WO2017161874A1 - 一种mimo雷达波达方向估计方法和装置 - Google Patents

一种mimo雷达波达方向估计方法和装置 Download PDF

Info

Publication number
WO2017161874A1
WO2017161874A1 PCT/CN2016/103285 CN2016103285W WO2017161874A1 WO 2017161874 A1 WO2017161874 A1 WO 2017161874A1 CN 2016103285 W CN2016103285 W CN 2016103285W WO 2017161874 A1 WO2017161874 A1 WO 2017161874A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
matrix
dimensionality reduction
mimo radar
reduction processing
Prior art date
Application number
PCT/CN2016/103285
Other languages
English (en)
French (fr)
Inventor
王会文
Original Assignee
中兴通讯股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中兴通讯股份有限公司 filed Critical 中兴通讯股份有限公司
Publication of WO2017161874A1 publication Critical patent/WO2017161874A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • 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
    • G01S7/42—Diversity systems specially adapted for radar
    • 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

Definitions

  • the invention relates to a direction-of-arrival estimation technique, in particular to a method and a device for estimating a direction of arrival of a multiple-input multiple-output (MIMO) radar.
  • MIMO multiple-input multiple-output
  • DOA estimation is one of the important research contents of array signal processing, and has important application value in sonar, radar, wireless communication systems and other fields.
  • the DOA estimation methods such as Multiple Signal Classification (MUSIC) algorithm and Estimating Signal Parameter via Rotational In variance Techniques (ESPRIT) algorithm are applied. It is more extensive, but the performance of this type of algorithm drops sharply in the case of low SNR and small snapshot, which is not suitable for increasingly complex communication environments.
  • MUSIC Multiple Signal Classification
  • ESPRIT Estimating Signal Parameter via Rotational In variance Techniques
  • the current DOA estimation algorithm based on compressed sensing theory includes an l1-SVD algorithm proposed for DOA estimation (see IEEE Trans.Signal Process.2005, 53(8): 3010-3022), l1-SRACV algorithm (see IEEE Trans. Signal Process. 2011, 59(2): 629-638) and CMSR algorithm (see IEEE Trans. Aerosp. Electron. Syst. 2013, 49) (3)), real-domain l1-SVD algorithm (see IEEE Antennas Wireless Propag. Lett. 2013, 12: 376-379), however these algorithms have higher computational complexity.
  • embodiments of the present invention are expected to provide a method and apparatus for estimating a direction of arrival of a MIMO radar, which can reduce computational complexity when performing MIMO radar direction of arrival estimation.
  • the embodiment of the invention provides a direction-of-arrival estimation method for a MIMO radar with multiple inputs and multiple outputs.
  • the antenna array of the MIMO radar is an M-ary uniform linear array for transmitting signals and receiving echo signals; the method includes:
  • the sparse representation model of the received data is constructed based on the reduced-dimensional processed data, and the direction of arrival of the MIMO radar is estimated based on the constructed sparse representation model.
  • the using the reversible matrix to perform the dimension reduction processing on the received data to obtain the data after the dimension reduction processing including:
  • the first dimensionality reduction processing is performed on the received data by using a preset dimensionality reduction matrix, and the first dimensionality reduction data is obtained; the second dimensionality reduction processing is performed on the first dimensionality reduction data by using the invertible matrix, and the dimensionality reduction processing is obtained. After the data.
  • the second dimension reduction processing is performed on the first dimensionality reduction data by using the invertible matrix to obtain the dimension reduction processed data, including: multiplying the invertible matrix by the first dimensionality reduction data , the data after the dimension reduction process is obtained.
  • the first dimension reduction processing is performed on the received data by using a preset dimensionality reduction matrix, and the first dimensionality reduction data is obtained, including: the preset dimensionality reduction matrix is compared with the received data represented by the matrix form. Multiply, the first dimensionality reduction data is obtained.
  • the constructing the reversible matrix related to the number of MIMO radar antenna array elements includes:
  • W diag (1, 2, ..., M, M-1, ..., 2, 1)
  • diag ( ⁇ ) represents a diagonal matrix composed of elements in parentheses as the main diagonal elements
  • the -1/2 power W - 1/2 of the matrix W is taken as the invertible matrix related to the number of MIMO radar antenna array elements.
  • the sparse representation of the received data is constructed based on the reduced-dimensionality processed data.
  • the model includes:
  • the dimensionality-reduced data is represented in a matrix form, and the singular value decomposition is performed on the dimensionality-reduced data represented by the matrix form to obtain the decomposed data; the redundant dictionary is constructed, based on the decomposed data and the constructed data.
  • a redundant dictionary constructing a sparse representation model of the received data.
  • the embodiment of the present invention further provides a MIMO radar direction-of-arrival estimation apparatus, where the antenna array of the MIMO radar is an M-ary uniform line array for transmitting signals and receiving echo signals; the apparatus includes: a filter processing module, a dimension reduction processing module, and an estimation module; wherein
  • a filtering processing module configured to perform matched filtering processing on the echo signal to obtain received data
  • a dimension reduction processing module is configured to construct a reversible matrix related to the number of MIMO radar antenna array array elements, and use the reversible matrix to perform dimensionality reduction processing on the received data to obtain data after dimension reduction processing;
  • the estimation module is configured to construct a sparse representation model of the received data based on the reduced-dimensionality processed data, and estimate a direction of arrival of the MIMO radar based on the constructed sparse representation model.
  • the dimension reduction processing module is specifically configured to perform first dimension reduction processing on the received data by using a preset dimensionality reduction matrix to obtain first dimensionality reduction data, and use the invertible matrix to first reduce the dimension.
  • the data is subjected to the second dimension reduction process to obtain the data after the dimension reduction process.
  • the dimension reduction processing module is specifically configured to multiply the invertible matrix and the first dimension reduction data to obtain data after dimension reduction processing.
  • the dimensionality reduction processing module is further configured to use the -1/2 power W - 1/2 of the matrix W as the reversible matrix related to the number of MIMO radar antenna array elements.
  • the estimating module is specifically configured to use the reduced-dimensional processed data as a moment
  • the matrix form indicates that the reduced-dimensional processed data represented by the matrix form is subjected to singular value decomposition to obtain the decomposed data; the redundant dictionary is constructed, and the received data is constructed based on the decomposed data and the constructed redundant dictionary. Sparse representation model.
  • a storage medium is also provided.
  • the storage medium is arranged to store program code for performing the following steps:
  • Receiving an echo signal by using a MIMO radar antenna array performing matched filtering processing on the echo signal to obtain received data; constructing a reversible matrix related to the number of MIMO radar antenna array array elements, and using the reversible matrix to receive the received data
  • the data is subjected to dimensionality reduction processing to obtain data after dimension reduction processing; the sparse representation model of the received data is constructed based on the data after dimensionality reduction processing, and the direction of arrival of the MIMO radar is estimated based on the constructed sparse representation model.
  • the storage medium is further arranged to store program code for performing the following steps:
  • the first dimensionality reduction processing is performed on the received data by using a preset dimensionality reduction matrix, and the first dimensionality reduction data is obtained; the second dimensionality reduction processing is performed on the first dimensionality reduction data by using the invertible matrix, and the dimensionality reduction processing is obtained. After the data.
  • the storage medium is further arranged to store program code for performing the following steps:
  • the storage medium is further arranged to store program code for performing the following steps:
  • the storage medium is further arranged to store program code for performing the following steps:
  • the dimensionality-reduced data is represented in a matrix form, and the singular value decomposition is performed on the dimensionality-reduced data represented by the matrix form to obtain the decomposed data; the redundant dictionary is constructed, based on the decomposed data and the constructed data.
  • a redundant dictionary constructing a sparse representation model of the received data.
  • Embodiments of the present invention provide a method and apparatus for estimating a direction of arrival of a MIMO radar, which use an MIMO radar antenna array to receive an echo signal, and perform matching filtering on the echo signal. And obtaining the received data; constructing a reversible matrix related to the number of MIMO radar antenna array elements, and using the reversible matrix to perform dimensionality reduction processing on the received data to obtain data after dimension reduction processing; The data constructs a sparse representation model of the received data, and estimates a direction of arrival of the MIMO radar based on the constructed sparse representation model; thus, the received data may be subjected to dimensionality reduction processing, and the direction of arrival estimation is performed based on the data after the dimension reduction processing , reduces the computational complexity when performing MIMO radar direction of arrival estimation.
  • FIG. 1 is a flowchart of a first embodiment of a method for estimating a direction of arrival of a MIMO radar according to the present invention
  • FIG. 2 is a schematic structural diagram of a signal transceiving channel in a second embodiment of the present invention.
