CN112327264A - Bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method - Google Patents

Bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method Download PDF

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CN112327264A
CN112327264A CN202011094747.XA CN202011094747A CN112327264A CN 112327264 A CN112327264 A CN 112327264A CN 202011094747 A CN202011094747 A CN 202011094747A CN 112327264 A CN112327264 A CN 112327264A
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CN112327264B (en
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王咸鹏
刘飞龙
黄梦醒
迟阔
国月皓
徐腾贤
王华飞
李亮亮
黄立东
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Hainan University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract

The invention discloses a bistatic FDA-MIMO radar-based multi-dimensional parameter joint estimation method, which is a real-valued ESPRIT method based on a partitioned subarray. The traditional technologies, such as the ESPRIT algorithm and the MUSIC algorithm, have the problems of low precision, high complexity and the like when the bistatic FDA-MIMO radar parameter is estimated. In order to improve the estimation precision and reduce the operation complexity, the invention firstly designs the transmitting array, so that different sub-arrays have different frequency increments; then processing according to the echo signal received by the receiving array to obtain expanded sub-array receiving data, thereby obtaining real-valued expanded data by adopting unitary transformation; decomposing the covariance matrix of the data to obtain a phase matrix of the parameters, and solving DOA estimation according to a least square method (LS); in addition, according to the designed pairing method, a plurality of phase matrixes containing target parameter information are matched. In addition, according to the characteristic of dividing the subarray, the decoupling of DOD and distance information is completed, and therefore the multi-dimensional parameter joint estimation of the bistatic FDA-MIMO radar is achieved. Simulation results show that the method obtains better estimation performance.

