WO2022027241A1 - 信号处理方法和装置 - Google Patents

信号处理方法和装置 Download PDF

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Publication number
WO2022027241A1
WO2022027241A1 PCT/CN2020/106864 CN2020106864W WO2022027241A1 WO 2022027241 A1 WO2022027241 A1 WO 2022027241A1 CN 2020106864 W CN2020106864 W CN 2020106864W WO 2022027241 A1 WO2022027241 A1 WO 2022027241A1
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Prior art keywords
signals
data
doppler
fft
range
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PCT/CN2020/106864
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English (en)
French (fr)
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朱金台
劳大鹏
李德建
刘劲楠
王犇
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华为技术有限公司
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Priority to PCT/CN2020/106864 priority Critical patent/WO2022027241A1/zh
Priority to EP20948325.4A priority patent/EP4184797B1/en
Priority to CN202080005160.9A priority patent/CN112740069B/zh
Publication of WO2022027241A1 publication Critical patent/WO2022027241A1/zh
Priority to US18/164,398 priority patent/US20230184886A1/en

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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
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • 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
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/288—Coherent receivers
    • G01S7/2883—Coherent receivers using FFT processing
    • 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
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/356—Receivers involving particularities of FFT processing
    • 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
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/10—Systems for measuring distance only using transmission of interrupted, pulse modulated waves
    • G01S13/26—Systems for measuring distance only using transmission of interrupted, pulse modulated waves wherein the transmitted pulses use a frequency- or phase-modulated carrier wave
    • 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
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S13/34—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
    • G01S13/343—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal using sawtooth modulation
    • 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
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • 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
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/581—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets
    • G01S13/582—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements
    • 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
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/583—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
    • G01S13/584—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements

Definitions

  • the present application relates to the field of communications, and in particular, to a signal processing method and apparatus.
  • Radar detection is widely used in various fields, such as advanced driver assistance systems (ADAS), unmanned driving, etc. With the development of technology, higher range resolution and higher angular resolution are required for radar detection.
  • ADAS advanced driver assistance systems
  • MIMO radars can improve range resolution by transmitting more bandwidth, and achieve higher angular resolution by setting up more multiple input multiple output (MIMO) arrays.
  • a radar provided with a MIMO array may be referred to as a MIMO radar.
  • MIMO radar can support a large number of transmitting antennas to transmit in turn, but because MIMO radar uses antennas to transmit in turn, the relative pulse repetition interval (PRI) of a single antenna is relatively long, which reduces the detection of target movement speed by MIMO radar.
  • PRI pulse repetition interval
  • the speed of the target beyond the maximum speed detection range of the MIMO radar is aliased into the detection range, causing the speed of the target to be blurred.
  • the industry has also proposed a single input multiple output (simple input multiple output, SIMO) + MIMO radar detection mechanism.
  • the radar can work either in MIMO mode or in SIMO mode.
  • the radar can detect targets in the SIMO mode and the MIMO mode respectively, and based on the difference in the PRI period of the SIMO mode and the MIMO mode, the signal acquired in the SIMO mode is used to perform velocity matching and de-ambiguity processing on the signal acquired in the MIMO mode.
  • the accuracy of velocity-matching de-blurring of signals acquired in SIMO+MIMO mode still faces challenges.
  • the present application provides a signal processing method and device, which can improve the accuracy of speed matching and deblurring processing on signals in SIMO+MIMO mode.
  • a first aspect provides a signal processing method, comprising: acquiring Nr1 ⁇ M1 signals, where the Nr1 ⁇ M1 signals are echo signals of M1 signals sent by a radar to a target in a SIMO mode, the SIMO mode Corresponding to 1 transmit channel and Nr1 receive channels, Nr1 and M1 are integers greater than 1; obtain Nt ⁇ Nr2 ⁇ M2 signals, the Nt ⁇ Nr2 ⁇ M2 signals are sent by the radar to the target in the MIMO mode
  • the echo signals of M2 signals, the MIMO mode corresponds to Nt transmission channels and Nr2 reception channels, and Nt, Nr2, and M2 are integers greater than 1; perform first signal processing on the Nr1 ⁇ M1 signals to obtain Acquiring first processing data, the first signal processing includes performing the following processing in sequence: range FFT analysis, linear prediction and Doppler FFT analysis, where the linear prediction is used to predict the time domain of the FFT data obtained by the range FFT analysis or later FFT data; perform second signal processing on the Nt ⁇ Nr2 ⁇ M2 signals
  • the above-mentioned linear prediction is used to predict the FFT data before or after the time domain of the FFT data obtained through the distance FFT analysis, which means that the time domain signal corresponding to the predicted FFT data is the FFT obtained through the distance FFT analysis.
  • the signal before or after the time domain signal of the data is used to predict the FFT data before or after the time domain of the FFT data obtained through the distance FFT analysis.
  • the above-mentioned FFT data obtained through distance FFT analysis may be subjected to linear prediction after other types of signal processing (eg, coherent superposition of receiving channels).
  • the number of signals M1 transmitted by SIMO is much smaller than the number Nt ⁇ M2 of signals transmitted by the MIMO radar, and the speed resolution ⁇ v and speed measurement accuracy ⁇ v measured in the SIMO mode are poorer than those in the MIMO mode, or
  • the ⁇ v and ⁇ v ranges of targets acquired in SIMO mode and MIMO mode, respectively, do not match, which adversely affects the accuracy of velocity-matched deblurring. Therefore, the solution of the present application performs first signal processing including linear prediction on the echo signal obtained in the SIMO mode to obtain first processed data.
  • the velocity resolution ⁇ v and the velocity measurement accuracy ⁇ v of the target acquired in the SIMO mode can be improved, so that the velocity of the target acquired in the SIMO mode and the MIMO mode respectively can be improved.
  • the range of the resolution ⁇ v and the velocity measurement accuracy ⁇ v is closer, which is beneficial to improve the accuracy of the velocity matching and de-blurring of the signals obtained according to the SIMO mode and the MIMO mode.
  • the first signal processing includes sequentially performing the following processing: range FFT analysis, coherent superposition of receive channels, linear prediction, Doppler FFT analysis, and CFAR.
  • the FFT data corresponding to different receiving channels are coherently superimposed, and the superimposed FFT data is used for linear prediction, and then based on the linearly predicted data.
  • Velocity matching defuzzification Linear prediction can improve the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode, so that the range of velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode and MIMO mode respectively is closer, It is beneficial to improve the precision of speed matching de-blurring for signals obtained according to the SIMO mode and the MIMO mode.
  • the first signal processing includes sequentially performing the following processing: range FFT analysis, linear prediction, Doppler FFT analysis, signal superposition, and CFAR.
  • the FFT data corresponding to different receiving channels are linearly predicted, and the velocity matching defuzzification is performed based on the FFT data obtained by the linear prediction.
  • Linear prediction can improve the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of targets acquired in SIMO mode, so that the ranges of velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of targets acquired in SIMO mode and MIMO mode respectively are closer, It is beneficial to improve the precision of speed matching de-blurring for signals obtained according to the SIMO mode and the MIMO mode.
  • the second signal processing further includes performing the following processing in sequence: range FFT analysis, Doppler FFT analysis, signal superposition, and CFAR.
  • performing first signal processing on the Nr1 ⁇ M1 signals to obtain first processed data includes: performing distance FFT on the Nr1 ⁇ M1 signals Analyze, and obtain Nr1 ⁇ M1 1-dimensional FFT data; perform coherent superposition of the receiving channel on the Nr1 ⁇ M1 1-dimensional FFT data to obtain M1 1-dimensional FFT data; perform linear prediction on the M1 1-dimensional FFT data , obtain M1+Y 1-dimensional FFT data, Y is a positive integer; perform Doppler FFT analysis on the M1+Y 1-dimensional FFT data, and obtain a range-Doppler spectrogram; CFAR detection is performed on the range-Doppler spectrogram to obtain the first processed data.
  • the FFT data corresponding to multiple receiving channels are coherently superimposed, and the superimposed FFT data is linearly predicted.
  • the data is processed to obtain the first processed data. Due to the linear prediction, the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode can be improved, so that the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode and MIMO mode are The range is closer, which is beneficial to improve the accuracy of speed deblurring of signals acquired according to SIMO mode and MIMO mode.
  • performing first signal processing on the Nr1 ⁇ M1 signals to obtain first processed data includes: performing distance FFT on the Nr1 ⁇ M1 signals Analyze, and obtain Nr1 ⁇ M1 1-dimensional FFT data; based on different receiving channels, perform linear prediction on the Nr1 ⁇ M1 1-dimensional FFT data respectively, and obtain Nr1 ⁇ (M1+Y) 1-dimensional FFT data, Y is Positive integer; based on different receiving channels, perform Doppler FFT analysis on the Nr1 ⁇ (M1+Y) 1-dimensional FFT data respectively to obtain Nr1 first range-Doppler spectrograms; for Nr1 first range-Doppler spectrograms The range-Doppler spectrograms are superimposed on signals to obtain a second range-Doppler spectrogram; CFAR detection is performed on the one second range-Doppler spectrogram to obtain the first processed data.
  • the linear prediction is firstly carried out for the FFT data obtained from different receiving channels, and then Doppler FFT and signal superposition are carried out.
  • the first processed data is obtained by data processing. Because of the linear prediction, the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in the SIMO mode can be improved, so that the velocity resolution of the target acquired in the SIMO mode and the MIMO mode respectively can be improved.
