CN109471086B - Estimation method for direction of arrival of co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of collective array signal - Google Patents

Estimation method for direction of arrival of co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of collective array signal Download PDF

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CN109471086B
CN109471086B CN201811218389.1A CN201811218389A CN109471086B CN 109471086 B CN109471086 B CN 109471086B CN 201811218389 A CN201811218389 A CN 201811218389A CN 109471086 B CN109471086 B CN 109471086B
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张宗煜
史治国
周成伟
陈积明
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Zhejiang University ZJU
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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
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Abstract

The invention discloses a method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of a collective array signal, which mainly solves the problem of higher calculation complexity of the existing method. The method comprises the following implementation steps: constructing a co-prime MIMO radar structure; receiving the reflected signals through a radar receiving subarray and modeling the radar output signals; constructing a co-prime MIMO radar multi-sampling snapshot and a collective array receiving signal; zero filling is carried out on the multi-sampling snapshot and the collective array received signals; carrying out side lobe suppression on the multi-sampling snapshot and the gather array received signal after zero padding; carrying out discrete Fourier transform operation on the filtered multi-sampling snapshot and the collected array received signals to construct a spatial spectrum; and estimating the direction of arrival according to the obtained spatial spectrum. The invention obtains higher array aperture under the condition of certain physical array element number, and reduces the computational complexity of the estimation of the direction of arrival.

