+2 ,..., U M corresponding to the eigenvalues are regarded as the noise subspace U N , and the eigenvalue decomposition process is as follows
  • ⁇ S diag( ⁇ 1 , ⁇ 2 ,..., ⁇ K ) is a diagonal matrix of K larger eigenvalues
  • ⁇ N diag( ⁇ K+1 , ⁇ K+2 ,.. ., ⁇ M ) is a diagonal matrix consisting of MK eigenvalues.
  • Step S330 using the signal subspace and the noise subspace to perform a peak search of the spatial spectrum to obtain the direction of arrival of the echo signal.
  • step S330 includes:
  • Step S331 constructing a spatial spectral function based on the orthogonal relationship between the signal subspace and the noise subspace;
  • step S332 a peak value is obtained for the spatial spectral function, and the obtained peak value is used as the estimated value of the direction of arrival.
  • a spatial spectral function is constructed by using the signal subspace and the noise subspace, and then a peak value is obtained for the spatial spectral function, and the obtained peak value is used as the estimated value of the direction of arrival. use to calculate the spectral function, and obtain an estimate of the direction of arrival by seeking the peak value.
  • Bit sampling is also the same as traditional high-precision sampling (12bit or 16bit, etc.), which can well preserve the phase information of the signal while avoiding the introduction of noise interference, so that one-bit quantization can be used to reduce the low data volume of the array radar.
  • the coherent signal DOA estimation Data volume reducing the cost of data acquisition, transmission and storage.
  • the DOA estimation method for coherent signals with low data volume transmits radar signals, receives echo signals reflected by the target, and then performs single-frequency time-varying threshold one-bit quantization sampling on the echo signals to obtain the quantized sampling Then, based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal, the direction of arrival of the echo signal is determined.
  • the single-frequency time-varying threshold one-bit quantized sampling reduces the amount of data in the process of radar echo data acquisition, storage, transmission, and processing, and saves system costs.
  • the forward and backward spatial smoothing algorithm is adopted to convert the rank of covariance of coherent signals. Recovering to the number of target objects realizes decorrelation and improves the accuracy of DOA estimation of coherent signals with low data amount.
  • a second embodiment of the method for estimating the DOA of a low-data-amount coherent signal of the present application is proposed.
  • the method includes:
  • Step S400 dividing the antenna array of the millimeter-wave array radar into a plurality of overlapping sub-arrays, and respectively determining the covariance matrix of the forward sub-array and the covariance matrix of the backward sub-array corresponding to each sub-array;
  • Step S500 Determine the forward and backward smoothed covariance matrix based on the covariance matrix of each forward sub-array and the covariance matrix of the backward sub-array.
  • the spatial smoothing technique is an effective method to deal with coherent or strongly correlated signals.
  • the basic idea is that the equidistant linear array is divided into several overlapping sub-arrays, and the sub-array covariance matrices can be added and averaged to replace the original ones.
  • the covariance matrix R in the sense.
  • the covariance matrix of the h-th forward subarray is:
  • R b and R f are conjugate inverse order matrix, and there is a conjugate inverse order invariance between them, so the forward and backward smooth covariance matrix can be defined as:
  • the antenna array of the millimeter-wave array radar is divided into multiple overlapping sub-arrays, and the covariance matrices of the forward sub-arrays corresponding to each sub-array are determined respectively. and the covariance matrix of the backward subarray, and then determine the forward and backward smoothed covariance matrix based on the covariance matrix of each forward subarray and the covariance matrix of the backward subarray.
  • FIG. 11 is a schematic diagram of functional modules of an embodiment of the low-data-volume coherent signal DOA estimation apparatus of the present application.
  • the transceiver module 10 is used for transmitting radar signals and receiving echo signals corresponding to the radar signals reflected by the target;
  • a sampling module 20 configured to perform single-frequency time-varying threshold one-bit quantization sampling on the echo signal to obtain a quantized and sampled echo signal;
  • the processing module 30 is configured to determine the direction of arrival of the echo signal based on the forward and backward spatial smoothing algorithm and the quantized and sampled echo signal.