  • FIG. 3 is a schematic diagram of an application scenario of vehicle obstacle detection in a second embodiment of the present invention.
  • FIG. 4 is a flowchart of a vehicle obstacle detecting method according to a second embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a MIMO radar direction of arrival estimating apparatus according to an embodiment of the present invention.
  • a first embodiment of the present invention provides a method for estimating a direction of arrival of a MIMO radar.
  • the antenna array of the MIMO radar is an M-ary uniform line array for transmitting signals and receiving echo signals, and M is greater than 1; that is, for In the MIMO radar, each array element of the M-ary uniform linear array is used not only for transmitting signals but also for receiving corresponding echo signals; it can be understood that the M-element
  • the spacing between adjacent array elements of the uniform line array is equal, and the spacing of the array elements of the M-ary uniform linear array is recorded as d.
  • FIG. 1 is a flowchart of a first embodiment of a method for estimating a direction of arrival of a MIMO radar according to the present invention. As shown in FIG. 1, the process includes:
  • Step 100 Receive an echo signal by using a MIMO radar antenna array, perform matched filtering processing on the echo signal, and obtain received data.
  • each array element of the MIMO radar antenna array transmits a signal with a burst number of L, and signals transmitted by any two array elements of the MIMO radar antenna array are orthogonal to each other, and each array of the MIMO radar antenna array
  • the signal transmitted by the element can be represented as a row vector of L columns, and the signal transmitted by the mth array element of the MIMO radar antenna array is recorded as s m (t), m is taken as 1 to M, and the signal received at the obstacle is y Expressed as:
  • y is a real matrix of size 1 ⁇ L
  • L represents the number of snapshots of the transmitted signal
  • superscript T represents the transposition of the matrix or vector
  • a( ⁇ ) is the received signal direction vector corresponding to the azimuth angle ⁇
  • the echo signal X received by the MIMO radar antenna array is:
  • N 1 is a noise matrix of size M ⁇ L
  • X is a real matrix of size M ⁇ L
  • the azimuths of the K roadblocks relative to the MIMO antenna array are expressed as ⁇ 1 to ⁇ K , wherein the k-th roadblock is expressed as ⁇ k with respect to the azimuth angle of the MIMO antenna array. 1 to K; at this time, the echo signal X received by the MIMO radar antenna array is:
  • the superscript T represents the transposition of the matrix or the vector
  • X m represents the signal received by the mth array element of the MIMO antenna array
  • m takes 1 to M
  • ⁇ k represents the attenuation in the signal transceiving process corresponding to the kth roadblock Coefficient
  • N 3 is a noise matrix of size M x L.
  • Each of the array elements of the MIMO antenna array is connected with a matched filter bank for performing matched filtering processing on the echo signals received by the corresponding array elements, and the number of matched filters in the matched filter group can be set according to actual conditions.
  • the number of matched filters in the matched filter bank can be set to M.
  • the output signal X m of the m-th array element of the MIMO antenna array after matched filtering processing is:
  • N 4 is a noise vector having a size of 1 ⁇ L.
  • the signals orthogonal to the matched filter characteristic functions are filtered out, and the remaining signals are retained.
  • the output signals of each array element of the MIMO antenna array after being matched and filtered are vectorized and sequentially stacked to obtain the received data Y obtained by the matched filtering process after the matched filtering process is performed:
  • Y represents the received data obtained by performing matched filtering processing on the echo signal, That is, Y is a complex matrix of size M 2 ⁇ L, L represents the number of snapshots of the transmitted signal; a r ( ⁇ k ) represents the reception direction vector corresponding to the azimuth angle ⁇ k , and a t ( ⁇ k ) represents the azimuth angle ⁇ k Corresponding transmission direction vector, k takes 1 to K; Represents the Kronecker product of the matrix; A R is the receive array flow matrix, and A T is the transmit array flow matrix, The Khatri-Rao product of the matrix is represented.
  • the receiving array and the transmitting array are the same, and are all M-ary uniform linear arrays of the MIMO radar;
  • A represents the equivalent array flow pattern matrix, That is, A is a complex matrix of size M 2 ⁇ K; N 5 represents a noise matrix, N 5 is a matrix of size M 2 ⁇ L; H represents a matrix of attenuation coefficients, and H is a complex matrix of size K ⁇ L, H
  • the expansion can be expressed as:
  • ⁇ kl represents the first element of the matrix H k l column line.
  • T represents the transpose of a matrix or a vector
  • G is a real matrix of size M 2 ⁇ (2M-1)
  • the expansion of G is:
  • G is a matrix related only to the number M of MIMO radar antenna array elements.
  • the element of the a+b line of the aM+b line is 1 and the remaining elements are 0.
  • a takes 0 to M- 1
  • b takes 1 to M.
  • the definition matrix W is G H G, ie Then the expansion of W can be expressed as:
  • diag( ⁇ ) represents a matrix composed of elements in parentheses as the main diagonal elements.
  • Step 101 Construct an invertible matrix related to the number of array elements of the MIMO radar antenna array, and perform dimensionality reduction processing on the received data by using the invertible matrix to obtain data after dimension reduction processing.
  • the step specifically includes: performing a first dimensionality reduction processing on the received data by using a preset dimensionality reduction matrix to obtain first dimensionality reduction data; and using the invertible matrix to perform a second dimensionality reduction processing on the first dimensionality reduction data, The data after dimension reduction processing is obtained.
  • the received data is represented in a matrix form, and the preset dimensionality reduction matrix is multiplied by the received data represented in a matrix form to obtain the first dimensionality reduction data; obviously, The first dimensionality reduction data is also represented in matrix form.
  • the preset dimensionality reduction matrix is: W - 1/2 G H
  • the first dimensionality reduction data can be expressed as:
  • Z represents the first dimensionality reduction data
  • Z is a complex matrix of size (2M-1) ⁇ L
  • W -1/2 represents the -1/2 power of matrix W
  • superscript H represents the conjugate of the matrix Transpose
  • Y represents the received data obtained by performing matched filtering on the echo signal
  • B [b( ⁇ 1 ), b( ⁇ 2 ), ..., b( ⁇ K )]
  • N y W -1/2 G H N 5
  • N y is the noise matrix.
  • the second dimension reduction processing is performed on the first dimensionality reduction data by using the invertible matrix to obtain the dimension reduction processed data, including: multiplying the invertible matrix by the first dimensionality reduction data. , the data after the dimension reduction process is obtained.
  • the reversible matrix constructed only with respect to the number of MIMO radar antenna array elements is W - 1/2 due to Obviously, W 1/2 is a full rank diagonal matrix, ie the matrix W 1/2 is reversible.
  • the dimensionality-reduced data can be expressed as:
  • Z represents the simple reducing the dimension data
  • Z is simply a size of (2M-1) ⁇ L complex matrix
  • N Jane W -1/2 N y
  • N is the noise matrix Jane.
  • the process of obtaining the data after the dimension reduction processing does not affect the orientation information of the data received by the MIMO radar antenna array, and thus, the active signal can be received.
  • the model is transformed into a passive signal reception model.
  • the size of the received data matrix is reduced from M 2 ⁇ K to (2M-1) ⁇ K, so that the data is estimated based on the dimension after the dimensionality reduction process. Can effectively reduce the computational complexity.
  • Step 102 Construct a sparse representation model of the received data based on the reduced-dimensionality processed data, and estimate a direction of arrival of the MIMO radar based on the constructed sparse representation model.
  • constructing the sparse representation model of the received data based on the reduced-dimensionality processed data includes: expressing the reduced-dimensional processed data in a matrix form, and performing singular values on the reduced-dimensional processed data represented by the matrix form (SVD) Decomposing, decomposing the data; constructing a redundancy dictionary, constructing a sparse representation model of the received data based on the decomposed data and the constructed redundancy dictionary.
  • SVD matrix form
  • each column of the matrix B corresponds to one direction of arrival.
  • N angle values is greater than K; for example, for a spatial domain, by dividing the angle, N angle values ⁇ 1 , ⁇ 2 , ..., ⁇ N are obtained , for example, when N takes 181, from 0
  • the degree begins to increase gradually in steps of 1 degree up to 180 degrees.
  • redundancy dictionary B s is a matrix of size (2M-1) ⁇ N.
  • the sparse representation model of the received data is:
  • the constructed sparse representation model of the received data is referred to as a Single Measurement Vectors (SMV) model; conversely, if H s is not an N ⁇ 1 dimensional vector Then, the constructed sparse representation model of the received data is called a Multiple Measurement Vectors (MMV) model.
  • SMV Single Measurement Vectors
  • MMV Multiple Measurement Vectors
  • estimating the direction of arrival of the MIMO radar based on the constructed sparse representation model includes: reconstructing the constructed sparse representation model based on the reconstruction theory based on the compressed sensing theory Solution, the estimated value of the MIMO radar direction of arrival is obtained.
  • the spectral function P of the MIMO radar direction of arrival estimation can be obtained:
  • H s [i ,:] denotes the i-th row of H s
  • 2 represents H s [i ,:] minimum 2-norm.
  • the estimated value of the MIMO radar's direction of arrival can be obtained according to the corresponding angle of the spectral function P peak.
  • the MIMO radar direction of arrival estimation method can perform multiple dimensionality reduction processing on the received data, and perform the direction of arrival estimation based on the data after the dimensionality reduction processing, thereby reducing the direction of the MIMO radar.
  • the embodiments of the present invention can be used in the field of vehicle obstacle detection, base station-to-terminal detection, radar/sound detection, and the like; the following describes the vehicle obstacle detection field as an example.