  • the range of ⁇ v and the velocity measurement accuracy ⁇ v is closer, so that it is beneficial to improve the accuracy of velocity de-blurring of the signals obtained according to the SIMO mode and the MIMO mode.
  • performing second signal processing on the Nt ⁇ Nr2 ⁇ M2 signals to obtain second processing data includes: performing second signal processing on the Nt ⁇ Nr2 ⁇ M2 signals Perform distance FFT analysis on the M2 signals to obtain Nt ⁇ Nr2 ⁇ M2 1-dimensional FFT data; perform Doppler FFT analysis on the Nt ⁇ Nr2 ⁇ M2 1-dimensional FFT data respectively to obtain Nt ⁇ Nr2 third distance- Doppler spectrogram; perform signal superposition on Nt ⁇ Nr2 third distance-Doppler spectrograms to obtain a fourth distance-Doppler spectrogram; CFAR detection is performed on the graph to obtain the second processed data.
  • a signal processing apparatus has the function of implementing the above-mentioned method, and includes components corresponding to the steps or functions described in the above-mentioned method aspect.
  • the steps or functions can be implemented by software, or by hardware (eg, circuits), or by a combination of hardware and software.
  • the apparatus described above includes one or more processors and communication units.
  • the one or more processors are configured to support the apparatus to perform the functions of the methods described above. For example, a random access signal is sent to the access network device.
  • the communication unit is used to support the communication between the apparatus and other devices, and realize the function of receiving and/or sending.
  • the apparatus may further include one or more memories coupled to the processor, which hold program instructions and/or data necessary for the apparatus.
  • the one or more memories may be integrated with the processor, or may be provided separately from the processor. This application is not limited.
  • the device may be a radar system.
  • the device may also be a chip.
  • a computer-readable storage medium for storing a computer program, the computer program comprising instructions for performing the method in the first aspect or any one of possible implementations of the first aspect.
  • a computer program product comprising: computer program code, when the computer program code is run on a computer, the computer is made to execute the first aspect or any one of the first aspects methods in possible implementations.
  • a radar system including a processor and a receiver, where the processor is configured to execute the method in the first aspect or any one of possible implementations of the first aspect.
  • a smart car including a processor and a receiver, where the processor is configured to execute the method in the first aspect or any one of possible implementations of the first aspect.
  • FIG. 1 is a schematic diagram of a radar detection system according to an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a signal processing method according to an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a signal processing method according to an embodiment of the present application.
  • FIG. 4 is a schematic flowchart of a signal processing method according to another embodiment of the present application.
  • FIG. 5 is a schematic diagram of signal transmission in a SIMO+MIMO mode according to an embodiment of the present application.
  • FIG. 6 is a schematic diagram of performing forward prediction extension on a signal according to an embodiment of the present application.
  • FIG. 7 is a schematic diagram of performing backward prediction extension on a signal according to an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a signal processing apparatus according to an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a signal processing apparatus according to an embodiment of the present application.
  • FIG. 1 is a schematic diagram of a radar detection system 100 according to an embodiment of the present application.
  • the radar detection system 100 can work in a SIMO mode or in a MIMO mode.
  • the radar detection system 100 includes a transmitter 110 , a receiver 120 and a processing unit 130 .
  • the transmitter 110 may include Nt transmit antennas, and each transmit antenna corresponds to one transmit channel.
  • the receiver 120 includes Nr receive antennas, and each receive antenna corresponds to one receive channel. Nt transmit antennas and Nr receive antennas can form a MIMO antenna array.
  • At least two of the Nt transmit channels can be used to transmit signals to the target, and at least two of the Nr receive channels can be used to receive echo signals reflected by the target. .
  • one of the Nt transmit channels may be used to transmit signals to the target, and at least two of the Nr receive channels may be used to receive echo signals reflected by the target.
  • SIMO mode and MIMO mode can share the same set of transmit and receive antennas (eg, MIMO antenna array).
  • the SIMO mode and the MIMO mode may also use different transmit antennas and receive antennas, respectively, which is not limited in this embodiment of the present application.
  • the number of reception channels used by the radar detection system 100 in the SIMO mode and the MIMO mode may be the same or different.
  • the processing unit 130 may be used to control the transmitter 110 to transmit signals, or control the receiver 120 to receive signals, and may also be used to process signals to be transmitted or received signals.
  • the processing unit 130 may include a central processing unit (CPU), a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), or may be other types of processing chip.
  • CPU central processing unit
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • the structure of the radar detection system 100 in FIG. 1 is only used as an example, rather than a limitation. Those skilled in the art can understand that the above-mentioned radar detection system 100 may further include more or less modules or units.
  • the above-mentioned radar detection system 100 may also be referred to as a radar for short.
  • the main purpose of the SIMO mode includes speed matching and de-ambiguity of the detection target in the MIMO mode. Therefore, the industry has minimized the number of signals transmitted in the SIMO mode considering the radar update cycle. This results in a velocity resolution ⁇ v and velocity measurement accuracy ⁇ v for the target in SIMO mode when velocity deblurring is performed. Both are quite different from the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target in MIMO mode, which affects the accuracy of velocity matching and de-blurring of the signal.
  • the embodiments of the present application propose a signal processing method and apparatus, which can improve the accuracy of speed matching and deblurring of signals under the SIMO+MIMO radar detection mechanism.
  • linear prediction can be performed on the signal obtained in the SIMO mode, and the linearly predicted signal can be used for velocity matching deblurring, thereby improving the accuracy of velocity matching deblurring of the signal.
  • FIG. 2 is a schematic flowchart of a signal processing method according to an embodiment of the present application.
  • the method of FIG. 2 is based on a SIMO+MIMO radar detection mechanism, which may be performed by the radar 100 of FIG. 1 .
  • the method of Figure 2 includes:
  • Nr1 ⁇ M1 signals where the Nr1 ⁇ M1 signals are echo signals of M1 signals sent by the radar to a target in a SIMO mode, and the SIMO mode corresponds to one transmit channel and Nr1 receive channels, Nr1 and M1 are integers larger than 1.
  • Nt ⁇ Nr2 ⁇ M2 signals are echo signals of M2 signals sent by the radar to the target in a MIMO mode, where the MIMO mode corresponds to the Nt transmission channels and the Nr2 receiving channels, Nr2, Nt, and M2 are integers greater than 1.
  • the radar uses one transmit channel and Nr1 receive channels to transmit and receive signals in SIMO mode. Specifically, the radar sends M1 signals through one transmitting channel, and after the M1 signals are reflected by the target, Nr1 receiving channels receive Nr1 ⁇ M1 signals in total.
  • the radar uses Nt transmit channels and Nr2 receive channels to transmit and receive signals in MIMO mode. Specifically, the radar transmits Nt ⁇ M2 signals through Nt transmit channels, wherein each transmit channel transmits M2 signals. After the Nt ⁇ M2 signals are reflected by the target, Nr2 receiving channels acquire Nt ⁇ Nr2 ⁇ M2 signals in total.
  • Nr1 and Nr2 may be the same or different.
  • the above-mentioned signal may be a chirp signal.
  • the above-mentioned signal may be a signal measured in other units.
  • S203 Perform first signal processing on the Nr1 ⁇ M1 signals to obtain first processed data.
  • the first signal processing includes performing the following processing in sequence: range FFT analysis, linear prediction, and Doppler FFT analysis.
  • Linear prediction is used to predict the FFT data before or after the time domain of the FFT data obtained by range FFT analysis.
  • linear prediction is used to predict the FFT data before or after the time domain of the FFT data obtained through the distance FFT analysis, which means that the time domain signal corresponding to the predicted FFT data is the FFT data obtained through the distance FFT analysis.
  • the signal before or after the time domain signal can refer to a mathematical method of calculating a future or past signal according to a linear function based on existing sampling point information or signals.
  • the above-mentioned sampling point information includes, for example, FFT data obtained through FFT analysis.
  • linear prediction may also be referred to as linear expansion or linear estimation.
  • the linear prediction includes at least one of the following: forward prediction extension, backward prediction extension.
  • the forward prediction extension refers to using the existing sampling point information or signal to predict a certain signal in the future
  • the backward prediction extension refers to using the existing sampling point information or signal to predict a certain signal in the past.
  • the FFT analysis is used to implement frequency domain analysis of time domain signals. According to different characteristics represented by the analysis results, the FFT analysis may include various types, for example, range FFT analysis, Doppler FFT analysis, or angle FFT analysis.
  • the distance FFT analysis is used to analyze the correspondence between the frequency spectrum of the signal and the distance of the observation target.
  • Doppler FFT analysis is used to analyze the correspondence between the frequency spectrum of the signal and the velocity of the observed target.
  • Angle FFT analysis is used to analyze the correspondence between the spectrum of the signal and the angle of the observed target.
  • the above FFT analysis can also be replaced by other time-frequency analysis methods, such as discrete Fourier transform (discrete Fourier transform, DFT) analysis.
  • discrete Fourier transform discrete Fourier transform, DFT
  • the first signal processing may further include other types of signal processing manners, such as signal superposition (or signal accumulation), target detection, and the like.
  • signal superposition or signal accumulation
  • target detection and the like.
  • the common methods of target detection are constant false alarm detection (CFAR), constant leakage alarm detection, maximum detection, and eigenvalue detection.
  • CFRA constant false alarm detection
  • the above-mentioned signal superposition may include coherent superposition or incoherent superposition of signals.
  • CFAR can refer to the method of detecting the signal to determine whether there is a target under the condition that the false alarm probability is kept constant in the radar system.