  • sampling module 20 is also used for:
  • One-bit quantization sampling is performed on the echo signal by using the sampling signal to obtain the quantized and sampled echo signal.
  • processing module 30 is also used for:
  • the signal subspace and the noise subspace are constructed based on the eigenvectors corresponding to the eigenvalues obtained after decomposition;
  • the spatial spectrum peak search is performed using the signal subspace and the noise subspace to obtain the direction of arrival of the echo signal.
  • processing module 30 is also used for:
  • the eigenvectors corresponding to the eigenvalues obtained after the decomposition are sorted, and the signal subspace and the noise subspace are constructed based on the sorted eigenvectors.
  • processing module 30 is also used for:
  • the eigenvectors with the same number of objects are selected as the signal subspace, and the other eigenvectors in the sorted eigenvalues are used as the noise subspace.
  • processing module 30 is also used for:
  • a peak value is obtained for the spatial spectral function, and the obtained peak value is used as the estimated value of the direction of arrival.
  • the low-data-amount coherent signal DOA estimation device further includes:
  • the dividing module is used to divide the antenna array of the millimeter wave array radar into a plurality of overlapping sub-arrays, and respectively determine the covariance matrix of the forward sub-array and the covariance matrix of the backward sub-array corresponding to each sub-array ;
  • the determining module is configured to determine the forward and backward smoothed covariance matrix based on the covariance matrix of each forward sub-array and the covariance matrix of the backward sub-array.
  • an embodiment of the present application further proposes a readable storage medium, where a low-data-volume coherent signal DOA estimation program is stored, and the low-data-volume coherent signal DOA estimation program is executed by a processor to implement the above-mentioned The steps of the DOA estimation method for low data volume coherent signals in various embodiments.