  • Vehicles traveling on the road need to have a good sense of the surrounding environment, including the perception of the road structure, the detection of other dynamic obstacles, etc.; reliable environmental perception capabilities for autonomous cruise control, collision warning and path planning Crucial role.
  • the existing vehicle obstacle detection technology includes map difference method, entity clustering method and target tracking method.
  • the map difference method solves the obstacle motion information according to the distribution characteristics of obstacles on the map or the grid map at different times, and the calculation data amount is large. Real-time non-high robustness, and often requires networking, posing a threat to safe driving in remote mountainous areas without network coverage and no navigation signal coverage; entity clustering and target tracking methods require storage and computational information It is very large, and the reliability is too bad when the environmental noise is serious.
  • roadblocks can be detected by the onboard radar system.
  • the onboard radar system can only detect the roadblock in front of the radar and cannot detect the angle of the roadblock in front of the driverless car.
  • a second embodiment of the present invention provides a vehicle obstacle detection method, in which a MIMO radar antenna array is provided on the vehicle, and the MIMO radar antenna array and the MIMO of the first embodiment of the present invention are provided.
  • the radar antenna array is the same and will not be repeated here; the first signal transceiving channel to the Mth signal transceiving channel, the transceiver and the digital signal processing (DSP) are also provided on the vehicle.
  • DSP digital signal processing
  • the mth signal transceiving channel includes an mth frequency selective switch, an mth duplexer, and an mth power amplifier (Power Amplifier, PA). ), the mth low noise amplifier (LNA), the mth matched filter bank, m takes 1 to M.
  • PA Power Amplifier
  • the digital signal processor is arranged to control the transceiver to transmit M two orthogonal signals S1, S2..., SM, the transceiver sends the signal Sm to the mth power amplifier, and the mth power amplifier is set to The signal from the transceiver is amplified, and the amplified signal is sent to the mth duplexer; the mth duplexer is set to send the signal from the mth power amplifier to the mth frequency selective switch, the mth frequency selection
  • the switch is configured to send the signal from the mth duplexer to the mth array of the MIMO radar antenna array, the mth frequency selection switch is set to select the signal transmission frequency, and control the MIMO radar antenna array according to the selected signal transmission frequency.
  • the m element emits a signal.
  • the mth frequency selective switch is connected to the mth array element of the MIMO radar antenna array, and the mth frequency selective switch, the mth duplexer, the mth low noise amplifier, and the mth matched filter group constitute the mth receiving signal channel.
  • the mth frequency selective switch is set to select the frequency of the mth array receiving signal of the MIMO radar antenna array, and the mth frequency selective switch is connected to the mth low noise amplifier through the mth duplexer,
  • the m low noise amplifier is arranged to amplify the received signal from the duplexer and send the amplified signal to the mth matched filter bank, and the mth matched filter bank is set to perform signal from the mth low noise amplifier Matching filtering, filtering out signals orthogonal to the matched filter characteristic function, and transmitting the matched filtered signal to the transceiver, the transceiver being arranged to send the signal from the mth matched filter bank to the digital signal processing
  • the digital signal processor is configured to detect obstacles based on signals from the transceiver and to plan driving directions.
  • the mth duplexer is set to achieve isolation of the transmitted and received signals.
  • FIG. 3 is a schematic diagram of an application scenario of vehicle obstacle detection in a second embodiment of the present invention.
  • the vehicle 1 is represented by a numeral 1
  • the vehicle 2 is represented by a numeral 2
  • the vehicle 3 is represented by a numeral 3.
  • the MIMO radar antenna array provided on the vehicle 2 transmits a signal, and the obstacle detection is performed based on the received echo signal.
  • FIG. 4 is a flowchart of a method for detecting an obstacle of a vehicle according to a second embodiment of the present invention. As shown in FIG. 4, the flow includes:
  • Step 400 Obtain a beam width and a direction when a MIMO radar antenna array transmits a signal on the vehicle, and obtain a beam width and a direction when the MIMO radar antenna array receives a signal on the vehicle;
  • the beamforming algorithm can be used to set the beam width and the direction when the MIMO radar antenna array transmits signals on the vehicle, and the beam when the MIMO radar antenna array on the vehicle receives the signal. Width and pointing.
  • the distance of the vehicle itself to the curb can be obtained by a distance sensor or camera on the vehicle.
  • Step 401 The MIMO radar antenna array transmits a signal and receives a corresponding echo signal.
  • the transmitted signal returns to the MIMO radar antenna array when encountering an obstacle.
  • step 100 The specific implementation manner of this step has been explained in step 100, and details are not described herein again.
  • Step 402 Estimate the direction of arrival of the MIMO radar based on the received echo signals.
  • the direction of arrival of the MIMO radar can be estimated using a digital signal processor.
  • Step 403 Calculate the direction of the obstacle according to the estimated value of the direction of arrival of the MIMO radar.
  • the digital signal processor provided on the vehicle can be used to calculate the direction of the obstacle and plan the driving route.
  • the DOA estimation is widely used in many fields such as wireless communication, radar, navigation, sonar, astronomy and biomedical engineering. It is one of the important research contents of high-resolution array signal processing.
  • the second embodiment of the present invention adopts the direction of arrival.
  • the estimation method performs vehicle obstacle direction detection, thereby providing driving route planning information to the vehicle, and improving vehicle driving safety performance.
  • the vehicle obstacle detection method according to the second embodiment of the present invention can accurately detect the direction of the obstacle around the vehicle by using the limited antenna array element, and at the same time, the second invention.
  • the method for estimating the direction of arrival of the MIMO radar applied in the embodiment has the characteristics of low computational complexity, and is beneficial to meet the real-time demand for obstacle detection in high-speed vehicles.
  • a third embodiment of the present invention provides a MIMO radar direction of arrival estimation apparatus.
  • the antenna array of the MIMO radar is an M-ary uniform linear array for transmitting signals and receiving echo signals, and M is greater than 1;
  • the radar antenna array is used to receive echo signals.
  • the device includes: a filter processing module 500, a dimension reduction processing module 501, and an estimation module 502;
  • the filter processing module 500 is configured to perform matched filtering processing on the echo signal to obtain received data.
  • the dimension reduction processing module 501 is configured to construct an invertible matrix related to the number of MIMO radar antenna array elements, and perform dimension reduction processing on the received data by using the invertible matrix to obtain data after dimension reduction processing;
  • the estimating module 502 is configured to construct the sparse data of the received data based on the data after the dimension reduction processing
  • the representation model estimates the direction of arrival of the MIMO radar based on the constructed sparse representation model.
  • the dimension reduction processing module 501 is configured to perform first dimension reduction processing on the received data by using a preset dimensionality reduction matrix to obtain first dimension reduction data, and use the invertible matrix to compare the first dimension reduction data.
  • the second dimension reduction process is performed to obtain the data after the dimension reduction process.
  • the dimension reduction processing module 501 is specifically configured to multiply the invertible matrix and the first dimension reduction data to obtain data after dimension reduction processing.
  • the dimensionality reduction processing module 501 is further configured to use the -1/2 power W - 1/2 of the matrix W as the reversible matrix related to the number of MIMO radar antenna array elements.
  • the estimating module 502 is specifically configured to represent the reduced-dimensional processed data in a matrix form, perform singular value decomposition on the reduced-dimensional processed data represented by the matrix form, and obtain the decomposed data; construct a redundant dictionary, based on The decomposed data and the constructed redundancy dictionary construct a sparse representation model of the received data.
  • the filter processing module 500, the dimensionality reduction processing module 501, and the estimation module 502 may each be a Central Processing Unit (CPU), a Micro Processor Unit (MPU), and a digital signal processor ( Digital Signal Processor (DSP), or Field Programmable Gate Array (FPGA) implementation.
  • CPU Central Processing Unit
  • MPU Micro Processor Unit
  • DSP Digital Signal Processor
  • FPGA Field Programmable Gate Array
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • an OFDM radar antenna array is used to receive an echo signal, and the echo signal is matched and filtered to obtain received data; and the MIMO radar antenna array element is constructed.
  • a number-related reversible matrix using the reversible matrix to perform dimensionality reduction processing on the received data to obtain data after dimension reduction processing; constructing a sparse representation model of the received data based on the dimension reduction processed data, based on the constructed sparseness
  • the representation model estimates the direction of arrival of the MIMO radar; thus, the received data can be subjected to dimensionality reduction processing, and the direction of arrival estimation is performed based on the data after the dimension reduction processing, thereby reducing the computational complexity when performing MIMO radar direction estimation.