Description

Estimation method for direction of arrival of co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of collective array signal
Technical Field
The invention belongs to the technical field of signal processing, particularly relates to statistical signal processing of radar signals, acoustic signals and electromagnetic signals, and particularly relates to a method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of array signals, which can be used for active positioning and target detection.
Background
As one of the basic problems in the field of array signal processing, Direction-of-Arrival (DOA) estimation, which estimates a signal received by a sensor array, statistically processes the received signal, and extracts Direction-of-Arrival information included in the signal therefrom, has been widely used in the fields of radar, sonar, voice, wireless communication, and the like. Among them, using Multiple-input Multiple-output (MIMO) radar to estimate the direction of arrival is an important branch.
MIMO radars use multiple sensor arrays to transmit orthogonal signals and receive reflected signals, respectively. In a conventional MIMO radar structure, the receiving array is generally set as a uniform linear array, thereby satisfying the nyquist sampling theorem. However, the arrangement of the uniform linear array limits the aperture of the array, and further limits the performance such as spatial resolution. Under the background, as the application of the co-prime array structure on the MIMO radar, the co-prime MIMO radar fully utilizes the systematized structure of the co-prime array, and improves the DOA estimation performance by constructing the virtual array with larger aperture.
Most of existing DOA estimation methods based on the co-prime MIMO radar perform a series of complex operations on second-order equivalent virtual signals corresponding to a constructed virtual array, mainly including complex matrix operations such as inversion and eigenvalue decomposition, and complex processes such as design and solution of a convex optimization problem, so that the hardware implementation in an actual system is complex, and certain challenges are faced in an application scene with high real-time requirements.
Disclosure of Invention
The invention aims to provide a method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of a collective array signal, aiming at the defects in the prior art, the method can obtain a larger aperture than the traditional MIMO radar structure while obtaining a correct direction of arrival estimation result by performing discrete Fourier transform on the basis of multi-sampling snapshot and collective array signal receiving of the co-prime MIMO radar, and has the characteristics of low calculation complexity, strong real-time performance and easy design and implementation in a practical system.
The purpose of the invention is realized by the following technical scheme: a method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of a collective array signal comprises the following steps:
(1) a mutual-prime MIMO radar structure is constructed by utilizing M + N actual array elements, and the construction method comprises the following steps: selecting a group of relatively prime integers M, N to be respectively used for constructing a transmitting subarray and a receiving subarray, wherein the transmitting subarray comprises M array elements with the distance Nd, and the positions of the array elements are 0, Nd, ·, (M-1) Nd; the receiving subarray comprises N array elements with the interval Md, and the positions of the N array elements are 0, Md, (N-1) Md; d is the half wavelength of the transmitting signal of the transmitting sub arrayNamely, it is
Figure BDA0001833854080000021
The transmitting subarray and the receiving subarray are deployed in the form of a monostatic radar;
(2) and (3) transmitting M orthogonal signals by using the transmitting sub-array constructed in the step (1). Suppose there are Q targets in the far field in space, whose direction with respect to the radar is denoted as θ ═ θ1，θ2，…，θQ]T，[·]T denotes a transpose operation. After the M signals are reflected by Q targets in the space, the M signals are received by N array elements of a receiving subarray, and after matching and filtering, a radar output signal x (t) at time t can be modeled as:
Figure BDA0001833854080000022
wherein the content of the first and second substances,
Figure BDA0001833854080000023
denotes the kronecker product, at(θq) And ar(θq) The steering vectors corresponding to the qth target, denoted as transmit subarray and receive subarray, respectively
Figure BDA0001833854080000024
Figure BDA0001833854080000025
Wherein s isq(t) is the reflectivity of the qth target at time t, n (t) is signal independent additive noise, obeying zero mean complex gaussian distribution, and j is an imaginary unit;