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Abstract

一种低数据量相干信号DOA估计方法、装置、设备及可读存储介质，该方法包括：发射雷达信号，并接收目标物反射的回波信号，而后对回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号，接下来基于前后向空间平滑算法以及量化采样后的回波信号，确定回波信号的波达方向。

Description

低数据量相干信号DOA估计方法、装置、设备及介质
优先权信息
本申请要求于2020年7月9日申请的、申请号为202010659196.0的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及数据采集和信号处理领域，尤其涉及一种低数据量相干信号DOA估计方法、装置、设备及可读存储介质。
背景技术
毫米波阵列雷达具有全天候、全天时的远距离、高分辨探测能力，在先进驾驶辅助系统(ADAS)、盲点监测、辅助变道、无人机定高与盲检、无人船航行、人体生命特征检测等多领域发挥着重要的作用。但随着阵列雷达接收阵元(通道)数目的增加以及信号带宽的不断增加，其数据采集、传输、处理的负担也在不断地加大。例如根据奈奎斯特采样定理，采样一个1G赫兹带宽的雷达一个通道的回波信号，需要2G赫兹/秒的采样率，如果每个信号采样量化为16比特，则ADC速率为4GB/秒，一分钟就可以存满一个中等大小的硬盘，如果是多个接收通道的阵列，雷达会导致ADC采样数据量更大。
进一步地，由于传播环境的复杂性，入射到阵列的信号中有相干信号源的存在，包括同频干扰和由于背景物体反射的多径传播信号，从而无法定位产生同频干扰的多个目标各自的DOA。
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。
发明内容
本申请的主要目的在于提供一种低数据量相干信号DOA估计方法、装置、设备及可读存储介质，旨在解决现有低数据量相干信号DOA估计算法的精度低且数据采集的数据量大的技术问题。
为实现上述目的，本申请提供一种低数据量相干信号DOA估计方法，所述的低数据量相干信号DOA估计方法包括以下步骤：
发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
进一步地，所述对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号的步骤包括：
基于所述回波信号的频率确定采样信号的频率，并基于所述回波信号的幅度确定所述采样信号的幅度；
利用所述采样信号对所述回波信号进行一比特量化采样，得到所述量化采样后的回波信号。
进一步地，所述基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向的步骤包括：
基于量化采样后的回波信号构建协方差矩阵，并对所述协方差矩阵进行特征值分解；
基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间；
利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向。
进一步地，所述基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间的步骤包括：
对所述分解后得到的特征值对应的特征向量进行排序，基于所述排序后的特征向量构建所述信号子空间和所述噪声子空间。
进一步地，所述基于所述排序后的特征向量构建所述信号子空间和所述 噪声子空间的步骤包括：
在排序后的特征向量中选取与目标物数量相同的特征向量作为信号子空间，将排序后的特征值中其他特征向量作为噪声子空间。
进一步地，所述利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向的步骤包括：
基于所述信号子空间和所述噪声子空间的正交关系构造空间谱函数；
对所述空间谱函数求峰值，将得到的峰值作为所述波达方向的估计值。
进一步地，所述毫米波阵列雷达的天线阵列为均匀线阵，所述发射雷达信号，并接收目标物反射的雷达信号对应的回波信号的步骤之前包括：
将所述毫米波阵列雷达的天线阵列分为多个相重叠的子阵列，并分别确定各个子阵列对应的前向子阵的协方差矩阵和后向子阵的协方差矩阵；
基于各个前向子阵的协方差矩阵和后向子阵的协方差矩阵确定前后向平滑协方差矩阵。
进一步地，所述低数据量相干信号DOA估计装置包括：
收发模块，用于发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
采样模块，用于对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
处理模块，用于基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