Landscapes

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

Abstract

一种MIMO雷达波达方向估计方法和估计装置，MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵；利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据（100）；构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据（101）；基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计（102）。

Description

一种MIMO雷达波达方向估计方法和装置 技术领域
本发明涉及波达方向估计技术，尤其涉及一种多输入多输出(Multiple-Input Multiple-Output，MIMO)雷达波达方向估计方法和装置。
背景技术
波达方向(Direction of arrival，DOA)估计作为阵列信号处理的重要研究内容之一，在声纳，雷达，无线通信系统等领域具有重要的应用价值。目前，现有的DOA估计理论已经发展得相对成熟，DOA估计方法如多重信号分类(Multiple Signal Classification，MUSIC)算法、旋转不变子空间算法(Estimating Signal Parameter via Rotational In variance Techniques，ESPRIT)算法应用比较广泛，但是在低信噪比、小快拍情况下该类算法性能急剧下降，不适合日趋复杂的通信环境。
为了解决上述问题，提出了基于压缩感知理论的DOA估计算法；目前研究的基于压缩感知理论的DOA估计算法包括：针对DOA估计提出的一种l1-SVD算法(参见IEEE Trans.Signal Process.2005，53(8)：3010-3022)、l1-SRACV算法(参见IEEE Trans.Signal Process.2011，59(2)：629-638)和CMSR算法(参见IEEE Trans.Aerosp.Electron.Syst.2013，49(3))、实域l1-SVD算法(参见IEEE Antennas Wireless Propag.Lett.2013，12：376-379)，然而这些算法运算复杂度较高。
发明内容
为解决上述技术问题，本发明实施例期望提供一种MIMO雷达波达方向估计方法和装置，能够降低进行MIMO雷达波达方向估计时的运算复杂度。
本发明的技术方案是这样实现的：
本发明实施例提供了一种多输入多输出MIMO雷达波达方向估计方 法，所述MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵；所述方法包括：
利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据；
构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；
基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
上述方案中，所述利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据，包括：
利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
上述方案中，所述利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据，包括：将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
上述方案中，所述利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据，包括：将预设的降维矩阵与以矩阵形式表示的接收数据相乘，得出第一降维数据。
上述方案中，所述构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，包括：
定义矩阵W，W＝diag(1，2，…，M，M-1，…，2，1)，diag(·)表示以括号中元素为主对角线元素而构成的对角矩阵；
将矩阵W的-1/2次方W-1/2作为所述与MIMO雷达天线阵列阵元个数有关的可逆矩阵。
上述方案中，所述基于降维处理后数据构造所述接收数据的稀疏表示 模型包括：
将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
本发明实施例还提供了一种多输入多输出MIMO雷达波达方向估计装置，所述MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵；所述装置包括：滤波处理模块、降维处理模块和估计模块；其中，
滤波处理模块，设置为对所述回波信号进行匹配滤波处理，得出接收数据；
降维处理模块，设置为构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；
估计模块，设置为基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
上述方案中，所述降维处理模块，具体设置为利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
上述方案中，所述降维处理模块，具体设置为将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
上述方案中，所述降维处理模块，具体设置为定义矩阵W，W＝diag(1，2，…，M，M-1，…，2，1)，diag(·)表示以括号中元素为主对角线元素而构成的对角矩阵；
所述降维处理模块，还设置为将矩阵W的-1/2次方W-1/2作为所述与MIMO雷达天线阵列阵元个数有关的可逆矩阵。
上述方案中，所述估计模块，具体设置为将所述降维处理后数据以矩 阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
根据本发明的又一个实施例，还提供了一种存储介质。该存储介质设置为存储用于执行以下步骤的程序代码：
利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据；构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
可选地，存储介质还设置为存储用于执行以下步骤的程序代码：
利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
可选地，存储介质还设置为存储用于执行以下步骤的程序代码：
将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
可选地，存储介质还设置为存储用于执行以下步骤的程序代码：
将预设的降维矩阵与以矩阵形式表示的接收数据相乘，得出第一降维数据。
可选地，存储介质还设置为存储用于执行以下步骤的程序代码：
将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
本发明实施例提供了一种MIMO雷达波达方向估计方法和装置，利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处 理，得出接收数据；构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计；如此，可以对接收数据进行降维处理，基于降维处理后的数据进行波达方向估计，降低了进行MIMO雷达波达方向估计时的运算复杂度。
附图说明
此处所说明的附图用来提供对本发明的进一步理解，构成本申请的一部分，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1为本发明MIMO雷达波达方向估计方法的第一实施例的流程图；
图2为本发明第二实施例中信号收发通道的结构示意图；
图3为本发明第二实施例中车辆障碍物检测的应用场景的示意图；
图4为本发明第二实施例车辆障碍物检测方法的流程图；
图5为本发明实施例MIMO雷达波达方向估计装置的组成结构示意图。
具体实施方式
面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述。
第一实施例
本发明第一实施例提供了一种MIMO雷达波达方向估计方法，该MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵，M大于1；也就是说，对于该MIMO雷达而言，M元均匀线阵的各个阵元不仅用于发射信号，还用于接收对应的回波信号；可以理解的是，M元 均匀线阵相邻阵元之间的间距相等，将M元均匀线阵的阵元间距记为d。
图1为本发明MIMO雷达波达方向估计方法的第一实施例的流程图，如图1所示，该流程包括：
步骤100：利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据。