  • the false alarm probability can refer to the probability that in the process of radar detection, when the threshold detection method is used, due to the existence of noise, the target does not actually exist but is misjudged as a target.
  • Constant leakage alarm detection can refer to the method of detecting the signal to determine whether there is a target under the condition that the probability of missing alarm is kept constant in the radar system.
  • the probability of missed alarm can refer to the probability that in the process of radar detection, when the threshold detection method is adopted, due to the existence of noise, the actual target is misjudged as no target.
  • Maximum value detection may refer to a method of determining whether a target exists in a radar system by detecting whether the maximum value in the signal is greater than a predetermined value.
  • Eigenvalue detection may refer to a method of detecting whether a signal has an eigenvalue in a radar system to determine whether a target exists.
  • Eigenvalues can refer to data representing the characteristics of the target.
  • the first signal processing may in turn include the following types of processing, range FFT analysis, coherent superposition of different receive channels, linear prediction, Doppler FFT analysis, CFAR.
  • range FFT analysis coherent superposition of different receive channels
  • linear prediction linear prediction
  • Doppler FFT analysis CFAR
  • the first signal processing may sequentially include the following types of processing: range FFT analysis, Doppler FFT analysis, signal superposition, CFAR.
  • range FFT analysis range FFT analysis
  • Doppler FFT analysis Doppler FFT analysis
  • signal superposition signal superposition
  • CFAR CFAR
  • the above-mentioned signal superposition may be coherent superposition or incoherent superposition.
  • the first signal processing may not include signal superposition or target detection.
  • the start time of sending M1 signals in the SIMO mode is the first time T1
  • the start time of sending the M2 signals in the MIMO mode is the second time T2
  • the time interval ⁇ t
  • the signals obtained in the SIMO mode and the MIMO mode need to be used for speed matching de-blurring. Therefore, the above-mentioned M1 signals and M2 signals should be transmitted simultaneously as much as possible.
  • the M1 signals and the M2 signals may be transmitted within one frame.
  • the first signal processing may further include other types of signal processing manners, for example, signal superposition, CFAR, and the like.
  • S205 Perform speed matching defuzzification processing according to the first processing data and the second processing data.
  • the velocity matching defuzzification processing includes various manners, for example, the velocity matching defuzzification processing can be performed by using the Chinese remainder theorem.
  • signal reconstruction is performed using the parameters of the received and transmitted signals to achieve velocity-matched deblurring.
  • the parameters of the received signal include first processing data and second processing data.
  • the number M1 of SIMO signal transmissions is much smaller than the number of transmissions Nt ⁇ M2 of the MIMO radar signals, and the velocity resolution ⁇ v measured in the SIMO mode is relative to the velocity measurement accuracy ⁇ v
  • the MIMO mode is poor, or the ⁇ v and ⁇ v ranges of the targets acquired in the SIMO mode and the MIMO mode, respectively, do not match, which adversely affects the accuracy of the velocity matching deblurring. Therefore, the solution of the present application performs first signal processing including linear prediction on the echo signal obtained in the SIMO mode to obtain first processed data.
  • the velocity resolution ⁇ v and the velocity measurement accuracy ⁇ v of the target acquired in the SIMO mode can be improved, so that the velocity of the target acquired in the SIMO mode and the MIMO mode respectively can be improved.
  • the range of the resolution ⁇ v and the velocity measurement accuracy ⁇ v is closer, which is beneficial to improve the accuracy of the velocity matching and de-blurring of the signals obtained according to the SIMO mode and the MIMO mode.
  • FIG. 3 is a schematic flowchart of a signal processing method according to an embodiment of the present application. Assuming that the radar transmits M1 signals in SIMO mode, the Nr1 receiving channels obtain Nr1 ⁇ M1 signals in total. The radar transmits M2 signals through each transmit channel in the MIMO mode, and the Nr2 receive channels obtain a total of Nt ⁇ Nr2 ⁇ M2 signals.
  • S301-S305 are used to describe the process of performing the first signal processing on the signal acquired in the SIMO mode
  • S306-S309 are used to describe the process of performing the second signal processing on the signal acquired in the MIMO mode.
  • S310 is used to describe the process of speed matching defuzzification.
  • Signals acquired in SIMO mode may be referred to as SIMO signals.
  • a signal acquired in the MIMO mode may be referred to as a MIMO signal.
  • the method of Figure 3 includes:
  • the 1-dimensional FFT data refers to data obtained after only performing 1-dimensional FFT transformation on the acquired time-domain signal.
  • S303 Perform linear prediction on M1 pieces of 1-dimensional FFT data to obtain M1+Y pieces of 1-dimensional FFT data.
  • the Y pieces of 1-dimensional FFT data of the linear prediction may be forward prediction extension performed on M1 1-dimensional FFT data, backward prediction extension, or forward prediction extension+backward prediction extension.
  • Y is a positive integer.
  • Nr1 ⁇ M1 1-dimensional FFT data obtained by Nr1 receiving channels may not be coherently superimposed, but M1 1-dimensional FFT data corresponding to a certain receiving channel may be arbitrarily selected for linearity. predict.
  • CFAR can also be replaced by constant leakage alarm detection, maximum value detection, and feature value detection.
  • performing signal superimposition on the Nt ⁇ Nr2 third range-Doppler spectrograms may include all the spectrograms in the Nt ⁇ Nr2 third range-Doppler spectrograms, and may also include part of the spectrograms.
  • S310 Perform speed matching defuzzification processing according to the first processing data and the second processing data.
  • the FFT data corresponding to multiple receiving channels are first coherently superimposed, and the superimposed FFT data is linearly predicted. , and obtain the first processed data through subsequent data processing. Due to the linear prediction, the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode can be improved, so that the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in SIMO mode and MIMO mode are The range is closer, which is beneficial to improve the accuracy of speed deblurring of signals acquired according to SIMO mode and MIMO.
  • FIG. 4 is a schematic flowchart of a signal processing method according to another embodiment of the present application. Assuming that the radar transmits M1 signals in SIMO mode, the Nr1 receiving channels obtain Nr1 ⁇ M1 signals in total. The radar transmits M2 signals through each transmit channel in the MIMO mode, and the Nr receive channels obtain Nt ⁇ Nr2 ⁇ M2 echo data in total.
  • S401-S405 are used to describe the process of performing the first signal processing on the signal acquired in the SIMO mode
  • S406-S409 are used to describe the process of performing the second signal processing on the signal acquired in the MIMO mode.
  • S410 is used to describe the process of speed matching defuzzification.
  • Signals acquired in SIMO mode may be referred to as SIMO signals.
  • a signal acquired in the MIMO mode may be referred to as a MIMO signal.
  • the method of Figure 4 includes:
  • the 1-dimensional FFT data refers to data obtained after only performing 1-dimensional FFT transformation on the acquired time-domain signal.
  • each one-dimensional FFT data may include L FFT data, and L represents the number of FFT points for distance FFT analysis.
  • Nr1 ⁇ M1 1-dimensional FFT data may be divided into Nr1 groups of 1-dimensional FFT data, and each group of 1-dimensional FFT data includes M1 FFT data.
  • Linear prediction can be performed on each group of 1-dimensional FFT data, and finally Nr1 ⁇ (M1+Y) 1-dimensional FFT data are obtained.
  • the Y pieces of 1-dimensional FFT data of the linear prediction may be forward prediction extension performed on M1 1-dimensional FFT data, backward prediction extension, or forward prediction extension+backward prediction extension.
  • the above-mentioned signal superposition manner may be coherent superposition or incoherent superposition.
  • S407 Perform Doppler FFT analysis on the Nt ⁇ Nr2 ⁇ M2 1-dimensional FFT data respectively, to obtain Nt ⁇ Nr2 third range-Doppler spectrograms.
  • CFAR can also be replaced by constant leakage alarm detection, maximum value detection, and feature value detection.
  • S410 Perform speed matching defuzzification processing according to the first processing data and the second processing data.
  • the linear prediction is firstly performed on the FFT data obtained from different receiving channels, and then Doppler FFT and signal superposition are performed. , and the first processed data is obtained through subsequent data processing. Due to the linear prediction, the velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target acquired in the SIMO mode can be improved, so that the target acquired in the SIMO mode and the MIMO mode can be obtained respectively.
  • the range of the velocity resolution ⁇ v of the target and the velocity measurement accuracy ⁇ v is closer, which is beneficial to improve the accuracy of velocity de-blurring of the signals obtained according to the SIMO mode and the MIMO mode.
  • FIG. 5 is a schematic diagram of signal transmission in a SIMO+MIMO mode according to an embodiment of the present application.
  • the radar transmits M1 signals in SIMO mode, and transmits Nt ⁇ M2 signals through Nt transmit antennas in MIMO mode.
  • the maximum unambiguous velocity range v max , velocity resolution ⁇ v and velocity measurement accuracy ⁇ v of the target measured in SIMO mode are expressed as equations (1)-(3), respectively:
  • ⁇ represents the wavelength of the transmitted signal
  • T c1 represents the PRI of the transmitted signal in SIMO mode
  • SNR represents the signal-to-noise ratio of the target in the received signal.
  • ⁇ represents the wavelength of the transmitted signal
  • T c2 represents the PRI of the transmitted signal in the MIMO mode
  • SNR represents the signal-to-noise ratio of the target in the received signal.
  • the main function of the SIMO mode is to perform velocity matching and de-ambiguity processing on the signals obtained by the MIMO mode, but it is not helpful for CFAR detection, ranging and angle measurement.
  • the number M1 of SIMO signal transmissions is much smaller than the number Nt ⁇ M2 of signals transmitted by the MIMO radar.