此外，为实现上述目的，本申请还提供一种设备，所述设备为毫米波阵列雷达，所述设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的低数据量相干信号DOA估计程序，所述低数据量相干信号DOA估计程序被所述处理器执行时实现上述任一项所述的低数据量相干信号DOA估计方法的步骤。
此外，为实现上述目的，本申请还提供一种可读存储介质，所述可读存储介质上存储有低数据量相干信号DOA估计程序，所述低数据量相干信号DOA估计程序被处理器执行时实现上述任一项所述的低数据量相干信号DOA估计方法的步骤。
本申请发射雷达信号，并接收目标物反射的回波信号，而后对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号，接下来基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。通过单频时变阈值一比特量化采样降低雷达的回波数据采集、存储、传输、处理等过程中的数据量，节约了系统成本，采取了前后向空间平滑算法，将相干信号协方差的秩恢复到目标物个数，实现了去相关，提高低数据量相干信号DOA估计的精度。
附图说明
图1是本申请实施例方案涉及的硬件运行环境中设备的结构示意图；
图2为本申请基于低数据量相干信号DOA估计方法第一实施例的流程示意图；
图3为本申请基于低数据量相干信号DOA估计方法一实施例中均匀线阵的示意图；
图4为本申请基于低数据量相干信号DOA估计方法一实施例中相干LFMCW信号的示意图；
图5为本申请基于低数据量相干信号DOA估计方法一实施例中单频时变阈值信号的示意图；
图6为本申请基于低数据量相干信号DOA估计方法一实施例中回波LFMCW信号单频时变阈值一比特采样量化后的示意图；
图7为本申请基于低数据量相干信号DOA估计方法一实施例中前后向空间平滑的示意图；
图8为本申请基于低数据量相干信号DOA估计方法一实施例中单频时变阈值一比特采样量化后的前后向空间平滑低数据量相干信号DOA估计的示意图；
图9为本申请基于低数据量相干信号DOA估计方法一实施例中相干LFMCW信号低数据量相干信号DOA估计结果对比的示意图；
图10为本申请基于低数据量相干信号DOA估计方法第二实施例的流程示意图；
图11为本申请基于低数据量相干信号DOA估计装置实施例的功能模块示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
如图1所示，图1是本申请实施例方案涉及的硬件运行环境中设备的结构示意图。
如图1所示，该设备可以包括：处理器1001，例如CPU，网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)，可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM存储器，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
本领域技术人员可以理解，图1中示出的设备结构并不构成对设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，作为一种存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及基于低数据量相干信号DOA估计程序。
在图1所示的设备中，网络接口1004主要用于连接后台服务器，与后台服务器进行数据通信；用户接口1003主要用于连接用户端，与用户端进行数据通信；而处理器1001可以用于调用存储器1005中存储的基于低数据量相 干信号DOA估计程序。
在本实施例中，设备包括：存储器1005、处理器1001及存储在所述存储器1005上并可在所述处理器1001上运行的基于低数据量相干信号DOA估计程序，其中，处理器1001调用存储器1005中存储的基于低数据量相干信号DOA估计程序时，执行本申请各个实施例提供的基于低数据量相干信号DOA估计方法的步骤
本申请还提供一种低数据量相干信号DOA估计方法，参照图2，图2为本申请低数据量相干信号DOA估计方法第一实施例的流程示意图。
本申请实施例提供了低数据量相干信号DOA估计方法的实施例，需要说明的是，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
在本实施例中，该低数据量相干信号DOA估计方法包括：
步骤S100，发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
在本实施例中，雷达是用无线电的方法发现目标并测定它们的空间位置。接收和发射波束均以数字方式形成的全数字化阵列天线雷达就称作数字阵列雷达，数字阵列雷达一般由天线阵列、数字发射/接收(T/R)组件、时钟、数据传输系统、数字处理机组成。雷达按照发射信号种类分成脉冲雷达和连续波雷达两大类，常规脉冲雷达发射周期性的高频脉冲，连续波雷达发射的是连续波信号。本申请的雷达为毫米波阵列雷达，且天线阵列为均匀线阵，如图3所示，两两天线阵子之间的距离d都相等，并且采用的是线性调频连续波信号，其缩写为LFMCW。