(3) under the structure of the co-prime MIMO radar, the output signal x (t) in the step (2) can be equivalently regarded as a single sampling snapshot and a collective array (sum array) generated by a transmitting subarray and a receiving subarray
Figure BDA0001833854080000026
Of the received signal, wherein
Figure BDA0001833854080000031
Therefore, the received signal in step (2) can be expressed as
Figure BDA0001833854080000032
Wherein the content of the first and second substances,
Figure BDA0001833854080000033
representing steering vectors corresponding to the qth target by the set-sum array; here, the
Figure BDA0001833854080000034
The position of each array element in the collective array is shown, wherein u1＝0；
On the basis, the multi-sampling snapshot and the collective array receive signals
Figure BDA0001833854080000035
Can be represented by the first order statistic of the continuous T single sampling snapshots received signal x (T);
(4) zero filling is carried out on the multi-sampling snapshot and the collective array received signals; in the vector
Figure BDA0001833854080000036
A certain number of zero elements are filled in, so that the vector z after filling satisfies the following condition:
Figure BDA0001833854080000037
wherein the content of the first and second substances,<·>ldrepresenting elements corresponding to the array elements in the ld position, [ ·]lRepresents the L-th element in the vector, L-0, 1., L-1, L-2 MN-M-N + 1;
(5) constructing a filter function w to carry out sidelobe suppression on the multi-sampling snapshot and the gather array receiving signal after zero filling, wherein the output of the filter is
Figure BDA0001833854080000038
Wherein
Figure BDA0001833854080000039
Represents a hadamard product, w ═ w (0), w (1), w (L-1)]TRepresents a vector consisting of the cather window function,
Figure BDA00018338540800000310
wherein I0Is a zero order Bessel function, beta is a window function shape coefficient;
(6) carrying out discrete Fourier transform operation on the filtered multi-sampling snapshot and the collected array received signals to construct a spatial spectrum; the multi-sampling snapshot and the collective array received signals after the filtering processing obtained in the step (5) are processed
Figure BDA00018338540800000311
Performing discrete Fourier transform to obtain L multiplied by 1 dimensional spatial response y;
constructing a spatial spectrum whose horizontal axis represents the angle θ, which can be expressed in relation to the kth element of the spatial response y as:
Figure BDA0001833854080000041
wherein K is 0, 1, K-1, arcsin (·) is an arcsine function, and r is a coefficient, which ensures that
Figure BDA0001833854080000042
Satisfies the domain of the arcsine function when
Figure BDA0001833854080000043
When in use
Figure BDA0001833854080000044
The vertical axis of the spatial spectrum represents the modulo [ y ] of each element in the spatial response vector]k| represents the modulus of the complex number;
(7) and estimating the direction of arrival according to the obtained spatial spectrum: and (4) performing spectrum peak searching operation on the spatial spectrum in the step (6), wherein the first Q peak values with the maximum amplitude correspond to the direction of arrival estimation of the Q targets in the space.
Further, in the step (3), the first-order statistic may be an average value or an addition form of T consecutive single-sample snapshot received signals x (T).
Further, in the step (5), the spatial response y obtained by the discrete fourier transform may be represented as:
Figure BDA0001833854080000045
wherein the content of the first and second substances,
Figure BDA0001833854080000046
representing a discrete Fourier transform operation, FKA K-point discrete fourier transform matrix, where K ═ L, can be expressed as:
Figure BDA0001833854080000047
further, in the step (5), the constructed spatial spectrum reflects the response amplitude at each angle in space, wherein there are Q peaks corresponding to Q far-field targets.
Compared with the prior art, the invention has the following advantages:
(1) the method provided by the invention utilizes a co-prime MIMO radar structure, obtains an array aperture larger than that of the traditional MIMO radar under the condition of a certain array element number, and obtains higher resolution performance on the basis of ensuring the accuracy of estimation of the direction of arrival.
(2) The method provided by the invention carries out discrete Fourier transform operation on the basis of the multi-sampling snapshot and the collective array receiving signal of the co-prime MIMO radar, constructs a space spectrum through the obtained response, and obtains the estimation result of the direction of arrival through a spectrum peak searching mode, thereby avoiding the complex calculation processes of matrix inversion, eigenvalue decomposition, convex optimization problem design solution and the like commonly used in the prior method, having the characteristics of low calculation complexity and easy realization in a practical system, and having remarkable advantages in an application scene with higher real-time requirement.