  • the v max of the target measured in the SIMO mode is much larger than the V max of the MIMO mode.
  • the ⁇ v and ⁇ v measured in the SIMO mode are relatively poor compared to the MIMO mode, or the ⁇ v and ⁇ v ranges of the targets obtained in the SIMO mode and the MIMO mode respectively do not match, which adversely affects the accuracy of velocity matching deblurring.
  • a linear prediction (or linear expansion) method can be used to perform linear prediction on the signal acquired in the SIMO mode, so as to achieve the purpose of improving the speed resolution and speed accuracy of SIMO, and further Improved the accuracy of velocity matching deblurring.
  • Nr1 ⁇ M1 1-dimensional FFT data are obtained.
  • the linear prediction based on the above Nr1 ⁇ M1 1-dimensional FFT data may include but not be limited to the following situations.
  • each group of 1-dimensional FFT data includes M1 1-dimensional FFT data, respectively for each group of 1-dimensional FFT data FFT data for linear prediction.
  • each group of 1-dimensional FFT data includes M1 1-dimensional FFT data, and one group of 1-dimensional FFT data is arbitrarily selected FFT data for linear prediction.
  • linear prediction includes forward prediction extension and backward prediction extension.
  • the forward prediction extension refers to using the existing sampling point information or signal to predict a certain signal in the future
  • the backward prediction extension refers to using the existing sampling point information or signal to predict a certain signal in the past.
  • the sample signal may include the above-described 1-dimensional FFT data.
  • the measurement samples may include all or part of the above-mentioned M1 1-dimensional FFT data, and the measurement samples may also include FFT data obtained by several previous linear predictions.
  • the sample signal can be selected in [X 1 , X 2 , . . . , X M1 ].
  • the measurement samples may be represented as [X M-p+1 , . . . , X M ], wherein the measurement samples include p 1-dimensional FFT data, p>1.
  • FIG. 6 is a schematic diagram of the principle of performing forward prediction expansion on data according to an embodiment of the present application.
  • Forward prediction extension may also be referred to as forward estimation or forward extension.
  • the forward prediction extension refers to estimating the M+1th signal based on the existing measurement samples [X M-p+1 ,..., X M ]
  • the forward estimated signal can be expressed as Using the measurement samples [X M-p+1 , . . . , X M ], it is possible to It is expressed as the following formula (7).
  • the following formula (8) shows the derivation formula for estimating A f .
  • X f [X Mp , . . . , X M-1 ]. It should be noted that X Mp is also a known sample signal.
  • a f can be solved by using the method of least squares. Specifically, as shown in the following formula (9), the solution that minimizes the norm in formula (9) is the solution of A f .
  • Equation (10) the solution of A f is expressed as Equation (10).
  • FIG. 7 is a schematic diagram of the principle of backward expansion of data according to an embodiment of the present application.
  • Backward prediction extension may also be referred to as backward estimation or backward extension.
  • the backward prediction extension refers to estimating the Mp-th signal according to the measurement samples [X M-p+1 , . . . , X M ] backward estimation signal can be expressed as formula (11).
  • a b can be represented by the following formula (12).
  • the MIMO signal is transmitted first in the same frame, and then the SIMO signal is transmitted, and the waveform and period of the MIMO signal and the SIMO signal are consistent, the backward direction of the SIMO
  • the estimated signal coincides with the measurement parameters of MIMO.
  • the estimated value of SIMO can be corrected by using the measurement result of MIMO.
  • FIG. 8 is a schematic block diagram of an apparatus 600 for signal processing according to an embodiment of the present application.
  • the apparatus 600 is capable of executing the methods in FIGS. 2 to 4 of the present application.
  • Apparatus 600 includes:
  • the acquiring unit 610 is configured to acquire Nr1 ⁇ M1 signals, where the Nr1 ⁇ M1 signals are echo signals of M1 signals sent by the radar to the target in a SIMO mode, the SIMO mode corresponds to one transmit channel and Nr1 receive channels, Nr1 and M1 are integers greater than 0.
  • the obtaining unit 610 is further configured to obtain Nt ⁇ Nr2 ⁇ M2 signals, where the Nt ⁇ Nr2 ⁇ M2 signals are echo signals of the M2 signals sent by the radar to the target in the MIMO mode, and the MIMO mode corresponds to: Nt sending channels and Nr2 receiving channels, Nt, Nr2 and M2 are integers greater than 0.
  • the processing unit 620 is configured to perform first signal processing on the Nr1 ⁇ M1 signals to obtain first processed data, where the first signal processing includes sequentially performing the following processing: range FFT analysis, linear prediction and Doppler FFT analysis, the linear prediction is used to predict the FFT data before or after the time domain of the FFT data obtained by the distance FFT analysis.
  • the processing unit 620 is further configured to perform second signal processing on the Nt ⁇ Nr2 ⁇ M2 signals to obtain second processing data, where the second signal processing includes range FFT analysis and Doppler FFT analysis.