进一步地，当多个目标物在同一距离上时，此时反射回来反射信号中的回波信号的时间延迟是一样的，从而会导致回波信号由于反射时间一样而出现同频相干信号。对于存在相干信号的情况，如果采用现有的低数据量相干信号DOA估计算法，如传统的MUSIC、ESPRIT等信号子空间算法不能有效分辨回波信号DOA，其中，DOA是电子、通信、雷达、声呐等研究领域的行业内用语，通过处理接收到的回波信号，获取目标的距离信息和方位信息。本申请提出的低数据量相干信号DOA估计方法通过单频时变阈值一比特量化采样降低雷达回波数据，降低了数据采集、存储、传输、处理等过程中的 数据量，节约了系统成本，采取了前后向空间平滑算法，提高低数据量相干信号DOA估计的精度。
具体地，毫米波阵列雷达采用线性调频连续波LFMCW来发射雷达信号，目标物反射的雷达信号对应的反射信号X可以公式一简单表示为：
公式一：X＝AS+N
其中，A为阵列相应矩阵，S是回波信号，N表示阵列噪声信号。
进一步地，反射信号X(t)可以用公式二表示为：
Figure PCTCN2021098993-appb-000001
在公式二中，M表示均匀的阵元数目，N表示快拍数，K表示目标物个数,注意信源个数的要求是K<M，θ K(k＝1,2,3…K)表示第k个目标的来波方向入射M根线。运用矩阵的定义，可得到公式一：X＝AS+N中各参数具体为：
X＝[x 1(t),x 2(t),…x M(t)] M×N T
S＝[S 1(t),S 2(t),…S K(t)] K×N T
A＝[a(θ 1),a(θ 2),…a(θ K)] M×K
N＝[n 1(t),n 2(t),…n M(t)] M×N T
来波方向为θ K(k＝1,2,3…K)的第k个目标的回波信号入射两个阵元间的相位差为
Figure PCTCN2021098993-appb-000002
d表示均匀线阵中的阵元间距，λ表示阵列雷达发射的线性调频信号的波长，其中λ＝c/f c，其中c表示光速，f c表示阵列雷达发射的线性调频信号的中心频率。如图3所示，阵元间距为d的均匀线阵的阵列响应矩阵为
Figure PCTCN2021098993-appb-000003
步骤S200，对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
具体地，步骤S200包括：
步骤S210，基于所述回波信号的频率确定采样信号的频率，并基于所述回波信号的幅度确定所述采样信号的幅度；
步骤S220，利用所述采样信号对所述回波信号进行一比特量化采样，得到所述量化采样后的回波信号。
在本实施例中，目标物经过时延τ反射回来的回波信号用S(t)表示：
Figure PCTCN2021098993-appb-000004
A为目标回波信号最大幅度值，k为线性调频信号的调频斜率，其中k＝B/T，B为信号带宽，T为信号脉冲宽度，f c为中心频率，
Figure PCTCN2021098993-appb-000005
表示相乘。τ为回波信号相对于发射信号时间延迟。如果τ一致，将会产生同频相干LFMCW信号。根据表1配置各参数，参照图4，得到相干LFMCW信号。
Figure PCTCN2021098993-appb-000006
表1
对毫米波阵列雷达的每个通道的相干LFMCW回波信号进行单频时变阈值一比特量化采样，通过将回波数据与时变阈值比较，将其量化为一比特量采样数据，从而降低雷达信号处理回波数据的位宽，达到简化系统、提升效率的目的。传统的一比特量量化将信号与0阈值比较，这将造成信号相对幅 度的非线性失真，影响雷达信号处理的质量，随机时变阈值虽然能够保留幅度信息，却会引入额外的类噪声干扰。单频时变阈值将能够有效地保留一比特量采样量化中丢失的相对幅度信息，同时避免引入类噪声干扰，有效地提高了一比特量采样量化下的相干LFMCW信号的低数据量相干信号DOA估计的质量。
对LFMCW信号回波进行一比特量采样量化，其中采样阈值为频率为f 0、幅度为A s的单频信号，表达式为：
Figure PCTCN2021098993-appb-000007
其中，
Figure PCTCN2021098993-appb-000008
为阈值的初相。在不同的脉冲重复间隔内，可以采用固定初相以最大程度简化阈值存储参数，或采用随机初相以降低时变阈值的相干特性。需要说明的是，单频时变阈值可以由模拟振荡器直接生成，其产生的成本与复杂度远小于需要预先计算、高精度存储、实时查表重现的随机时变阈值。单频时变阈函数的参数设置如表2所示，相位设置为随机初相，目的是为了降低时变阈值的相干特性，产生的时变阈值函数如图5所示。
Figure PCTCN2021098993-appb-000009
表2
令φ和ψ为如下表达式：
φ＝2πf c(t-τ)+πk(t-τ) 2，
Figure PCTCN2021098993-appb-000010
则基于单频时变阈值的LFMCW回波1-bit采样量化过程可以表述为：
S(t)＝sign[S(t)+h s(t)]＝sign(A cosφ+A s cosψ)+jsign(Asinφ+A s sinψ)