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FIG. 1 is a block diagram of the overall flow of the method of the present invention;
FIG. 2 is a schematic diagram of a structure of a co-prime MIMO radar according to the present invention;
FIG. 3 is a schematic diagram of a spatial spectrum for embodying the direction of arrival estimation performance of the proposed method;
Detailed Description
The technical means and effects of the present invention will be described in further detail below with reference to the accompanying drawings.
As an application of a co-prime array to the MIMO radar, the co-prime MIMO radar structure fully absorbs and embodies the structural advantages of the systematization of the co-prime array, and has recently received wide attention from academia. At present, the majority of DOA estimation methods based on the DOA are high in calculation complexity, and face challenges in scenes with high real-time requirements, and face certain difficulties in actual engineering implementation. In order to solve the above problems, the present invention provides a method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete fourier transform of a collective array signal, and referring to fig. 1, the implementation steps of the present invention are as follows:
the method comprises the following steps: a mutual-prime MIMO radar structure is constructed by utilizing M + N actual array elements, and the construction method comprises the following steps: a set of relatively prime integers M, N is selected for constructing the transmit and receive sub-arrays, respectively. The transmitting subarray comprises M array elements with the distance Nd, and the positions of the array elements are 0, Nd, (M-1) Nd; the receiving subarray comprises N array elements with the interval Md, and the positions of the N array elements are 0, Md, (N-1) Md; d is the half wavelength of the transmitting signal of the transmitting sub-array
Figure BDA0001833854080000051
The transmit and receive subarrays are deployed in the form of a monostatic radar.
Step two: the reflected signals are received by the radar receive subarrays and the radar output signals are modeled. And transmitting M orthogonal signals by using the transmitting sub-array constructed in the step one. Suppose there are Q targets in the far field in space, whose direction with respect to the radar is denoted as θ ═ θ1，θ2，…，θQ]T，[·]T denotes a transpose operation. After the M signals are reflected by Q targets in the space, the M signals are received by N array elements of a receiving subarray, and after matching and filtering, a radar output signal x (t) at time t can be modeled as:
Figure BDA0001833854080000061
wherein the content of the first and second substances,
Figure BDA0001833854080000062
denotes the kronecker product, at(θq) And ar(θq) The steering vectors corresponding to the qth target, denoted as transmit subarray and receive subarray, respectively
Figure BDA0001833854080000063
Figure BDA0001833854080000064
sq(t) is the reflectivity of the qth target at time t, and n (t) is signal independent additive noise, following a zero-mean complex Gaussian distribution. j is an imaginary unit.
Step three: and constructing a multi-sampling snapshot and a collective array receiving signal of the co-prime MIMO radar. Under the structure of the co-prime MIMO radar, the output signal x (t) in the second step can be equivalently regarded as a single sampling snapshot and a collection generated by a transmitting sub-array and a receiving sub-arrayArray of cells
Figure BDA0001833854080000065
Of the received signal, wherein
Figure BDA0001833854080000066
Therefore, the received signal in step two can be expressed as
Figure BDA0001833854080000067
Wherein the content of the first and second substances,
Figure BDA0001833854080000068
the steering vector corresponding to the qth target is represented by the set of sets. Here, the
Figure BDA0001833854080000069
The position of array elements in the gather array is shown, where u1＝0。
On the basis, the multi-sampling snapshot and the collective array receiving signal can be represented by the average value of continuous T single-sampling snapshot receiving signals x (T), namely
Figure BDA00018338540800000610
Step four: and carrying out zero padding on the multi-sampling snapshot and the collective array receiving signals. In the vector
Figure BDA00018338540800000611
A certain number of zero elements are filled in, so that the vector z after filling satisfies the following condition:
Figure BDA0001833854080000071
wherein the content of the first and second substances,<·>ldrelative to the array element in the ld positionCorresponding elements, [. to]lDenotes the L-th element in the vector, L-0, 1.