  • the processing unit 620 is further configured to perform speed matching defuzzification processing according to the first processing data and the second processing data.
  • the above-mentioned apparatus 600 may include the radar 100 in FIG. 1 .
  • the above-mentioned acquisition unit 610 and the above-mentioned processing unit 620 may be used by the processing unit 130 in FIG. 1 .
  • the signal acquired by the acquisition unit 610 may be a signal received by the receiver 120 in FIG. 1 .
  • FIG. 9 is a schematic block diagram of an apparatus 700 for signal processing according to an embodiment of the present application.
  • the apparatus 700 is capable of executing the methods in FIGS. 2 to 4 of the present application.
  • the apparatus 700 includes: one or more memories 710 , one or more communication interfaces 720 , and one or more processors 730 .
  • the processor 730 is used to control the communication interface 720 to send and receive signals, the memory 710 is used to store a computer program, and the processor 730 is used to call and run the computer program from the memory 2010, so that the apparatus 700 executes the method embodiments of the present application corresponding processes and/or actions in .
  • the apparatus 700 may perform the steps performed in FIG. 2 to FIG. 4 , which are not repeated here for brevity.
  • the above-mentioned apparatus 700 may include the radar 100 in FIG. 1 .
  • the above-mentioned processor 730 may include the processing unit 130 in FIG. 1 .
  • the above-described communication interface 720 may include the receiver 120 in FIG. 1 .
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, removable hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

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Abstract

本申请提供了一种信号处理的方法和装置，能够提高在雷达的SIMO+MIMO机制下对信号进行速度匹配解模糊的精度。该方法包括：获取Nr1×M1个信号，Nr1×M1个信号是雷达在SIMO模式下向目标发送的M1个信号的回波信号；获取Nt×Nr2×M2个信号，Nt×Nr2×M2个信号是雷达在MIMO模式下向目标发送的M2个信号的回波信号；对Nr1×M1个信号进行第一信号处理，以获取第一处理数据，第一信号处理包括依次进行距离FFT分析、线性预测和多普勒FFT分析；对Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，第二信号处理包括距离FFT分析和多普勒FFT分析；根据第一处理数据和第二处理数据，进行速度匹配解模糊处理。

Description

信号处理方法和装置 技术领域
本申请涉及通信领域，尤其涉及信号处理方法和装置。
背景技术
雷达探测广泛地应用于各种领域，例如，高级驾驶辅助系统(advanced driver assistant system，ADAS)、无人驾驶领域等。随着技术发展，对雷达探测提出了更高距离分辨率、更高角度分辨率的需求。
雷达可通过发射更大带宽来提升距离分辨率，通过设置更多的多输入多输出(multiple input multiple output，MIMO)阵列来实现更高的角度分辨率。设置有MIMO阵列的雷达可以称为MIMO雷达。MIMO雷达可以支持大量的发射天线轮流发射，但由于MIMO雷达采用天线轮流发射，导致其单天线的相对脉冲重复周期(pulse repetition interval，PRI)较长，从而降低了MIMO雷达对目标运动速度的探测范围，导致超过MIMO雷达最大速度探测范围外的目标运动速度混叠到探测范围内，造成目标的速度模糊。
为了解决上述问题，业界还提出了一种单输入多输出(simple input multiple output，SIMO)+MIMO的雷达探测机制。在SIMO+MIMO机制下，雷达既可以工作在MIMO模式下，也可以工作SIMO模式下。雷达可以分别通过SIMO模式和MIMO模式探测目标，并基于SIMO模式和MIMO模式的PRI周期不同，利用在SIMO模式下获取的信号对在MIMO模式下获取的信号进行速度匹配解模糊处理。但是随着技术进步，对SIMO+MIMO模式下获取的信号进行速度匹配解模糊处理的精度依然面临着挑战。
发明内容
本申请提供一种信号处理方法和装置，能够提高SIMO+MIMO模式下对信号进行速度匹配解模糊处理的精度。
第一方面，提供了一种信号处理方法，包括：获取Nr1×M1个信号，所述Nr1×M1个信号是雷达在SIMO模式下向目标发送的M1个信号的回波信号，所述SIMO模式对应于1个发射通道和Nr1个接收通道，Nr1、M1为大于1的整数；获取Nt×Nr2×M2个信号，该Nt×Nr2×M2个信号是雷达在MIMO模式下向所述目标发送的M2个信号的回波信号，所述MIMO模式对应于Nt个发送通道和Nr2个接收通道，Nt、Nr2、M2为大于1的整数；对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测和多普勒FFT分析，所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据；对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，所述第二信号处理包括距离FFT分析和多普勒FFT分析；根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
可选地，上述所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据，是指预测得到的FFT数据对应的时域信号是经过距离FFT分析得到的FFT数据的时域信号之前或之后的信号。
可选地，上述经过距离FFT分析得到的FFT数据，可以在进行其它类型的信号处理之后(例如，接收通道的相干叠加)，再进行线性预测。
在雷达探测场景中，SIMO的信号发射个数M1远小于MIMO雷达的信号的发射个数Nt×M2，SIMO模式下测量的速度分辨率Δv和速度测量精度σ v相对MIMO模式较差，或者说SIMO模式和MIMO模式下分别获取的目标的Δv和σ v范围不匹配，这对速度匹配解模糊处理的精度产生了不利影响。因此，本申请方案对SIMO模式下获取的回波信号进行包括线性预测在内的第一信号处理以得到第一处理数据。由于线性预测相当于扩展了SIMO模式下获取的信号个数，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO模式获取的信号进行速度匹配解模糊处理的精度。
结合第一方面，在第一方面的某些实现方式中，所述第一信号处理包括依次进行以下处理：距离FFT分析、接收通道的相干叠加、线性预测、多普勒FFT分析、CFAR。
在对SIMO模式下的获取的Nr1×M1个信号进行距离FFT分析之后，将不同接收通道对应的FFT数据进行相干叠加，并利用叠加后的FFT数据进行线性预测，然后基于线性预测后的数据进行速度匹配解模糊处理。线性预测可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，从而使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，有利于提高根据SIMO模式和MIMO模式获取的信号进行速度匹配解模糊处理的精度。
结合第一方面，在第一方面的某些实现方式中，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测、多普勒FFT分析、信号叠加、CFAR。
在对SIMO模式下的获取的Nr1×M1个信号进行距离FFT分析之后，分别将不同接收通道对应的FFT数据进行线性预测，并基于线性预测得到的FFT数据进行速度匹配解模糊处理。线性预测可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，从而在SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，有利于提高根据SIMO模式和MIMO模式获取的信号进行速度匹配解模糊处理的精度。
结合第一方面，在第一方面的某些实现方式中，所述第二信号处理还包括依次进行以下处理：距离FFT分析、多普勒FFT分析、信号叠加、CFAR。
结合第一方面，在第一方面的某些实现方式中，对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，包括：对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；对所述Nr1×M1个1维FFT数据进行接收通道的相干叠加，得到M1个1维FFT数据；对所述M1个1维FFT数据进行线性预测，得到M1+Y个1维FFT数据，Y为正整数；对所述M1+Y个1维FFT数据进行多普勒FFT分析，得到1个距离-多普勒频谱图；对所述1个距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
在雷达探测场景中，对SIMO模式下获取的回波信号进行距离FFT分析之后，首先将多个接收通道对应的FFT数据进行相干叠加，以及对叠加后的FFT数据进行线性预测，并经过后续的数据处理得到第一处理数据。由于进行了线性预测，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO模式获取的信号进行速度解模糊处理的精度。
结合第一方面，在第一方面的某些实现方式中，对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，包括：对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；基于不同的接收通道，分别对所述Nr1×M1个1维FFT数据进行线性预测，得到Nr1×(M1+Y)个1维FFT数据，Y为正整数；基于不同的接收通道，分别对所述Nr1×(M1+Y)个1维FFT数据进行多普勒FFT分析，获得Nr1个第一距离-多普勒频谱图；对Nr1个第一距离-多普勒频谱图进行信号叠加，获得1个第二距离-多普勒频谱图；对所述1个第二距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