其中，sign表示符号函数，由于低数据量相干信号DOA估计需要利用LFMCW信号的相位信息，通常采集的数据为复数，因此一比特量采样量化的过程也需要分别针对数据的实部与虚部进行，产生的实部和虚部进行一比特量采样量化后的结果如图6所示，可以看到回波LFMCW信号全部被采样量化为+1和-1，完成了一比特采样量化。
需要说明的是，用于一比特采样量化的单频信号，其幅度A s可设定为采样信号的幅度平均值；其频率f 0一般取小于0.5倍LFMCW信号的带宽，主 要是为了降低量化采样后的数据量，因为f 0的取值与A s一样，会影响到量化采样后的数据量大小，在相同的时间内，频率f 0越大，则数据量就会更大，对设备的要求越高，因此，在满足一比特量化采样的条件下，尽可能将f 0的值设小一些，一般取小于0.5倍带宽的大小。
步骤S300，基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
具体地，步骤S300包括：
步骤S310，基于量化采样后的回波信号构建协方差矩阵，并对所述协方差矩阵进行特征值分解；
步骤S320，基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间；
具体地，步骤S320包括：
步骤S321，对所述分解后得到的特征值对应的特征向量进行排序，基于所述排序后的特征向量构建所述信号子空间和所述噪声子空间。
具体地，步骤S321包括：在排序后的特征值中选取与目标物数量相同的特征值作为信号子空间，将排序后的特征值中其他特征值作为噪声子空间。
在本实施例中，空间平滑技术是对付相干或者强相关信号的的有效方法，其基本思想是等距线阵分成若干个相重叠的子阵列，子阵协方差矩阵可以相加后平均取代原来意义上的协方差矩阵R。例如将M个的等距线阵用滑动方式分成Q个子阵，每个子阵有N个阵元，其中N＝M-Q+1，符号f表示前向，定义前向空间平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000011
同理符号b表示后向，定义后向空间平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000012
其实R b和R f的关系是共轭倒序阵，他们之间具有共轭倒序不变性，因此可以定义前后向平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000013
将量化采样后的回波信号输入前后向平滑协方差矩阵，进行特征值分解。 具体地，对前后向平滑协方差矩阵进行特征值分解，表达式如下：
R＝UΣU H
其中，Σ＝diag(λ 1,λ 2,...,λ M)，λ 1≥λ 2≥…≥λ K≥λ K+1…≥λ M＝σ 2，其中diag表示对角矩阵，表示高斯白噪声的噪声功率示对角矩阵，σ 2表示高斯白噪声的噪声功率。
按照特征值的大小顺序，把与信号个数K相等的最大特征值λ对应的特征向量看U 1,U 2,...,U K做信号子空间U s，把剩下的(M-K)个特征值对应的特征向量U K+1,U K+2,...,U M看做噪声子空间U N，则特征值分解过程如下
R＝U SΣ SU S H+U NΣ NU N H
其中Σ S＝diag(λ 1,λ 2,...,λ K)是K个较大特征值构成的对角矩阵，而后Σ N＝diag(λ K+1,λ K+2,...,λ M)是由M-K个特征值构成的对角矩阵。
步骤S330，利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向。
具体地，步骤S330包括：
步骤S331，基于所述信号子空间和所述噪声子空间的正交关系构造空间谱函数；
步骤S332，对所述空间谱函数求峰值，将得到的峰值作为所述波达方向的估计值。
在本实施例中，利用信号子空间和噪声子空间构造空间谱函数，然后对空间谱函数求峰值，将得到的峰值作为所述波达方向的估计值。用
Figure PCTCN2021098993-appb-000014
来计算谱函数，通过寻求峰值来得到波达方向的估计值。
可以看到单频时变阈值一比特采样量化后的前后向空间平滑低数据量相干信号DOA估计峰值搜索的结果如图8，观察图中实线可以发现经过前后向空间平滑后，相干LFMCW信号已经可以很好估计出信号源的波达方向。对比图8中实线和虚线发现，经过一比特采样的数据与未经过一比特采样的数据进行低数据量相干信号DOA估计后得到的角度信息几乎一致，这也证明了单频时变阈值一比特采样也和传统的高精度采样(12bit或者16bit等)一样，可以很好保留信号的相位信息，同时避免引入噪声干扰，从而可以采用一比特 采用量化降低阵列雷达的低数据量相干信号DOA估计数据量，降低数据采集，传输和存储的成本。