这里，需要首先利用MIMO雷达天线阵列发射信号，之后，利用MIMO雷达天线阵列接收对应的回波信号。
在具体实现过程中，MIMO雷达天线阵列的每个阵元均发射快拍数为L的信号，MIMO雷达天线阵列的任意两个阵元发射的信号相互正交，MIMO雷达天线阵列的每个阵元发射的信号可以表示为L列的行向量，将MIMO雷达天线阵列第m个阵元发射的信号记为sm(t)，m取1至M，则在障碍物处接收到的信号y表示为：
y＝aT(θ)S(t)+N0
其中，y是大小为1×L的实矩阵，L表示发射信号的快拍数，上标T表示矩阵或向量的转置；a(θ)为方位角θ对应的接收信号方向向量，
S(t)＝[s1(t)，s2(t)，…，sM(t)]T，S(t)是大小为M×L的矩阵，N0是大小为1×L的噪声矢量；这里，a(θ)的表达式为：
a(θ)＝[1，ej2πdsinθ/λ，…，ej2π(M-1)dsinθ/λ]T
其中，λ表示MIMO雷达天线阵列的发射信号波长，令τ＝2πdsinθ/λ，τ表示由于相对障碍物位置不同而造成相邻发射阵元间的相位差。
在理想情况下，MIMO雷达天线阵列所接收的回波信号X为：
X＝a(θ)aT(θ)S(t)+N1
其中，N1是大小为M×L的噪声矩阵，可以看出X为大小为M×L的实矩阵。
考虑空间存在K个路障的情况，将这K个路障相对于MIMO天线阵列的方位角表示为θ1至θK，其中第k个路障相对于MIMO天线阵列的方 位角表示为θk，k取1至K；此时，MIMO雷达天线阵列所接收的回波信号X为：
Figure PCTCN2016103285-appb-000001
其中，上标T表示矩阵或向量的转置，Xm表示MIMO天线阵列第m个阵元接收到的信号，m取1至M；βk表示第k个路障对应的信号收发过程中的衰减系数；N3是大小为M×L的噪声矩阵。
MIMO天线阵列每个阵元后均连接有匹配滤波器组，用于对相应阵元接收的回波信号进行匹配滤波处理，匹配滤波器组中匹配滤波器的个数可以根据实际情况进行设置，例如，可以将匹配滤波器组中匹配滤波器的个数设置为M。MIMO天线阵列第m个阵元经匹配滤波处理后的输出信号Xm为：
Figure PCTCN2016103285-appb-000002
其中，上标*表示共轭转置，N4是大小为1×L的噪声矢量。
这里，对每个阵元接收的回波信号进行匹配滤波处理后，滤除了与匹配滤波器特性函数相互正交的信号，其余信号得到保留。
在实际应用中，将MIMO天线阵列各个阵元经匹配滤波处理后的输出信号进行向量化并依次堆砌可得所述回波信号进行匹配滤波处理后所得出的接收数据Y：
Figure PCTCN2016103285-appb-000003
其中，Y表示对所述回波信号进行匹配滤波处理后所得出的接收数据，
Figure PCTCN2016103285-appb-000004
即Y是大小为M2×L的复矩阵，L表示发射信号的快拍数；ar(θk)表示方位角θk对应的接收方向向量，at(θk)表示方位角θk对应的发射方向向量，k取1至K；
Figure PCTCN2016103285-appb-000005
表示矩阵的Kronecker积；AR为接收阵列流型矩阵，AT为发射阵列流型矩阵，
Figure PCTCN2016103285-appb-000006
表示矩阵的Khatri-Rao积，在本发明实施例中，接收阵列和发射阵列相同，均为MIMO雷达的M元均匀线阵；
Figure PCTCN2016103285-appb-000007
A表示等效的阵列流型矩阵，
Figure PCTCN2016103285-appb-000008
即A是大小为M2×K的复矩阵；N5表示噪声矩阵，N5是大小为M2×L的矩阵；H表示衰减系数矩阵，H是大小为K×L的复矩阵，H的展开式可以表示为：
Figure PCTCN2016103285-appb-000009
这里，当k取1至K且l取1至L时，βkl表示矩阵H中第k行第l列的元素。
根据方向向量的特殊形式和Kronecker积的性质，令k取1至K，则有：
Figure PCTCN2016103285-appb-000010
其中，
Figure PCTCN2016103285-appb-000011
上标T表示矩阵或向量的转置，G是大小为M2×(2M-1)的实矩阵，G的展开式为：
Figure PCTCN2016103285-appb-000012
这里，G是仅与MIMO雷达天线阵列阵元个数M相关的矩阵，矩阵G中第aM+b行第a+b行的元素为1，其余元素为0，这里，a取0至M-1，b取1至M。
定义矩阵W为GHG，即
Figure PCTCN2016103285-appb-000013
则W的展开式可表示为：
Figure PCTCN2016103285-appb-000014
其中，
Figure PCTCN2016103285-appb-000015
表示定义，diag(·)表示以括号中元素为主对角线元素而构成的矩阵。
步骤101：构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据。
本步骤具体包括：利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
这里，在得出第一降维数据时，将所述接收数据以矩阵形式表示，将预设的降维矩阵与以矩阵形式表示的接收数据相乘，得出第一降维数据；显然，第一降维数据同样以矩阵形式表示。
示例性地，预设的降维矩阵为：W-1/2GH，第一降维数据可以表示为：
Z＝W-1/2GHY
＝W-1/2GH{G[b(θ1)，b(θ2)，…，b(θK)]H+N5}
＝W1/2BH+W-1/2GHN5
＝W1/2BH+Ny
式中，Z表示第一降维数据，Z是大小为(2M-1)×L的复矩阵，W-1/2表示矩阵W的-1/2次方，上标H表示矩阵的共轭转置，Y表示对所述回波信号进行匹配滤波处理后所得出的接收数据，B＝[b(θ1)，b(θ2)，…，b(θK)]， Ny＝W-1/2GHN5，Ny为噪声矩阵。
本步骤中，所述利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据，包括：将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
这里，构造的仅与MIMO雷达天线阵列阵元个数有关的可逆矩阵为W-1/2，由于
Figure PCTCN2016103285-appb-000016
显然，W1/2为满秩对角矩阵，即矩阵W1/2可逆。
示例性地，降维处理后数据可以表示为：
Z简＝W-1/2Z＝BH+W-1/2Ny＝BH+N简
其中，Z简表示降维处理后数据，Z简是大小为(2M-1)×L的复矩阵，N简＝W-1/2Ny，N简为噪声矩阵。
由于矩阵W-1/2仅与MIMO雷达天线阵列阵元个数M有关，所以得出降维处理后数据的过程不会影响MIMO雷达天线阵列接收数据的方位信息，如此，可以将主动信号接收模型转变为被动信号接收模型。同时，在不改变MIMO雷达天线阵列接收数据方位信息的前提下，接收数据矩阵的大小从M2×K降为(2M-1)×K，这样，基于降维处理后数据估计波达方向时，可以有效地降低运算复杂度。
步骤102：基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
本步骤中，基于降维处理后数据构造所述接收数据的稀疏表示模型包括：将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值(SVD)分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
在实际应用中，考虑K已知，可以对矩阵Z简进行奇异值分解，则Z简可以写成如下形式：
Z简＝ULVH＝[USV UNV]LVH
式中，矩阵U是大小为(2M-1)×(2M-1)的特征矢量矩阵，矩阵L是大小为(2M-1)×L的对角矩阵，矩阵V是大小为L×L的正交矩阵；矩阵L中的奇异值是沿着主对角线从大到小排列的，特征矢量矩阵U的奇异值是按照从大到小的顺序排列的，U＝[USV UNV]，其中，USV是大小为(2M-1)×K的信号子矩阵，UNV是噪声子矩阵；对式Z简＝ULVH右乘VDK则有Z简VDK＝ULVHVDK＝ULDK，这里，DK是大小为L×K的矩阵，DK＝[IK O]H,IK是K×K维单位矩阵，O为零矩阵。
令ZSV＝ULDK＝Z简VDK，HSV＝HVDK，NSV＝N简VDK，则有
ZSV＝BHSV+NSV
这里，矩阵B的每一列与一个波达方向相对应。
获取N个角度值，N大于K；例如，对于一个空间域，通过对其进行角度划分，得出N个角度值θ1，θ2，…，θN，例如：N取181时，从0度开始，以1度为步长逐步增加，直到180度。
基于获取的N个角度值构造冗余字典Bs：
Bs＝[b(θ1)，b(θ2)，…，b(θN)]
可以看出，冗余字典Bs是大小为(2M-1)×N的矩阵。
在实际应用中，所构造的接收数据的稀疏表示模型为：
ZSV＝Bs×Hs
则矩阵Hs中必有N-K行均为零；
这里，如果Hs是N×1维的向量，则将所构造的接收数据的稀疏表示模型称为单快拍(Single Measurement Vectors，SMV)模型；反之，如果Hs不是N×1维的向量，则将所构造的接收数据的稀疏表示模型称为多快拍(Multiple Measurement Vectors，MMV)模型。