在雷达探测场景中，对SIMO模式下获取的回波信号进行距离FFT分析之后，首先针对不同接收通道获取的FFT数据分别进行线性预测，然后再进行多普勒FFT和信号叠加，并经过后续的数据处理得到第一处理数据，由于进行了线性预测，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO模式获取的信号进行速度解模糊处理的精度。
结合第一方面，在第一方面的某些实现方式中，所述对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，包括：对所述Nt×Nr2×M2个信号进行距离FFT分析，得到Nt×Nr2×M2个1维FFT数据；对所述Nt×Nr2×M2个1维FFT数据分别进行多普勒FFT分析，获得Nt×Nr2个第三距离-多普勒频谱图；对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，获得1个第四距离-多普勒频谱图；对所述1个第四距离-多普勒频谱图进行CFAR检测，得到所述第二处理数据。
第二方面，提供了一种信号处理的装置。该装置具有实现上述方法的功能，其包括用于执行上述方法方面所描述的步骤或功能相对应的部件。所述步骤或功能可以通过软件实现，或硬件(如电路)实现，或者通过硬件和软件结合来实现。
在一种可能的设计中，上述装置包括一个或多个处理器和通信单元。所述一个或多个处理器被配置为支持所述装置执行上述方法的功能。例如，向接入网设备发送随机接入信号。所述通信单元用于支持所述装置与其他设备通信，实现接收和/或发送功能。
可选地，所述装置还可以包括一个或多个存储器，所述存储器用于与处理器耦合，其保存装置必要的程序指令和/或数据。所述一个或多个存储器可以和处理器集成在一起，也可以与处理器分离设置。本申请并不限定。
可选地，所述装置可以为雷达系统。
可选地，所述装置还可以为芯片。
第三方面，提供了一种计算机可读存储介质，用于存储计算机程序，该计算机程序包括用于执行第一方面或第一方面中任一种可能实现方式中的方法的指令。
第四方面，提供了一种计算机程序产品，所述计算机程序产品包括：计算机程序代码， 当所述计算机程序代码在计算机上运行时，使得计算机执行上述第一方面或第一方面中任一种可能实现方式中的方法。
第五方面，提供了一种雷达系统，包括处理器和接收器，所述处理器用于执行上述第一方面或第一方面中任一种可能实现方式中的方法。
第六方面，提供了一种智能车，包括处理器和接收器，所述处理器用于执行上述第一方面或第一方面中任一种可能实现方式中的方法。
附图说明
图1是本申请一实施例的雷达探测系统的示意图。
图2是本申请一实施例的信号处理方法的流程示意图。
图3是本申请一实施例的信号处理方法的流程示意图。
图4是本申请又一实施例的信号处理方法的流程示意图。
图5是本申请一实施例的SIMO+MIMO模式的信号发射示意图。
图6是本申请一实施例的对信号进行前向预测扩展的示意图。
图7是本申请一实施例的对信号进行后向预测扩展的示意图。
图8是本申请一实施例的信号处理的装置的结构示意图。
图9是本申请一实施例的信号处理的装置的结构示意图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
图1是本申请一实施例的雷达探测系统100的示意图。该雷达探测系统100可以工作在SIMO模式下，也可以工作在MIMO模式下。如图1所示，雷达探测系统100包括发射机110、接收机120以及处理单元130。其中，发射机110可包括Nt个发射天线，每个发射天线对应一个发射通道。接收机120包括Nr个接收天线，每个接收天线对应一个接收通道。Nt个发射天线和Nr个接收天线可以组成MIMO天线阵列。
当雷达探测系统100工作在MIMO模式下时，可以利用Nt个发射通道中的至少两个发射通道向目标发射信号，并利用Nr个接收通道中的至少两个接收通道接收目标反射的回波信号。
当雷达探测系统100工作在SIMO模式下时，可以利用Nt个发射通道中的其中一个发射通道向目标发射信号，并利用Nr个接收通道中的至少两个接收通道接收目标反射的回波信号。
换句话说，SIMO模式和MIMO模式可以共用同一套发射天线和接收天线(例如，MIMO天线阵列)。可选地，SIMO模式和MIMO模式也可以分别使用不同的发射天线和接收天线，本申请实施例对此不作限定。
可选地，雷达探测系统100在SIMO模式和MIMO模式使用的接收通道数目可以相同，也可以不同。
处理单元130可以用于控制发射机110发送信号，或者控制接收机120接收信号，并且还可以用于处理待发送的信号或接收到的信号。
处理单元130可以包括中央处理器(central processor unit，CPU)、现场可编程门阵 列(field programmable gate array，FPGA)或专用集成电路(application specific integrated circuit，ASIC)，或者也可以为其它类型的处理芯片。
在本申请实施例中，图1的雷达探测系统100的结构仅仅作为示例，而非限定。本领域技术人员能够理解，上述雷达探测系统100还可以包括更多或更少的模块或单元。
在本申请实施例中，上述雷达探测系统100也可以简称为雷达。
SIMO模式主要用途包括对MIMO模式下的探测目标进行速度匹配解模糊，因此，业界从雷达更新周期考虑，尽量减少了SIMO模式下发射的信号个数。这导致在进行速度解模糊处理时，SIMO模式下的目标的速度分辨率Δv和速度测量精度σ v。均与MIMO模式下的目标的速度分辨率Δv和速度测量精度σ v存在较大的差别，从而影响了对信号进行速度匹配解模糊处理的精度。为了解决上述问题，本申请实施例提出了一种信号处理方法和装置，可以提高SIMO+MIMO雷达探测机制下对信号进行速度匹配解模糊的精度。
在该方法中可以通过对SIMO模式下获取的信号进行线性预测(linear prediction)，并利用线性预测后的信号进行速度匹配解模糊处理，从而提高了对信号进行速度匹配解模糊的精度。
图2是本申请一实施例的信号处理方法的流程示意图。图2的方法基于SIMO+MIMO雷达探测机制，该方法可以由图1中的雷达100执行。图2的方法包括：
S201、获取Nr1×M1个信号，所述Nr1×M1个信号是雷达在SIMO模式下向目标发送的M1个信号的回波信号，所述SIMO模式对应于1个发射通道和Nr1个接收通道，Nr1、M1为大于1的整数。
S202、获取Nt×Nr2×M2个信号，Nt×Nr2×M2个信号是雷达在MIMO模式下向所述目标发送的M2个信号的回波信号，所述MIMO模式对应于Nt个发送通道和Nr2个接收通道，Nr2、Nt、M2为大于1的整数。
其中，雷达在SIMO模式下使用1个发射通道和Nr1个接收通道进行信号的发送和接收。具体地，雷达通过1个发射通道发送了M1个信号，该M1个信号经过目标反射之后，Nr1个接收通道共接收Nr1×M1个信号。
雷达在MIMO模式下使用Nt个发射通道和Nr2个接收通道进行信号的发送和接收。具体地，雷达通过Nt个发射通道发送Nt×M2个信号，其中，每个发射通道发送M2个信号。该Nt×M2个信号经过目标反射之后，Nr2个接收通道共获取Nt×Nr2×M2个信号。
可选地，Nr1和Nr2可以相同，也可以不同。
可选地，上述信号可以是叽叽(chirp)信号。或者，上述信号也可以是使用其它单位计量的信号。
S203、对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测和多普勒FFT分析，所述线性预测用于基于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据。
其中，上述所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据，是指预测得到的FFT数据对应的时域信号是经过距离FFT分析得到的FFT数据的时域信号之前或之后的信号。在另一种解释中，线性预测可以指根据已有采样点信息或信号，按照线性函数计算未来或过去某一信号的数学方法。上述采样点信息例如包括经过FFT分析得到的FFT数据。
可选地，线性预测也可以称为线性扩展(linear expansion)或线性估计。
所述线性预测包括以下至少一项：前向预测扩展、后向预测扩展。前向预测扩展是指利用已有采样点信息或者信号对未来某一信号进行预测，后向预测扩展是指利用已有采样点信息或信号对过去某一信号进行预测。
FFT分析用于实现对时域信号进行频域分析。根据分析结果表征的特征不同，FFT分析可以包括多种类型，例如，距离FFT分析、多普勒FFT分析或者角度FFT分析。其中，距离FFT分析用于分析信号的频谱与观测目标的距离之间的对应关系。多普勒FFT分析用于分析信号的频谱与观测目标的速度之间的对应关系。角度FFT分析用于分析信号的频谱与观测目标的角度之间的对应关系。
可选地，上述FFT分析也可以被其它时频分析的方式替代，例如离散傅里叶变换(discrete Fourier transform,DFT)分析。
可选地，所述第一信号处理还可以包括其他类型的信号处理方式，例如，信号叠加(或者说，信号积累)、目标检测等。其中目标检测的常用方式为恒虚警检测(constant false alarm rate detection,CFAR)、恒漏警检测、最大值检测、特征值检测。上述信号叠加可以包括信号的相干叠加或非相干叠加。
其中，CFAR可以指雷达系统中保持虚警概率恒定条件下，对信号进行检测，以判定是否存在目标的方法。虚警概率可以指雷达探测过程中，采用门限检测的方法时，由于噪声的存在，实际不存在目标却误判为有目标的概率。
恒漏警检测可以指雷达系统中保持漏警概率恒定的条件下，对信号进行检测，以判定是否存在目标的方法。漏警概率可以指雷达探测过程中，采用门限检测的方法时，由于噪声的存在，实际存在目标却误判为没有目标的概率。
最大值检测可以指雷达系统中通过检测信号中最大值是否大于预定值的方式，判定是否存在目标的方法。
特征值检测可以指雷达系统中检测信号是否存在特征值，以判定是否存在目标的方法。特征值用可以指表示目标特征的数据。
作为示例，所述第一信号处理可以依次包括以下类型的处理，距离FFT分析、不同接收通道的相干叠加、线性预测、多普勒FFT分析、CFAR。下文中将结合图3详细描述该示例中的第一信号处理的过程。
作为另一示例，所述第一信号处理可以依次包括以下类型的处理：距离FFT分析、多普勒FFT分析、信号叠加、CFAR。其中，上述信号叠加可以是相干叠加，也可以是非相干叠加。下文中将结合图4详细描述该示例中的第一信号处理的过程。
可选地，第一信号处理中可以不包括信号叠加或目标检测。
可选地，假设在SIMO模式下发送M1个信号的起始时刻为第一时刻T1，在MIMO模式下发送M2个信号的起始时刻为第二时刻T2，则时间间隔Δt＝|T2-T1|小于预设值。应理解，由于SIMO模式下和MIMO模式下获取的信号需用于速度匹配解模糊处理。因此，上述M1个信号和M2个信号应尽可能同时发送。作为示例而非限定，可以在一个帧的时间内发送所述M1个信号和所述M2个信号。
S204、对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，所述第二信号处理包括距离FFT分析和多普勒FFT分析。
可选地，所述第一信号处理还可以包括其他类型的信号处理方式，例如，信号叠加、CFAR等。
S205、根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
可选地，在获取第一处理数据和第二处理数据之后，速度匹配解模糊处理包括多种方式，例如，可以采用中国余数定理进行速度匹配解模糊处理。或者，利用接收信号和发射信号的参数进行信号重建，以实现速度匹配解模糊处理。其中，接收信号的参数包括第一处理数据和第二处理数据。
在本申请实施例中，在雷达探测场景中，SIMO的信号发射个数M1远小于MIMO雷达的信号的发射个数Nt×M2，SIMO模式下测量的速度分辨率Δv和速度测量精度σ v相对MIMO模式较差，或者说SIMO模式和MIMO模式下分别获取的目标的Δv和σ v范围不匹配，这对速度匹配解模糊处理的精度产生了不利影响。因此，本申请方案对SIMO模式下获取的回波信号进行包括线性预测在内的第一信号处理以得到第一处理数据。由于线性预测相当于扩展了SIMO模式下获取的信号个数，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO模式获取的信号进行速度匹配解模糊处理的精度。
图3是本申请一实施例的信号处理方法的流程示意图。假设雷达在SIMO模式下发送了M1个信号，则Nr1个接收通道共获得Nr1×M1个信号。雷达在MIMO模式下通过每个发射通道发射了M2个信号，则Nr2个接收通道共获得Nt×Nr2×M2个信号。