Step five: constructing a Kaiser window to perform side lobe suppression on the multi-sampling snapshot and the gather array receiving signal after zero filling, and outputting a filter
Figure BDA0001833854080000072
Wherein
Figure BDA0001833854080000073
Represents a hadamard product, w ═ w (0), w (1), w (L-1)]TRepresents a vector consisting of the cather window function,
Figure BDA0001833854080000074
where I0 is a zero-order bessel function and β is a window function shape coefficient. Compared with other window function forms, the Kaiser window can flexibly adjust the shape of the window function by changing the coefficient beta, thereby realizing good balance of resolution performance and sidelobe suppression performance after filtering, obtaining the optimal comprehensive performance,
step six: and performing discrete Fourier transform operation on the filtered multi-sampling snapshot and the collected array received signals to construct a spatial spectrum. The filled multi-sampling snapshot and the collective array received signals obtained in the step five
Figure BDA0001833854080000075
Performing discrete fourier transform to obtain an L × 1 dimensional spatial response y, expressed as:
Figure BDA0001833854080000076
wherein the content of the first and second substances,
Figure BDA0001833854080000077
representing discrete FourierTransformation operation, FKFor a K-point discrete fourier transform matrix, where K ═ L can be expressed as:
Figure BDA0001833854080000078
constructing a spatial spectrum whose horizontal axis represents the angle θ, and whose relation to the kth element of the spatial response can be expressed as:
Figure BDA0001833854080000079
wherein K is 0, 1, K-1, arcsin (·) is an arcsine function, and r is a coefficient, which ensures that
Figure BDA0001833854080000081
Satisfies the domain of the arcsine function when
Figure BDA0001833854080000082
When in use
Figure BDA0001833854080000083
The vertical axis of the spatial spectrum represents the modulo [ y ] of each element in the spatial response vector]k| represents the modulus of the complex number.
Step seven: and estimating the direction of arrival according to the obtained spatial spectrum. And performing spectral peak searching operation on the spatial spectrum in the step six, and arranging the peak values of the spatial spectrum from high to low, wherein the maximum first Q peak values correspond to the direction of arrival estimation of Q targets in the space.
The method for estimating the direction of arrival is based on a co-prime MIMO radar structure, discrete Fourier transform operation is carried out on the basis of multi-sampling snapshot and array receiving signals of the co-prime MIMO radar, a spatial spectrum is constructed through the obtained response, and a direction of arrival estimation result is obtained through a spectrum peak searching mode. Compared with the traditional MIMO radar structure, the method provided by the invention fully utilizes the structural characteristics of the co-prime MIMO radar, and improves the resolution performance; direction of arrival estimation compared to the prior artThe calculation complexity of the method provided by the invention is only
Figure BDA0001833854080000084
The method has obvious advantages in application scenes with high real-time performance, and is easy to realize on a practical system.
The effect of the present invention will be further described with reference to the simulation example.
Simulation example 1: by using the co-prime MIMO radar structure, the parameters are selected to be M9 and N10, that is, the transmitting subarray comprises 9 array elements, and the receiving subarray comprises 10 array elements. And the fixed sampling fast beat number T is 500. Assuming that there are 1 target from-30 ° in space, the signal-to-noise ratio is fixed at 10 dB. The spatial spectrum obtained under the above conditions using the method of the present invention is shown in FIG. 3. Therefore, the method provided by the invention can accurately obtain the estimation information of the direction of arrival of the target under the above conditions.
In summary, the method provided by the invention is based on a co-prime MIMO radar structure, and the method provided by the invention performs discrete fourier transform operation on the basis of multi-sampling snapshot and collective array received signals of the co-prime MIMO radar, constructs a spatial spectrum through the obtained response, and obtains a direction of arrival estimation result through a spectral peak search mode. The structural characteristics of the co-prime MIMO radar are fully utilized, and the improvement of the resolution performance is realized; and the computational complexity is only
Figure BDA0001833854080000085
The method has obvious advantages in the application scene with high real-time performance, and is easy to realize on the actual system.