本步骤中，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计包括：基于压缩感知理论的重构算法对所构造的稀疏表示模型进行求 解，得出MIMO雷达波达方向的估计值。
在实际应用中，在基于压缩感知理论的重构算法对所构造的稀疏表示模型进行求解时，可以得出MIMO雷达波达方向估计的谱函数P：
P＝||Hs||2，1
其中，
Figure PCTCN2016103285-appb-000017
这里，Hs[i，：]表示Hs的第i行，||Hs[i，：]||2的表示Hs[i，：]的最小2范数。
在得出MIMO雷达波达方向估计的谱函数P之后，根据谱函数P谱峰对应角度，即可得出MIMO雷达波达方向的估计值。
应用本发明第一实施例的MIMO雷达波达方向估计方法，可以对接收数据进行多次降维处理，基于降维处理后的数据进行波达方向估计，如此，降低了进行MIMO雷达波达方向估计时的运算复杂度。
第二实施例
这里，本发明实施例可以用于车辆障碍物检测、基站对终端的探测、雷达/声呐探测等领域；下面以车辆障碍物检测领域为例进行说明。
行驶在公路上的车辆需要对周围环境有很好的感知能力，包括对道路结构的感知、对其他动态障碍物的检测等；可靠的环境感知能力对自主巡航控制、碰撞预警和路径规划起到至关重要的作用。
现有车辆障碍物检测技术，包括地图差分法、实体聚类法和目标跟踪法，地图差分法根据地图或者栅格地图上障碍物在不同时刻的分布特点求解障碍物运动信息，计算数据量大，实时性不高鲁棒性较弱，并且往往需要联网，对没有网络覆盖和无导航信号覆盖的偏远山区的安全驾驶造成威胁；实体聚类法和目标跟踪方法，需要存储和计算的信息也很庞大，环境噪声严重时可靠性太差。
此外，还可以通过车载雷达系统对路障进行探测，然而，车载雷达系统只能对雷达正前方的路障进行探测，并不能检测出路障在无人驾驶车前方的角度。
针对上述车辆障碍物探测的技术问题，本发明第二实施例提供了一种车辆障碍物检测方法，在车辆上设置有MIMO雷达天线阵列，该MIMO雷达天线阵列与本发明第一实施例的MIMO雷达天线阵列相同，这里不再重复；在车辆上还设置有第1信号收发通道至第M信号收发通道、收发信机和数字信号处理(Digital Signal Processing，DSP)器。
图2为本发明第二实施例中信号收发通道的结构示意图，如图2所示，第m信号收发通道包括第m选频开关、第m双工器、第m功率放大器(Power Amplifier，PA)、第m低噪声放大器(Low Noise Amplifier，LNA)、第m匹配滤波器组，m取1至M。
参照图2，数字信号处理器设置为控制收发信机发射M个两两正交的信号S1,S2…,SM，收发信机将信号Sm发送至第m功率放大器，第m功率放大器设置为对来自收发信机的信号进行放大，并将放大后的信号发送至第m双工器；第m双工器设置为将来自第m功率放大器的信号发送至第m选频开关，第m选频开关设置为将来自第m双工器的信号发送至MIMO雷达天线阵列的第m阵元，第m选频开关设置为选取信号发射频率，并按照选取的信号发射频率控制MIMO雷达天线阵列的第m阵元发射信号。
参照图2，第m选频开关连接MIMO雷达天线阵列的第m阵元，第m选频开关、第m双工器、第m低噪声放大器、第m匹配滤波器组构成第m接收信号通道；在第m接收信号通道中，第m选频开关设置为选取MIMO雷达天线阵列的第m阵元接收信号的频率，第m选频开关通过第m双工器连接第m低噪声放大器，第m低噪声放大器设置为对来自双工器的接收信号进行放大，并将放大后的信号发送至第m匹配滤波器组，第m匹配滤波器组设置为对来自第m低噪声放大器的信号进行匹配滤波，滤除与匹配滤波器特性函数相互正交的信号，将经匹配滤波处理的信号发送至收发信机，收发信机设置为将来自第m匹配滤波器组的信号发送至数字信号处理器，数字信号处理器设置为根据来自收发信机的信号，对障碍物进行探测，并规划行车路线。
在实际应用中，第m双工器设置为实现收发信号的隔离。
图3为本发明第二实施例中车辆障碍物检测的应用场景的示意图，如图3所示，用数字1表示车辆1，用数字2表示车辆2，用数字3表示车辆3；在车辆2进行障碍物检测时，车辆2上设置的MIMO雷达天线阵列发射信号，并根据接收的回波信号进行障碍物检测。
图4为本发明第二实施例车辆障碍物检测方法的流程图，如图4所示，该流程包括：
步骤400：获取车辆上的MIMO雷达天线阵列发射信号时的波束宽度及指向，并获取车辆上的MIMO雷达天线阵列接收信号时的波束宽度及指向；
这里，可以根据车辆自身到路沿的距离和需要探测的距离，用波束形成算法设置车辆上的MIMO雷达天线阵列发射信号时的波束宽度及指向、车辆上的MIMO雷达天线阵列接收信号时的波束宽度及指向。
示例性地，可以车辆上的距离传感器或摄像头获取车辆自身到路沿的距离。
步骤401：MIMO雷达天线阵列发射信号，并接收对应的回波信号。
可以理解的是，在车辆上设置的MIMO雷达天线阵列发射信号后，所发射的信号遇到障碍物时会返回至MIMO雷达天线阵列。
本步骤的具体实现方式已经在步骤100中作出说明，这里不再赘述。
步骤402：基于所接收的回波信号，对MIMO雷达的波达方向进行估计。
这里，可以利用数字信号处理器对MIMO雷达的波达方向进行估计。
本步骤的实现方式已经在本发明第一实施例中作出说明，这里不再赘述。
步骤403：根据MIMO雷达的波达方向的估计值，计算得出障碍物所在方向。
进一步地，还可以基于障碍物所在方向规划行车路线。
在实际应用中，可以利用车辆上设置的数字信号处理器计算得出障碍物所在方向，并规划行车路线。
波达方向估计是广泛应用于无线通信、雷达、导航、声纳、天文学和生物医学工程等诸多领域，是高分辨阵列信号处理的重要研究内容之一，本发明第二实施例采用波达方向估计方法进行车辆障碍物方向检测，从而给车辆提供行车路径规划信息，提高车辆了行车安全性能。
此外，由于车载雷达的天线数量是有限的，采用本发明第二实施例车辆障碍物检测方法，能够利用有限的天线阵元对车辆周围障碍物所在方向做出准确的检测，同时本发明第二实施例所应用的MIMO雷达波达方向估计方法，具有运算复杂度低的特点，有利于满足高速行驶车辆对障碍物检测的实时性需求。
第三实施例
基于本发明第一实施例的MIMO雷达波达方向估计方法，本发明第三实施例提供了一种MIMO雷达波达方向估计装置。
图5为本发明实施例MIMO雷达波达方向估计装置的组成结构示意图，所述MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵，M大于1；所述MIMO雷达天线阵列用于接收回波信号。
如图5所示，该装置包括：滤波处理模块500、降维处理模块501和估计模块502；其中，
滤波处理模块500，设置为对所述回波信号进行匹配滤波处理，得出接收数据；
降维处理模块501，设置为构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；
估计模块502，设置为基于降维处理后数据构造所述接收数据的稀疏 表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
具体地，所述降维处理模块501，设置为利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
所述降维处理模块501，具体设置为将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
所述降维处理模块501，具体设置为定义矩阵W，W＝diag(1，2，…，M，M-1，…，2，1)，diag(·)表示以括号中元素为主对角线元素而构成的对角矩阵；
所述降维处理模块501，还设置为将矩阵W的-1/2次方W-1/2作为所述与MIMO雷达天线阵列阵元个数有关的可逆矩阵。
所述估计模块502，具体设置为将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
在实际应用中，所述滤波处理模块500、降维处理模块501和估计模块502均可由中央处理器(Central Processing Unit，CPU)、微处理器(Micro Processor Unit，MPU)、数字信号处理器(Digital Signal Processor，DSP)、或现场可编程门阵列(Field Programmable Gate Array，FPGA)等实现。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流 程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。
工业实用性
在本发明实施例的MIMO雷达波达方向估计过程中，利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据；构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计；如此，可以对接收数据进行降维处理，基于降维处理后的数据进行波达方向估计，降低了进行MIMO雷达波达方向估计时的运算复杂度。