其中，S301～S305用于描述在SIMO模式下获取的信号进行第一信号处理的过程，S306～S309用于描述在MIMO模式下获取的信号进行第二信号处理的过程。S310用于描述速度匹配解模糊的过程。SIMO模式下获取的信号可以称为SIMO信号。MIMO模式下获取的信号可以称为MIMO信号。图3的方法包括：
S301、对SIMO模式下获取的Nr1×M1个SIMO信号进行距离FFT处理，获得Nr1×M1个一维FFT(1dimension FFT,1D FFT)数据。
其中，在本申请实施例中，1维FFT数据是指对获取的时域信号只进行1维FFT变换之后得到的数据。其中，每个1维FFT数据中可以包括L个FFT数据，L表示进行距离FFT分析的FFT点数。L为正整数，例如，L＝512。
S302、对Nr1个接收通道获得的Nr1×M1个1维FFT数据进行相干叠加，获得M1个1维FFT数据。
S303、对M1个1维FFT数据进行线性预测，得到M1+Y个1维FFT数据。
其中，上述线性预测的Y个1维FFT数据可以是对M1个1维FFT数据进行前向预测扩展，也可以是后向预测扩展，或者也可以是前向预测扩展+后向预测扩展。Y为正整数。
可选地，在S302和S303中，也可以不对Nr1个接收通道获得的全部Nr1×M1个1维FFT数据进行相干叠加，而是任意选择某一个接收通道对应的M1个1维FFT数据进行线性预测。
S304、对线性预测得到的M1+Y个1维FFT数据进行多普勒FFT分析，得到1个距离-多普勒频谱图。
S305、对该距离-多普勒频谱图进行CFAR检测，得到第一处理数据。
可选地，CFAR也可以被恒漏警检测、最大值检测、特征值检测替代。
S306、对MIMO模式下获取的Nt×Nr2×M2个MIMO信号进行距离FFT分析，得到Nt×Nr2×M2个1维FFT数据。
S307、对所述Nt×Nr2×M2个1维FFT数据分别进行多普勒FFT分析，获得Nt×Nr2个第三距离-多普勒频谱图。
S308、对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，获得1个第四距离-多普勒频谱图。
可选地，对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，可以包括Nt×Nr2个第三距离-多普勒频谱图中的所有频谱图，也可以包括部分频谱图。
S309、对所述1个第四距离-多普勒频谱图进行CFAR检测，得到所述第二处理数据。
S310、根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
在图3中，在雷达探测场景中，对SIMO模式下获取的回波信号进行距离FFT分析之后，首先将多个接收通道对应的FFT数据进行相干叠加，以及对叠加后的FFT数据进行线性预测，并经过后续的数据处理得到第一处理数据。由于进行了线性预测，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO获取的信号进行速度解模糊处理的精度。
图4是本申请又一实施例的信号处理方法的流程示意图。假设雷达在SIMO模式下发送了M1个信号，则Nr1个接收通道共获得Nr1×M1个信号。雷达在MIMO模式下通过每个发射通道发射了M2个信号，则Nr个接收通道共获得Nt×Nr2×M2个回波数据。
其中，S401～S405用于描述在SIMO模式下获取的信号进行第一信号处理的过程，S406～S409用于描述在MIMO模式下获取的信号进行第二信号处理的过程。S410用于描述速度匹配解模糊的过程。SIMO模式下获取的信号可以称为SIMO信号。MIMO模式下获取的信号可以称为MIMO信号。图4的方法包括：
S401、对SIMO模式下获取的Nr1×M1个SIMO信号进行距离FFT处理，获得Nr1×M1个一维FFT(1dimension FFT,1D FFT)数据。
在本申请实施例中，1维FFT数据是指对获取的时域信号只进行1维FFT变换之后得到的数据。其中，每个1维FFT数据中可以包括L个FFT数据，L表示进行距离FFT分析的FFT点数。L为正整数，例如，L＝512。
S402、基于不同的接收通道，分别对所述Nr1×M1个1维FFT数据进行线性预测，得到Nr1×(M1+Y)个1维FFT数据。
例如，基于不同的接收通道，上述Nr1×M1个1维FFT数据可以被划分为Nr1组1维FFT数据，每组1维FFT数据包括M1个FFT数据。可以对每组1维FFT数据进行线性预测，最终获得Nr1×(M1+Y)个1维FFT数据。
其中，上述线性预测的Y个1维FFT数据可以是对M1个1维FFT数据进行前向预测扩展，也可以是后向预测扩展，或者也可以是前向预测扩展+后向预测扩展。
S403、基于不同的接收通道，分别对所述Nr1×(M1+Y)个1维FFT数据进行多普勒FFT分析，获得Nr1个第一距离-多普勒频谱图。
S404、对Nr1个第一距离-多普勒频谱图进行信号叠加，获得1个第二距离-多普勒频谱图。
可选地，上述信号叠加的方式可以是相干叠加，也可以是非相干叠加。
S405、对该第二距离-多普勒频谱图进行CFAR检测，得到第一处理数据。
S406、对所述Nt×Nr2×M2个信号进行距离FFT分析，得到Nt×Nr2×M2个1维FFT数据。
S407、对所述Nt×Nr2×M2个1维FFT数据分别进行多普勒FFT分析，获得Nt×Nr2个第三距离-多普勒频谱图。
S408、对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，获得1个第四距离-多普勒频谱图。
S409、对所述1个第四距离-多普勒频谱图进行CFAR检测，得到所述第二处理数据。
可选地，CFAR也可以被恒漏警检测、最大值检测、特征值检测替代。
S410、根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
在图4中，在雷达探测场景中，对SIMO模式下获取的回波信号进行距离FFT分析之后，首先针对不同接收通道获取的FFT数据分别进行线性预测，然后再进行多普勒FFT和信号叠加，并经过后续的数据处理得到第一处理数据，由于进行了线性预测，因此可以提高SIMO模式下获取的目标的速度分辨率Δv和速度测量精度σ v，使得SIMO模式和MIMO模式下分别获取的目标的速度分辨率Δv和速度测量精度σ v的范围更加接近，从而有利于提高根据SIMO模式和MIMO模式获取的信号进行速度解模糊处理的精度。
接下来将结合附图，继续描述本申请实施例中的线性预测的方法。
图5是本申请一实施例的SIMO+MIMO模式的信号发射示意图。如图5所示，雷达在SIMO模式下发射M1个信号，在MIMO模式下通过Nt个发射天线发射Nt×M2个信号。在SIMO模式下测量的目标的最大无模糊速度范围v max、速度分辨率Δv和速度测量精度σ v分别表示为公式(1)-(3)：
Figure PCTCN2020106864-appb-000001
Figure PCTCN2020106864-appb-000002
Figure PCTCN2020106864-appb-000003
其中，λ表示发射信号波长，T c1表示SIMO模式下发射信号的PRI，SNR表示接收信号中目标的信噪比。
在MIMO模式下测量的目标的最大无模糊速度范围v max、速度分辨率Δv和速度测量精度σ v分别表示为公式(4)-(6)：
Figure PCTCN2020106864-appb-000004
Figure PCTCN2020106864-appb-000005
Figure PCTCN2020106864-appb-000006
其中，λ表示发射信号波长，T c2表示MIMO模式下发射信号的PRI，SNR表示接收信号中目标的信噪比。
在SIMO+MIMO雷达发射模式下，SIMO模式的主要作用是对MIMO模式获取的信号进行速度匹配解模糊处理，而对CFAR检测、测距测角等帮助不大。在这种情况下，SIMO的信号发射个数M1远小于MIMO雷达的信号的发射个数Nt×M2。
因此，根据公式(1)至(6)的对比可知，SIMO模式下的测量的目标的v max远大于MIMO模式的V max。但是SIMO模式下测量的Δv和σ v相对MIMO模式较差，或者说SIMO模式和MIMO模式下分别获取的目标的Δv和σ v范围不匹配，这对速度匹配解模糊的精度产生不利影响。
本申请实施例提出的信号处理的方法，可以采用线性预测(或者说线性扩展)的方法对在SIMO模式下获取的信号进行线性预测，从而达到提高SIMO的速度分辨率和速度精度的目的，进而提高了速度匹配解模糊的精度。
对在SIMO模式下获取的Nr1×M1个信号进行距离FFT处理之后，得到Nr1×M1个1维FFT数据。基于上述Nr1×M1个1维FFT数据进行线性预测，可包括但不限于以下几种情况。
(i).对所述Nr1×M1个1维FFT数据进行接收通道的相干叠加，得到M1个1维FFT数据，对所述M1个1维FFT数据进行线性预测。
(ii).根据对应的不同接收通道将所述Nr1×M1个1维FFT数据划分为Nr1组1维FFT数据，每组1维FFT数据包括M1个1维FFT数据，分别对每组1维FFT数据进行线性预测。
(iii).根据对应的不同接收通道将所述Nr1×M1个1维FFT数据划分为Nr1组1维FFT数据，每组1维FFT数据包括M1个1维FFT数据，任意选择1组1维FFT数据进行线性预测。
接下来以线性预测是针对M1个1维FFT数据进行线性预测为例进行说明。
M1个1维FFT数据可表示为[X 1，X 2，…，X M1]，每个X i(i＝1，2，…,M1)表示一个L×1的向量，L为正整数。L表示距离FFT分析的FFT点数，作为示例，L＝512或1024。
其中，线性预测包括前向预测扩展和后向预测扩展。前向预测扩展是指利用已有采样点信息或者信号对未来某一信号进行预测，后向预测扩展是指利用已有采样点信息或信号对过去某一信号进行预测。
接下来分别介绍前向预测扩展和后向预测扩展的方法。下文中将引入测量样本的概念，测量样本表示用于进行线性预测的样本信号的集合。样本信号可以包括上述1维FFT数据。例如，测量样本可以包括上述M1个1维FFT数据中的全部或部分数据，测量样本还可以包括之前若干次线性预测得到的FFT数据。例如，在第一次线性预测的计算过程中，样本信号可以在[X 1，X 2，…，X M1]中选择。
作为示例，测量样本可表示为[X M-p+1，…，X M]，其中测量样本中包括p个1维FFT 数据，p＞1。
(A)前向预测扩展
图6是本申请一实施例的对数据进行前向预测扩展的原理示意图。前向预测扩展还可以称为前向估计或前向扩展。前向预测扩展是指根据已有的测量样本[X M-p+1，…，X M]，估计第M+1个信号
Figure PCTCN2020106864-appb-000007
如图6所示，在前向预测扩展中，可以将前向估计信号表示为
Figure PCTCN2020106864-appb-000008
利用测量样本[X M-p+1，…，X M]，可以将
Figure PCTCN2020106864-appb-000009
表示为如下公式(7)。
Figure PCTCN2020106864-appb-000010
其中，a j(j＝1，2，…，p)表示传递系数。
a j的集合可以表示为估计传递系数集合A f＝[a p,…,a 2,a 1] T，a j(j＝1，2，…，p)表示传递系数。如下公式(8)示出了估计A f的推导公式。
Figure PCTCN2020106864-appb-000011
其中，X f＝[X M-p，…，X M-1]。需要说明的是，X M-p也属于已知的样本信号。
具体地，可以利用最小二乘的方法对A f进行求解。具体地，如下公式(9)所示，使公式(9)中的范数最小的解即为A f的解。
Figure PCTCN2020106864-appb-000012
通过求解公式(9)，A f的解表示为公式(10)。
Figure PCTCN2020106864-appb-000013
其中，
Figure PCTCN2020106864-appb-000014
表示X f的转置共轭矩阵。
(B)后向预测扩展
图7是本申请一实施例的对数据进行后向扩展的原理示意图。后向预测扩展还可以称为后向估计或后向扩展。
如图7所示，后向预测扩展是指根据测量样本[X M-p+1，…，X M]，估计第M-p个信号
Figure PCTCN2020106864-appb-000015
后向估计信号
Figure PCTCN2020106864-appb-000016
可以表示为公式(11)。
Figure PCTCN2020106864-appb-000017
其中，X b＝[X M-p+1，…，X M]。