Claims (4)

1. A method for estimating the direction of arrival of a co-prime MIMO radar based on multi-sampling snapshot and discrete Fourier transform of an array signal is characterized by comprising the following steps:
(1) a mutual-prime MIMO radar structure is constructed by utilizing M + N actual array elements, and the construction method comprises the following steps: selecting a group of relatively prime integers M, N to be respectively used for constructing a transmitting subarray and a receiving subarray, wherein the transmitting subarray comprises M array elements with the distance Nd, and the positions of the array elements are 0, Nd) Nd; the receiving subarray comprises N array elements with the interval Md, and the positions of the N array elements are 0, Md, (N-1) Md; d is the half wavelength of the transmitting signal of the transmitting sub-array
Figure FDA0001833854070000011
The transmitting subarray and the receiving subarray are deployed in the form of a monostatic radar;
(2) transmitting M orthogonal signals by using the transmitting subarray constructed in the step (1); suppose there are Q targets in the far field in space, whose direction with respect to the radar is denoted as θ ═ θ1，θ2，…，θQ]T，[·]T represents a transpose operation; after the M signals are reflected by Q targets in the space, the M signals are received by N array elements of a receiving subarray, and after matching and filtering, a radar output signal x (t) at time t can be modeled as:
Figure FDA0001833854070000012
wherein the content of the first and second substances,
Figure FDA0001833854070000013
denotes the kronecker product, at(θq) And ar(θq) The steering vectors corresponding to the qth target, denoted as transmit subarray and receive subarray, respectively
Figure FDA0001833854070000014
Figure FDA0001833854070000015
Wherein s isq(t) is the reflectivity of the qth target at time t, n (t) is signal independent additive noise, obeying zero mean complex gaussian distribution, and j is an imaginary unit;
(3) outputting the signal in step (2) under the structure of the co-prime MIMO radarThe number x (t) can be equivalently regarded as a single sample snapshot and a collective array generated by the transmit and receive sub-arrays
Figure FDA0001833854070000017
Of the received signal, wherein
Figure FDA0001833854070000016
Therefore, the received signal in step (2) can be expressed as
Figure FDA0001833854070000021
Wherein the content of the first and second substances,
Figure FDA0001833854070000022
presentation and collection array
Figure FDA0001833854070000023
A steering vector corresponding to the qth target; here, the
Figure FDA0001833854070000024
Indicating the position of each array element in the gather array, where u1＝0；
On the basis, the multi-sampling snapshot and the collective array receive signals
Figure FDA0001833854070000025
Can be represented by the first order statistic of the continuous T single sampling snapshots received signal x (T);
(4) zero filling is carried out on the multi-sampling snapshot and the collective array received signals; in the vector
Figure FDA00018338540700000210
A certain number of zero elements are filled in, so that the vector z after filling satisfies the following condition:
Figure FDA0001833854070000026
wherein the content of the first and second substances,<·>ldrepresenting elements corresponding to the array elements in the ld position, [ ·]lRepresents the L-th element in the vector, L-0, 1., L-1, L-2 MN-M-N + 1;
(5) constructing a filter function w to carry out sidelobe suppression on the multi-sampling snapshot and the gather array receiving signal after zero filling, wherein the output of the filter is
Figure FDA0001833854070000027
Wherein
Figure FDA0001833854070000028
Represents a hadamard product, w ═ w (0), w (1), w (L-1)]TRepresents a vector consisting of the cather window function,
Figure FDA0001833854070000029
wherein I0Is a zero order Bessel function, beta is a window function shape coefficient;
(6) carrying out discrete Fourier transform operation on the filtered multi-sampling snapshot and the collected array received signals to construct a spatial spectrum: the multi-sampling snapshot and the collective array received signals after the filtering processing obtained in the step (5) are processed
Figure FDA00018338540700000211
Performing discrete Fourier transform to obtain L multiplied by 1 dimensional spatial response y;
constructing a spatial spectrum whose horizontal axis represents the angle θ, which can be expressed in relation to the kth element of the spatial response y as:
wherein K is 0, 1, K-1, arcsin (·) is an arcsine function, and r is a coefficient, which ensures that
Figure FDA0001833854070000032
Satisfies the domain of the arcsine function when
Figure FDA0001833854070000033
r is 0; when in use
Figure FDA0001833854070000034
r is 1; the vertical axis of the spatial spectrum represents the modulo [ y ] of each element in the spatial response vector]k| represents the modulus of the complex number;
(7) and estimating the direction of arrival according to the obtained spatial spectrum: and (4) performing spectrum peak searching operation on the spatial spectrum in the step (6), wherein the first Q peak values with the maximum amplitude correspond to the direction of arrival estimation of the Q targets in the space.
2. The method for estimating the direction of arrival of the co-prime MIMO radar based on the multi-sampling snapshot and the discrete Fourier transform of the collective array signal according to claim 1, wherein: in the step (3), the first-order statistic may be an average value or an addition form of T consecutive single-sample snapshot received signals x (T).
3. The method for estimating the direction of arrival of the co-prime MIMO radar based on the multi-sampling snapshot and the discrete Fourier transform of the collective array signal according to claim 1, wherein: in the step (6), the spatial response y obtained by the discrete fourier transform can be expressed as:
Figure FDA0001833854070000035
wherein the content of the first and second substances,
Figure FDA0001833854070000037
representing a discrete Fourier transform operation, FKA K-point discrete fourier transform matrix, where K ═ L, can be expressed as:
Figure FDA0001833854070000036
4. the method for estimating the direction of arrival of the co-prime MIMO radar based on the multi-sampling snapshot and the discrete Fourier transform of the collective array signal according to claim 1, wherein: in step (6), the constructed spatial spectrum reflects the response amplitude at each angle in space, where there are Q peaks corresponding to the Q far-field targets.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107015190A (en) * 2017-03-01 2017-08-04 浙江大学 Relatively prime array Wave arrival direction estimating method based on the sparse reconstruction of virtual array covariance matrix

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104883157B (en) * 2015-05-18 2017-12-01 华侨大学 A kind of variable subband digital filter
CN107102291B (en) * 2017-05-03 2019-07-23 浙江大学 The relatively prime array Wave arrival direction estimating method of mesh freeization based on virtual array interpolation
CN107576953B (en) * 2017-09-12 2020-04-28 成都理工大学 Coherent and incoherent mixed target DOA estimation method based on co-prime MIMO array
CN108614234B (en) * 2018-05-15 2020-09-01 浙江大学 Direction-of-arrival estimation method based on multi-sampling snapshot co-prime array received signal fast Fourier inverse transformation

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107015190A (en) * 2017-03-01 2017-08-04 浙江大学 Relatively prime array Wave arrival direction estimating method based on the sparse reconstruction of virtual array covariance matrix

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation;Chengwei Zhou等;《IEEE TRANSACTIONS ON SIGNAL PROCESSING》;20180926;第66卷(第22期);全文 *

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