Claims (11)

  1. 一种多输入多输出MIMO雷达波达方向估计方法，所述MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵；所述方法包括：
    利用MIMO雷达天线阵列接收回波信号，对所述回波信号进行匹配滤波处理，得出接收数据；
    构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；
    基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
  2. 根据权利要求1所述的方法，其中，所述利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据，包括：
    利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
  3. 根据权利要求2所述的方法，其中，所述利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据，包括：将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后数据。
  4. 根据权利要求2所述的方法，其中，所述利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据，包括：将预设的降维矩阵与以矩阵形式表示的接收数据相乘，得出第一降维数据。
  5. 根据权利要求1所述的方法，其中，所述构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，包括：
    定义矩阵W，W＝diag(1，2，…，M，M-1，…，2，1)，diag(·)表示以括号中元素为主对角线元素而构成的对角矩阵；
    将矩阵W的-1/2次方W-1/2作为所述与MIMO雷达天线阵列阵元个数有关的可逆矩阵。
  6. 根据权利要求1至5任一项所述的方法，其中，所述基于降维处理后数据构造所述接收数据的稀疏表示模型包括：
    将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
  7. 一种多输入多输出MIMO雷达波达方向估计装置，所述MIMO雷达的天线阵列为用于发射信号并接收回波信号的M元均匀线阵；所述装置包括：滤波处理模块、降维处理模块和估计模块；其中，
    滤波处理模块，设置为对所述回波信号进行匹配滤波处理，得出接收数据；
    降维处理模块，设置为构造与MIMO雷达天线阵列阵元个数有关的可逆矩阵，利用所述可逆矩阵对所述接收数据进行降维处理，得出降维处理后数据；
    估计模块，设置为基于降维处理后数据构造所述接收数据的稀疏表示模型，基于所构造的稀疏表示模型对MIMO雷达波达方向进行估计。
  8. 根据权利要求7所述的装置，其中，所述降维处理模块，具体设置为利用预设的降维矩阵对接收数据进行第一次降维处理，得出第一降维数据；利用所述可逆矩阵对第一降维数据进行第二次降维处理，得出降维处理后数据。
  9. 根据权利要求8所述的装置，其中，所述降维处理模块，具体设置为将所述可逆矩阵与所述第一降维数据相乘，得出降维处理后 数据。
  10. 根据权利要求7所述的装置，其中，所述降维处理模块，具体设置为定义矩阵W，W＝diag(1，2，…，M，M-1，…，2，1)，diag(·)表示以括号中元素为主对角线元素而构成的对角矩阵；
    所述降维处理模块，还设置为将矩阵W的-1/2次方W-1/2作为所述与MIMO雷达天线阵列阵元个数有关的可逆矩阵。
  11. 根据权利要求7至10任一项所述的装置，其中，所述估计模块，具体设置为将所述降维处理后数据以矩阵形式表示，对以矩阵形式表示的降维处理后数据进行奇异值分解，得出分解后数据；构造冗余字典，基于所述分解后数据和所构造的冗余字典，构造所述接收数据的稀疏表示模型。
PCT/CN2016/103285 2016-03-23 2016-10-25 一种mimo雷达波达方向估计方法和装置 WO2017161874A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610172209.5 2016-03-23
CN201610172209.5A CN107229041A (zh) 2016-03-23 2016-03-23 一种mimo雷达波达方向估计方法和装置

Publications (1)

Publication Number Publication Date
WO2017161874A1 true WO2017161874A1 (zh) 2017-09-28

Family

ID=59899309

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/103285 WO2017161874A1 (zh) 2016-03-23 2016-10-25 一种mimo雷达波达方向估计方法和装置

Country Status (2)

Country Link
CN (1) CN107229041A (zh)
WO (1) WO2017161874A1 (zh)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108896974A (zh) * 2018-05-15 2018-11-27 西北工业大学 一种改进的mimo阵列高分辨空间谱估计方法
CN109901103A (zh) * 2019-03-14 2019-06-18 长江大学 基于非正交波形的mimo雷达doa估算方法及设备
CN110426701A (zh) * 2019-09-03 2019-11-08 河海大学 一种基于速度维稀疏表示的捷变频雷达目标速度测量方法
CN111142063A (zh) * 2020-01-06 2020-05-12 西安邮电大学 一种基于降维优化的快速压缩感知低空目标测角方法
CN111487599A (zh) * 2020-04-30 2020-08-04 长江大学 增益-相位误差背景下双基地mimo雷达角度估计方法及装置
CN111983552A (zh) * 2020-07-28 2020-11-24 南京航空航天大学 一种基于差分共阵的嵌套阵列快速doa估计方法与装置
CN112285641A (zh) * 2020-09-16 2021-01-29 西安空间无线电技术研究所 一种基于ica的波达方向doa的估计方法及装置
CN112327264A (zh) * 2020-10-14 2021-02-05 海南大学 一种双基地fda-mimo雷达多维参数联合估计方法
CN112485755A (zh) * 2020-11-20 2021-03-12 中国人民解放军空军工程大学 基于2q阶嵌套MIMO阵列结构的角度估计方法
CN112666526A (zh) * 2021-01-28 2021-04-16 深圳成谷科技有限公司 一种雷达目标定位方法、系统、设备及存储介质
CN112698289A (zh) * 2020-12-08 2021-04-23 南京理工大学 一种基于压缩感知的mimo雷达目标信息恢复方法
CN112904296A (zh) * 2021-01-18 2021-06-04 西安电子科技大学 基于实值处理cs-parafac算法的双基地mimo雷达角度估计方法
CN112965059A (zh) * 2021-02-06 2021-06-15 西安邮电大学 实值求根多重信号分类方法、系统、计算机设备及应用
CN113051739A (zh) * 2021-03-17 2021-06-29 电子科技大学长三角研究院(衢州) 一种基于稀疏约束的鲁棒性自适应处理方法
CN113050026A (zh) * 2021-03-29 2021-06-29 西安电子科技大学 拓展阵元间距移位子阵的结构性运动稀疏阵列设计方法
CN113673317A (zh) * 2021-07-12 2021-11-19 电子科技大学 基于原子范数最小化可降维的二维离格doa估计方法
CN115327473A (zh) * 2022-10-17 2022-11-11 广东大湾区空天信息研究院 等效多快拍的4d毫米波雷达测角方法、装置及相关设备

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020097903A1 (zh) * 2018-11-16 2020-05-22 华为技术有限公司 测角方法以及雷达设备
CN110174658B (zh) * 2019-06-14 2023-03-28 西安电子科技大学 基于秩一降维模型和矩阵补全的波达方向估计方法
CN110873866A (zh) * 2019-12-03 2020-03-10 海南大学 一种互耦条件下单基地mimo雷达目标角度估计方法
CN111044996A (zh) * 2019-12-20 2020-04-21 南京航空航天大学 一种基于降维近似消息传递的lfmcw雷达目标检测方法
US20240219553A1 (en) * 2022-12-30 2024-07-04 Chiun Mai Communication Systems, Inc. Multiple-input multiple-output radar device and method for scanning at least one moving object and estimating probability of the object in 3d surrounding space