b j(j＝1，2，…，p)表示后向预测扩展的传递系数。b j的集合可表示为A b＝[b p,…,b 1] T。估计出传递系数b j即可以进行信号的后向预测估计。A b可如下公式(12)表示。
Figure PCTCN2020106864-appb-000018
其中，
Figure PCTCN2020106864-appb-000019
表示X b的转置共轭矩阵。
需要说明的是，如图6和图7所示，因为测量样本[X M-p+1，…，X M]为复信号，因此，各个样本之间的复相差(或者说，相位相差)为
Figure PCTCN2020106864-appb-000020
例如，X M-1与X M之间的复相差为
Figure PCTCN2020106864-appb-000021
可选地，作为示例，在SIMO+MIMO的模式下，如果同一帧内先发射MIMO信号，再发射SIMO信号，并且MIMO信号和SIMO信号的波形、周期等参数一致的情况下，SIMO的后向估计信号存在与MIMO的测量参数重合的现象。对于重合的情况可以利用MIMO的测量结果对SIMO的估计值进行校正。
前文中结合图1至图7介绍了本申请实施例的信号处理方法，下文中将结合图8和图9，介绍本申请实施例中的装置。
图8是本申请一实施例的信号处理的装置600的示意性框图。装置600能够执行本申请图2至图4中的方法。装置600包括：
获取单元610，用于获取Nr1×M1个信号，所述Nr1×M1个信号是雷达在SIMO模式下向目标发送的M1个信号的回波信号，所述SIMO模式对应于1个发射通道和Nr1个接收通道，Nr1、M1为大于0的整数。
所述获取单元610还用于获取Nt×Nr2×M2个信号，Nt×Nr2×M2个信号是雷达在MIMO模式下向所述目标发送的M2个信号的回波信号，所述MIMO模式对应于Nt个发送通道和Nr2个接收通道，Nt、Nr2、M2为大于0的整数。
处理单元620，用于对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测和多普勒FFT分析，所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据。
所述处理单元620还用于对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，所述第二信号处理包括距离FFT分析和多普勒FFT分析。
所述处理单元620还用于根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
可选地，上述装置600可以包括图1中的雷达100。上述获取单元610和上述处理单元620可以被图1中的处理单元130。其中获取单元610获取的信号可以是通过图1中的接收机120接收的信号。
图9是本申请一实施例的信号处理的装置700的示意性框图。装置700能够执行本申请图2至图4中的方法。装置700包括：一个或多个存储器710，一个或多个通信接口720，一个或多个处理器730。该处理器730用于控制通信接口720收发信号，该存储器710用于存储计算机程序，该处理器730用于从存储器2010中调用并运行该计算机程序，使得该装置700执行本申请的方法实施例中的相应流程和/或操作。例如，装置700可以执行图2-图4中执行的步骤，为了简洁，此处不再赘述。
可选地，上述装置700可以包括图1中的雷达100。上述处理器730可以包括图1中的处理单元130。上述通信接口720可以包括图1中的接收机120。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可 以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。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或者该技术方案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一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (18)

  1. 一种信号处理方法，其特征在于，包括：
    获取Nr1×M1个信号，所述Nr1×M1个信号是雷达在单输入多输出SIMO模式下向目标发送的M1个信号的回波信号，所述SIMO模式对应于1个发射通道和Nr1个接收通道，Nr1、M1为大于1的整数；
    获取Nt×Nr2×M2个信号，所述Nt×Nr2×M2个信号是雷达在多输入多输出MIMO模式下向所述目标发送的M2个信号的回波信号，所述MIMO模式对应于Nt个发送通道和Nr2个接收通道，Nt、Nr2、M2为大于1的整数；
    对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，所述第一信号处理包括依次进行以下处理：距离快速傅里叶变换FFT分析、线性预测和多普勒FFT分析，所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据；
    对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，所述第二信号处理包括距离FFT分析和多普勒FFT分析；
    根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
  2. 如权利要求1所述的方法，其特征在于，所述第一信号处理包括依次进行以下处理：距离FFT分析、接收通道的相干叠加、线性预测、多普勒FFT分析、恒虚警检测CFAR。
  3. 如权利要求1所述的方法，其特征在于，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测、多普勒FFT分析、信号叠加、CFAR。
  4. 如权利要求1至3中任一项所述的方法，其特征在于，所述第二信号处理还包括依次进行以下处理：距离FFT分析、多普勒FFT分析、信号叠加、CFAR。
  5. 如权利要求2所述的方法，其特征在于，对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，包括：
    对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；
    对所述Nr1×M1个1维FFT数据进行接收通道的相干叠加，得到M1个1维FFT数据；
    对所述M1个1维FFT数据进行线性预测，得到M1+Y个1维FFT数据，Y为正整数；
    对所述M1+Y个1维FFT数据进行多普勒FFT分析，得到1个距离-多普勒频谱图；
    对所述1个距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
  6. 如权利要求3所述的方法，其特征在于，对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，包括：
    对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；
    基于不同的接收通道，分别对所述Nr1×M1个1维FFT数据进行线性预测，得到Nr1×(M1+Y)个1维FFT数据，Y为正整数；
    基于不同的接收通道，分别对所述Nr1×(M1+Y)个1维FFT数据进行多普勒FFT分析，获得Nr1个第一距离-多普勒频谱图；
    对Nr1个第一距离-多普勒频谱图进行信号叠加，获得1个第二距离-多普勒频谱图；
    对所述1个第二距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
  7. 如权利要求4所述的方法，其特征在于，所述对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，包括：
    对所述Nt×Nr2×M2个信号进行距离FFT分析，得到Nt×Nr2×M2个1维FFT数据；
    对所述Nt×Nr2×M2个1维FFT数据分别进行多普勒FFT分析，获得Nt×Nr2个第三距离-多普勒频谱图；
    对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，获得1个第四距离-多普勒频谱图；
    对所述1个第四距离-多普勒频谱图进行CFAR检测，得到所述第二处理数据。
  8. 一种信号处理的装置，其特征在于，包括：
    获取单元，用于获取Nr1×M1个信号，所述Nr1×M1个信号是雷达在单输入多输出SIMO模式下向目标发送的M1个信号的回波信号，所述SIMO模式对应于1个发射通道和Nr1个接收通道，Nr1、M1为大于1的整数；
    所述获取单元还用于获取Nt×Nr2×M2个信号，所述Nt×Nr2×M2个信号是雷达在多输入多输出MIMO模式下向所述目标发送的M2个信号的回波信号，所述MIMO模式对应于Nt个发送通道和Nr2个接收通道，Nt、Nr2、M2为大于1的整数；
    处理单元，用于对所述Nr1×M1个信号进行第一信号处理，以获取第一处理数据，所述第一信号处理包括依次进行以下处理：距离快速傅里叶变换FFT分析、线性预测和多普勒FFT分析，所述线性预测用于预测经过距离FFT分析得到的FFT数据的时域之前或之后的FFT数据；
    所述处理单元还用于对所述Nt×Nr2×M2个信号进行第二信号处理，以获取第二处理数据，所述第二信号处理包括距离FFT分析和多普勒FFT分析；
    所述处理单元还用于根据所述第一处理数据和第二处理数据，进行速度匹配解模糊处理。
  9. 如权利要求8所述的装置，其特征在于，所述第一信号处理包括依次进行以下处理：距离FFT分析、接收通道的相干叠加、线性预测、多普勒FFT分析、恒虚警检测CFAR。
  10. 如权利要求8所述的装置，其特征在于，所述第一信号处理包括依次进行以下处理：距离FFT分析、线性预测、多普勒FFT分析、信号叠加、CFAR。
  11. 如权利要求8至10中任一项所述的装置，其特征在于，所述第二信号处理包括依次进行以下处理：距离FFT分析、多普勒FFT分析、信号叠加、CFAR。
  12. 如权利要求9所述的装置，其特征在于，所述处理单元具体用于：对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；对所述Nr1×M1个1维FFT数据进行接收通道的相干叠加，得到M1个1维FFT数据；对所述M1个1维FFT数据进行线性预测，得到M1+Y个1维FFT数据，Y为正整数；对所述M1+Y个1维FFT数据进行多普勒FFT分析，得到1个距离-多普勒频谱图；对所述1个距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
  13. 如权利要求10所述的装置，其特征在于，所述处理单元具体用于：对所述Nr1×M1个信号进行距离FFT分析，得到Nr1×M1个1维FFT数据；基于不同的接收通道，分别对所述Nr1×M1个1维FFT数据进行线性预测，得到Nr1×(M1+Y)个1维FFT数据； 基于不同的接收通道，分别对所述Nr1×(M1+Y)个1维FFT数据进行多普勒FFT分析，获得Nr1个第一距离-多普勒频谱图，Y为正整数；对Nr1个第一距离-多普勒频谱图进行信号叠加，获得1个第二距离-多普勒频谱图；对所述1个第二距离-多普勒频谱图进行CFAR检测，得到所述第一处理数据。
  14. 如权利要求11所述的装置，其特征在于，所述处理单元具体用于：对所述Nt×Nr2×M2个信号进行距离FFT分析，得到Nt×Nr2×M2个1维FFT数据；对所述Nt×Nr2×M2个1维FFT数据分别进行多普勒FFT分析，获得Nt×Nr2个第三距离-多普勒频谱图；对Nt×Nr2个第三距离-多普勒频谱图进行信号叠加，获得1个第四距离-多普勒频谱图；对所述1个第四距离-多普勒频谱图进行CFAR检测，得到所述第二处理数据。
  15. 一种信号处理的装置，包括：处理器，所述处理器与存储器耦合；
    所述存储器，用于存储计算机程序；
    所述处理器，用于执行所述存储器中存储的计算机程序，以使得所述装置执行如权利要求1-7中任一项所述的方法。
  16. 一种可读存储介质，其特征在于，包括程序或指令，当所述程序或指令在计算机上运行时，如权利要求1-7中任意一项所述的方法被执行。
  17. 一种雷达系统，其特征在于，包括处理器和接收器，所述处理器用于执行如权利要求1-7中任意一项所述的方法。
  18. 一种智能车，其特征在于，包括处理器和接收器，所述处理器用于执行如权利要求1-7中任意一项所述的方法。
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