从图9中的对比效果可知，对宽带相干LFMCW信号而言普通MUSIC等子空间类方法失效的问题，在当相干信号的个数大于等于3时，传统的基于矩阵重构解相干的MUSIC算法实现相干LFM的低数据量相干信号DOA估计的方法也失效。只有前后向空间平滑的低数据量相干信号DOA估计质量较好，且进行一比特采样量化后的效果与未进行一比特量化前的非常接近，所以说基于单频时变阈值一比特采样量化的引入不但没有降低低数据量相干信号DOA估计的质量，还可以大幅降低系统成本。
本实施例提出的低数据量相干信号DOA估计方法，发射雷达信号，并接收目标物反射的回波信号，而后对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号，接下来基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。通过单频时变阈值一比特量化采样降低雷达的回波数据采集、存储、传输、处理等过程中的数据量，节约了系统成本，采取了前后向空间平滑算法，将相干信号协方差的秩恢复到目标物个数，实现了去相关，提高低数据量相干信号DOA估计的精度。
基于第一实施例，参照图10，提出本申请低数据量相干信号DOA估计方法的第二实施例，在本实施例中，步骤S100之前，包括：
步骤S400，将所述毫米波阵列雷达的天线阵列分为多个相重叠的子阵列，并分别确定各个子阵列对应的前向子阵的协方差矩阵和后向子阵的协方差矩阵；
步骤S500，基于各个前向子阵的协方差矩阵和后向子阵的协方差矩阵确定前后向平滑协方差矩阵。
在本实施例中，空间平滑技术是对付相干或者强相关信号的的有效方法，其基本思想是等距线阵分成若干个相重叠的子阵列，子阵协方差矩阵可以相加后平均取代原来意义上的协方差矩阵R。如图7所示，将M个的等距线阵用滑动方式分成Q个子阵，每个子阵有N个阵元，其中N＝M-Q+1，定义第h个前向子阵的输出为
Figure PCTCN2021098993-appb-000015
其中，A M为N×K维的方向矩阵，其列为N维的导向向量a M(θ i)(i＝1,2，…,K)
Figure PCTCN2021098993-appb-000016
所以，第h个前向子阵的协方差矩阵为：
Figure PCTCN2021098993-appb-000017
其中符号f表示前向，定义前向空间平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000018
同理符号b表示后向，定义后向空间平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000019
其实R b和R f的关系是共轭倒序阵，他们之间具有共轭倒序不变性，因此可以定义前后向平滑协方差矩阵为：
Figure PCTCN2021098993-appb-000020
本实施例提出的低数据量相干信号DOA估计方法，将所述毫米波阵列雷达的天线阵列分为多个相重叠的子阵列，并分别确定各个子阵列对应的前向子阵的协方差矩阵和后向子阵的协方差矩阵，而后基于各个前向子阵的协方差矩阵和后向子阵的协方差矩阵确定前后向平滑协方差矩阵。通过采用前后向空间平滑，而不是仅仅的前向或者后向，利用前向和后向的共轭倒叙不变性的优点，可以增加子阵的数目，从而提高低数据量相干信号DOA估计的精度。
本申请进一步提供一种低数据量相干信号DOA估计装置，参照图11，图11为本申请低数据量相干信号DOA估计装置实施例的功能模块示意图。
收发模块10，用于发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
采样模块20，用于对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
处理模块30，用于基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
进一步地，所述采样模块20还用于：
基于所述回波信号的频率确定采样信号的频率，并基于所述回波信号的幅度确定所述采样信号的幅度；
利用所述采样信号对所述回波信号进行一比特量化采样，得到所述量化采样后的回波信号。
进一步地，所述处理模块30还用于：
基于量化采样后的回波信号构建协方差矩阵，并对所述协方差矩阵进行特征值分解；
基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间；
利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向。
进一步地，所述处理模块30还用于：
对所述分解后得到的特征值对应的特征向量进行排序，基于所述排序后的特征向量构建所述信号子空间和所述噪声子空间。
进一步地，所述处理模块30还用于：
在排序后的特征向量中选取与目标物数量相同的特征向量作为信号子空间，将排序后的特征值中其他特征向量作为噪声子空间。
进一步地，所述处理模块30还用于：
基于所述信号子空间和所述噪声子空间的正交关系构造空间谱函数；
对所述空间谱函数求峰值，将得到的峰值作为所述波达方向的估计值。
进一步地，所述低数据量相干信号DOA估计装置还包括：
分割模块，用于将所述毫米波阵列雷达的天线阵列分为多个相重叠的子阵列，并分别确定各个子阵列对应的前向子阵的协方差矩阵和后向子阵的协方差矩阵；