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103983958A (zh) * 2014-05-16 2014-08-13 哈尔滨工程大学 基于多测量矢量稀疏表示的mimo雷达连续目标角度估计方法
CN104991236A (zh) * 2015-06-18 2015-10-21 哈尔滨工程大学 一种单基地mimo雷达非圆信号相干源波达方向估计方法
US20150323650A1 (en) * 2013-03-11 2015-11-12 Src, Inc. Mimo angle estimation with simultaneous mainlobe jammer cancellation
CN105093185A (zh) * 2015-08-23 2015-11-25 哈尔滨工程大学 基于稀疏表示的单基地多输入多输出雷达目标波达方向估计方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101534177B (zh) * 2009-04-03 2012-05-23 西安交通大学 Mimo系统中基于rumswf的低复杂度降秩均衡方法
CN101799535A (zh) * 2009-11-27 2010-08-11 西安电子科技大学 Mimo雷达目标方向的估计方法
CN102279387B (zh) * 2011-07-18 2013-02-27 西安电子科技大学 Mimo雷达的目标到达角估计方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150323650A1 (en) * 2013-03-11 2015-11-12 Src, Inc. Mimo angle estimation with simultaneous mainlobe jammer cancellation
CN103983958A (zh) * 2014-05-16 2014-08-13 哈尔滨工程大学 基于多测量矢量稀疏表示的mimo雷达连续目标角度估计方法
CN104991236A (zh) * 2015-06-18 2015-10-21 哈尔滨工程大学 一种单基地mimo雷达非圆信号相干源波达方向估计方法
CN105093185A (zh) * 2015-08-23 2015-11-25 哈尔滨工程大学 基于稀疏表示的单基地多输入多输出雷达目标波达方向估计方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DONG, TIANBAO ET AL.: "DOA Estimation Based on Sparse Bayesian Learning", FIRE CONTROL & COMMAND CONTROL, vol. 40, no. 3, 31 March 2015 (2015-03-31) *
LIANG, HAO ET AL.: "Reduced-Dimensional DOA Estimation Based on Sparse Reconstruction in MIMO Radar with L-Shaped Array", SYSTEMS ENGINEERING AND ELECTRONICS, vol. 37, no. 12, 31 December 2015 (2015-12-31), pages 2727 *

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108896974B (zh) * 2018-05-15 2022-03-08 西北工业大学 一种改进的mimo阵列高分辨空间谱估计方法
CN108896974A (zh) * 2018-05-15 2018-11-27 西北工业大学 一种改进的mimo阵列高分辨空间谱估计方法
CN109901103A (zh) * 2019-03-14 2019-06-18 长江大学 基于非正交波形的mimo雷达doa估算方法及设备
CN109901103B (zh) * 2019-03-14 2023-08-11 长江大学 基于非正交波形的mimo雷达doa估算方法及设备
CN110426701A (zh) * 2019-09-03 2019-11-08 河海大学 一种基于速度维稀疏表示的捷变频雷达目标速度测量方法
CN111142063A (zh) * 2020-01-06 2020-05-12 西安邮电大学 一种基于降维优化的快速压缩感知低空目标测角方法
CN111487599A (zh) * 2020-04-30 2020-08-04 长江大学 增益-相位误差背景下双基地mimo雷达角度估计方法及装置
CN111487599B (zh) * 2020-04-30 2023-07-14 长江大学 增益-相位误差背景下双基地mimo雷达角度估计方法及装置
CN111983552A (zh) * 2020-07-28 2020-11-24 南京航空航天大学 一种基于差分共阵的嵌套阵列快速doa估计方法与装置
CN111983552B (zh) * 2020-07-28 2023-11-07 南京航空航天大学 一种基于差分共阵的嵌套阵列快速doa估计方法与装置
CN112285641A (zh) * 2020-09-16 2021-01-29 西安空间无线电技术研究所 一种基于ica的波达方向doa的估计方法及装置
CN112285641B (zh) * 2020-09-16 2023-12-29 西安空间无线电技术研究所 一种基于ica的波达方向doa的估计方法及装置
CN112327264A (zh) * 2020-10-14 2021-02-05 海南大学 一种双基地fda-mimo雷达多维参数联合估计方法
CN112327264B (zh) * 2020-10-14 2023-10-13 海南大学 一种双基地fda-mimo雷达多维参数联合估计方法
CN112485755B (zh) * 2020-11-20 2023-05-12 中国人民解放军空军工程大学 基于2q阶嵌套MIMO阵列结构的角度估计方法
CN112485755A (zh) * 2020-11-20 2021-03-12 中国人民解放军空军工程大学 基于2q阶嵌套MIMO阵列结构的角度估计方法
CN112698289B (zh) * 2020-12-08 2024-05-17 南京理工大学 一种基于压缩感知的mimo雷达目标信息恢复方法
CN112698289A (zh) * 2020-12-08 2021-04-23 南京理工大学 一种基于压缩感知的mimo雷达目标信息恢复方法
CN112904296B (zh) * 2021-01-18 2023-12-26 西安电子科技大学 基于实值处理cs-parafac算法的双基地mimo雷达角度估计方法
CN112904296A (zh) * 2021-01-18 2021-06-04 西安电子科技大学 基于实值处理cs-parafac算法的双基地mimo雷达角度估计方法
CN112666526A (zh) * 2021-01-28 2021-04-16 深圳成谷科技有限公司 一种雷达目标定位方法、系统、设备及存储介质
CN112666526B (zh) * 2021-01-28 2024-02-13 深圳成谷科技有限公司 一种雷达目标定位方法、系统、设备及存储介质
CN112965059A (zh) * 2021-02-06 2021-06-15 西安邮电大学 实值求根多重信号分类方法、系统、计算机设备及应用
CN113051739B (zh) * 2021-03-17 2023-08-18 电子科技大学长三角研究院(衢州) 一种基于稀疏约束的鲁棒性自适应处理方法
CN113051739A (zh) * 2021-03-17 2021-06-29 电子科技大学长三角研究院(衢州) 一种基于稀疏约束的鲁棒性自适应处理方法
CN113050026A (zh) * 2021-03-29 2021-06-29 西安电子科技大学 拓展阵元间距移位子阵的结构性运动稀疏阵列设计方法
CN113673317B (zh) * 2021-07-12 2023-04-07 电子科技大学 基于原子范数最小化可降维的二维离格doa估计方法
CN113673317A (zh) * 2021-07-12 2021-11-19 电子科技大学 基于原子范数最小化可降维的二维离格doa估计方法
CN115327473B (zh) * 2022-10-17 2022-12-16 广东大湾区空天信息研究院 等效多快拍的4d毫米波雷达测角方法、装置及相关设备
CN115327473A (zh) * 2022-10-17 2022-11-11 广东大湾区空天信息研究院 等效多快拍的4d毫米波雷达测角方法、装置及相关设备

Also Published As

Publication number Publication date
CN107229041A (zh) 2017-10-03

Similar Documents

Publication Publication Date Title
WO2017161874A1 (zh) 一种mimo雷达波达方向估计方法和装置
Shao et al. Target sensing with intelligent reflecting surface: Architecture and performance
CN104991236B (zh) 一种单基地mimo雷达非圆信号相干源波达方向估计方法
Zhang et al. A method for finding best channels in beam-space post-Doppler reduced-dimension STAP
CN108562866B (zh) 基于矩阵填充的双基地mimo雷达角度估算方法
Yan et al. Two-stage ESPRIT for unambiguous angle and range estimation in FDA-MIMO radar
CN107064926B (zh) 空域色噪声背景下的双基地mimo雷达角度估算方法
CN104698433A (zh) 基于单快拍数据的相干信号doa估计方法
CN103605122A (zh) 相干mimo雷达的收发稳健降维自适应波束形成方法
CN104035069B (zh) 基于部分矫正对称均匀线阵的窄带近场信号源定位方法
CN103245956A (zh) 一种基于稳健波束形成算法的gps抗多径方法
CN102707258A (zh) 基于l型传感器阵列的信号方位角和仰角联合估计方法
Bosse et al. A spatio-temporal array processing for passive localization of radio transmitters
CN112379327A (zh) 一种基于秩损估计的二维doa估计与互耦校正方法
CN104730513A (zh) 一种分级子阵聚焦mvdr波束形成方法
Ponnusamy et al. Computationally efficient method for joint DOD and DOA estimation of coherent targets in MIMO radar
CN109143228A (zh) 互耦条件下双基地mimo雷达非圆目标的角度估计方法
Park et al. Maximum‐likelihood angle estimator for multi‐channel FM‐radio‐based passive coherent location
CN106324556A (zh) 一种稀疏重构辅助的非均匀阵列波达方向估计方法
CN114415137A (zh) 一种基于矩阵束原理的频率分集mimo雷达参数估计方法
Ikram et al. Joint antenna-array calibration and direction of arrival estimation for automotive radars
Li et al. DOD and DOA estimation for MIMO radar based on combined MUSIC and sparse Bayesian learning
Li Joint parameter estimation and target localization for bistatic MIMO radar system in impulsive noise
US8299955B2 (en) Method for cleaning signals for centralized antijamming
Pradhan et al. Direction of arrival estimation via ESPRIT algorithm for smart antenna system

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16895220

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 16895220

Country of ref document: EP

Kind code of ref document: A1