确定模块，用于基于各个前向子阵的协方差矩阵和后向子阵的协方差矩阵确定前后向平滑协方差矩阵。
此外，本申请实施例还提出一种可读存储介质，所述可读存储介质上存储有低数据量相干信号DOA估计程序，所述低数据量相干信号DOA估计程序被处理器执行时实现上述各个实施例中低数据量相干信号DOA估计方法的步骤。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个可读存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台系统设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (10)

  1. 一种低数据量相干信号DOA估计方法，应用于毫米波阵列雷达，其中，所述低数据量相干信号DOA估计方法包括：
    发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
    对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
    基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
  2. 如权利要求1所述的低数据量相干信号DOA估计方法，其中，所述对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号的步骤包括：
    基于所述回波信号的频率确定采样信号的频率，并基于所述回波信号的幅度确定所述采样信号的幅度；
    利用所述采样信号对所述回波信号进行一比特量化采样，得到所述量化采样后的回波信号。
  3. 如权利要求1所述的低数据量相干信号DOA估计方法，其中，所述基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向的步骤包括：
    基于量化采样后的回波信号构建协方差矩阵，并对所述协方差矩阵进行特征值分解；
    基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间；
    利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向。
  4. 如权利要求3所述的低数据量相干信号DOA估计方法，其中，所述基于分解后得到的特征值对应的特征向量构建信号子空间和噪声子空间的步骤包括：
    对所述分解后得到的特征值对应的特征向量进行排序，基于所述排序后的特征向量构建所述信号子空间和所述噪声子空间。
  5. 如权利要求4所述的低数据量相干信号DOA估计方法，其中，所述基于所述排序后的特征向量构建所述信号子空间和所述噪声子空间的步骤包括：
    在排序后的特征向量中选取与目标物数量相同的特征向量作为信号子空间，将排序后的特征值中其他特征向量作为噪声子空间。
  6. 如权利要求3所述的低数据量相干信号DOA估计方法，其中，所述利用所述信号子空间和噪声子空间进行空间谱峰值搜索，得到所述回波信号的波达方向的步骤包括：
    基于所述信号子空间和所述噪声子空间的正交关系构造空间谱函数；
    对所述空间谱函数求峰值，将得到的峰值作为所述波达方向的估计值。
  7. 如权利要求1至6中任一项所述的低数据量相干信号DOA估计方法，其中，所述毫米波阵列雷达的天线阵列为均匀线阵，所述发射雷达信号，并接收目标物反射的雷达信号对应的回波信号的步骤之前包括：
    将所述毫米波阵列雷达的天线阵列分为多个相重叠的子阵列，并分别确定各个子阵列对应的前向子阵的协方差矩阵和后向子阵的协方差矩阵；
    基于各个前向子阵的协方差矩阵和后向子阵的协方差矩阵确定前后向平滑协方差矩阵。
  8. 一种低数据量相干信号DOA估计装置，其中，所述低数据量相干信号DOA估计装置包括：
    收发模块，用于发射雷达信号，并接收目标物反射的雷达信号对应的回波信号；
    采样模块，用于对所述回波信号进行单频时变阈值一比特量化采样，得到量化采样后的回波信号；
    处理模块，用于基于前后向空间平滑算法以及所述量化采样后的回波信号，确定所述回波信号的波达方向。
  9. 一种设备，其中，所述设备为毫米波阵列雷达，所述设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的低数据量相干信号DOA估计程序，所述低数据量相干信号DOA估计程序被所述处理器执行时实现如权利要求1至7中任一项所述的低数据量相干信号DOA估计方法的步骤。
  10. 一种可读存储介质，其中，所述可读存储介质上存储有所述低数据量相干信号DOA估计程序，所述低数据量相干信号DOA估计程序被处理器执行时实现如权利要求1至7中任一项所述的低数据量相干信号DOA估计方法的步骤。
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