Description

Bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method
Technical Field
The invention belongs to the field of radar signal processing, and particularly relates to a bistatic Frequency control array multi-input multi-output (FDA-MIMO) radar multi-dimensional parameter joint estimation method.
Background
In recent years, FDA-MIMO radar has important application potential in the fields of waveform design, parameter estimation, radar imaging and the like as a novel radar system. FDA-MIMO radar has high resolution and excellent through parameter estimation performance, and becomes the focus of signal field research in recent years. FDA-MIMO radars can be classified into monostatic FDA-MIMO (monostatic FDA-MIMO) radars and bistatic FDA-MIMO (bistatic FDA-MIMO) radars according to where their transmit and receive arrays are placed. In recent years, articles aiming at FDA-MIMO parameter estimation focus on the field of single-base FDA-MIMO radar, such as music (multiple signal classification) algorithm, esprit (estimation of signal parameters) algorithm and sparse reconstruction algorithm, but due to the characteristics of poor interference resistance and poor recognition performance of a hidden target of the single-base FDA-MIMO radar, the research on the double-base FDA-MIMO radar with stronger survival capability and stronger interference resistance is very important.
For parameter estimation of bistatic FDA-MIMO radar, the transmit angle (DOD) and distance are naturally coupled in the array manifold, since the transmit array employs FDA techniques. Therefore, for bistatic FDA-MIMO radar, the decoupling method is very important. In the document z.zhao, z.wang and y.sun, "Joint angle range and velocity estimation for bi-static FDA-MIMO radar", 2017IEEE 2 and advanced Information Technology Electronic and estimation Control consistent. (IAEAC), pp.818-824, March 2017, an idea is provided to divide the transmit array into two sub-arrays, transmit two transmissions with opposite frequency increments to achieve decoupling, and then use the ESPRIT algorithm to achieve parameter estimation, which can achieve decoupling of DOD and distance Information, but which has poor accuracy and high computational complexity in the case of small number of fast beats. Furthermore, in the literature, C.cui, J.xu, R.Gui, W.Q.Wang, and W.Wu, "Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar," IEEE Access, vol.6, No. pp.15431-15445,2018, provides a scheme of sub-array partitioning for decoupling, and then, the ESPRIT algorithm is used for realizing the joint Estimation of the target parameters. However, there are also problems of higher complexity and poorer accuracy at low snapshot numbers, and in addition, for multi-target situations, a mismatch problem may occur. In the practical application process, the multi-target condition is more practical, and the parameters of the target are quickly and accurately estimated, so that lower calculation complexity is required. Therefore, the above method is greatly limited in practical application.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a bistatic FDA-MIMO radar multidimensional parameter joint estimation method, the invention is a bistatic FDAMIMO multidimensional parameter joint estimation method based on a real-valued ESPRIT (unified ESPRIT) algorithm, the decoupling of DOD and distance is realized through sub-array division, the expanded received data is constructed, the real-valued transformation is adopted to obtain a phase matrix containing DOA, DOD and distance, and then a novel pairing method is provided to realize the matching of multi-target parameters and realize the parameter joint estimation.
The technical solution for implementing the invention comprises the following steps:
the bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method comprises the following steps:
step 1: m transmitting array elements of the bistatic FDA-MIMO radar are divided into K sub-arrays, each sub-array comprises Q array elements, frequency increment among the sub-arrays is different, and waveforms transmitted by all the array elements are mutually orthogonal. The carrier frequency of the mth array element can be expressed as:
Figure BDA0002723357900000021
wherein Δ f1,Δf2,...,ΔfKIs the frequency increment of each sub-array and satisfies Δ f1≠Δf2≠...≠ΔfK。
Step 2: the bistatic FDA-MIMO radar system receives echo signals of P targets, and the received data after the matched filtering processing can be represented as:
X＝[X1,X2,…,XK]T＝AS+N＝Ar⊙AtS+N
wherein,
Figure BDA0002723357900000022
At＝[at(θ1,r1),at(θ2,r2)…,at(θP,rP)]and indicates a Khatri-Rao product,
Figure BDA0002723357900000023
n represents the number of elements of the receiving array,
Figure BDA0002723357900000024
(·)Trepresenting a matrix transpose, S representing a matrix of the matched filtered echo signals,
Figure BDA0002723357900000031
the expression of (a) is:
Figure BDA0002723357900000032
and step 3: the received data corresponding to each sub-array is expanded to meet the central hermitian symmetry property, and the specific construction transformation can be expressed as:
Yi＝[XiΠQNXiΠL]，i＝1,2,…,K
therein, IIQNAnd piLAn inverse unit matrix of QN × QN and L × L, L being the number of fast beats, is represented.
And 4, step 4: the extended sub-matrix received data is unitary transformed from complex numbers to real numbers:
Figure BDA0002723357900000033
Figure BDA0002723357900000034
Figure BDA0002723357900000035
wherein (·)HDenotes a conjugate transpose, IηAn identity matrix representing the dimension η × η.
And 5: calculating each covariance matrix according to the real-valued extended received data, and obtaining a signal subspace and a noise subspace by using eigen decomposition:
Figure BDA0002723357900000036
wherein,
Figure BDA0002723357900000037
is a diagonal matrix consisting of the first P larger eigenvalues,
Figure BDA0002723357900000038
a signal subspace consisting of the corresponding feature vectors,
Figure BDA0002723357900000039
is a diagonal matrix composed of the remaining QN-P eigenvalues,
Figure BDA00027233579000000310
a noise subspace consisting of its corresponding feature vectors.
Step 6: firstly, constructing a selection matrix, and obtaining a phase matrix containing DOA according to the rotation invariance of real values and a least square method, wherein the phase matrix can be expressed as:
Figure BDA00027233579000000311
Figure BDA00027233579000000312
wherein, J1＝[0(N-1)×1 IN-1]，Ψi＝(Ti)-1ΦiTi,ΦiIs a phase matrix containing target DOA parameters. Similarly, a phase matrix containing the DOD and the distance can be obtained:
Figure BDA0002723357900000041
Figure BDA0002723357900000042
wherein, J2＝[0(Q-1)×1 IQ-1]，Γi＝(Ti)-1ΣiTi，ΣiIs a phase matrix containing the DOD and the distance.
And 7: the method of phase matrix matching within a sub-array can be expressed as:
Ψi+jΓi＝T-1{Φi+jΣi}T，i＝1,2,…,K
the automatic matching of each sub-array to the target estimation can be realized through the above formula, and the matching method of the phase matrix between the sub-arrays can be expressed as follows:
Figure BDA0002723357900000043
wherein w1For the 1 st sub-array phi1Is a vector of diagonal elements, wiFor the ith sub-array phiiThe diagonal elements of (a) constitute a vector.
And 8: according to the obtained phiiThe DOA can be directly solved, and the specific formula is as follows:
Figure BDA0002723357900000044
the equation for decoupling DOD and distance can be expressed as:
Figure BDA0002723357900000045
wherein g is1,g2,…,gKFor phase ambiguity number, for determining ambiguity number, target distance rpIt should satisfy:
Figure BDA0002723357900000046
thus, the above formula can be obtained by the elimination method
Figure BDA0002723357900000051
Wherein, gi+1-giThe determination conditions of (1) are:
Figure BDA0002723357900000052
thus, rpIs estimated to obtain the fuzzy number giCan be determined by:
Figure BDA0002723357900000053
wherein,
Figure BDA0002723357900000054
indicating a rounding down. After the distance estimation and the fuzzy number are determined, the DOD parameters are also determined, so that a multi-parameter joint estimation is obtained.
The invention has the beneficial effects that: the invention provides a bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method, which comprises the steps of firstly dividing transmitting array elements, secondly expanding received data, secondly obtaining sub-array received data of a real value by adopting unitary transformation, determining a phase matrix containing DOA, DOD and distance according to the rotation invariance of the real value, and then realizing the matching of a plurality of target characteristic information by a matching method in the sub-array and among different sub-arrays, thereby realizing the decoupling of the DOD and the distance, and simultaneously directly solving DOA estimation to realize the multi-parameter joint estimation of a target. Compared with the existing method, the method effectively reduces the operation complexity, improves the estimation precision due to the structure of the extension data and unitary transformation, and solves the problem of multi-target multi-parameter mismatching.
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FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a signal model diagram of a bistatic FDA-MIMO radar system;
FIG. 3 is a partition diagram of a bistatic FDA-MIMO radar system transmit array;
FIG. 4 is a plot of the results of 3 parameter estimates of the target versus the number of Monte Carlo experiments;
FIG. 5 is a graph comparing the root mean square error of the target 3 parameters as a function of signal to noise ratio;
FIG. 6 is a comparison of the root mean square error of the target 3 parameters as a function of snapshot count;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method comprises the following steps:
1) m transmitting array elements of the bistatic FDA-MIMO radar are divided into K sub-arrays, each sub-array comprises Q array elements, frequency increment among different sub-arrays is different, and waveforms transmitted by all the array elements are mutually orthogonal. The carrier frequency of the mth array element can be expressed as:
Figure BDA0002723357900000061
wherein Δ f1,Δf2,...,ΔfKIs the frequency increment of each sub-array and satisfies Δ f1≠Δf2≠...≠ΔfK。
2) The bistatic FDA-MIMO radar system receives echo signals of P targets, and the received data after the matched filtering processing can be represented as:
X＝[X1,X2,…,XK]T＝AS+N＝Ar⊙AtS+N
wherein,
Figure BDA0002723357900000062
At＝[at(θ1,r1),at(θ2,r2)…,at(θP,rP)]and indicates a Khatri-Rao product,
Figure BDA0002723357900000063
n represents the number of elements of the receiving array,
Figure BDA0002723357900000064
(·)Trepresenting a matrix transpose, S representing a matrix of the matched filtered echo signals,
Figure BDA0002723357900000065
the expression of (a) is:
Figure BDA0002723357900000066
3) the received data corresponding to each sub-array is expanded to meet the central hermitian symmetry property, and the specific construction transformation can be expressed as:
Yi＝[Xi ΠQNXiΠL]，i＝1,2,…,K
therein, IIQNAnd piLAn inverse unit matrix of QN × QN and L × L, L being the number of fast beats, is represented.
4) The extended sub-matrix received data is unitary transformed from complex numbers to real numbers:
Figure BDA0002723357900000071
Figure BDA0002723357900000072
Figure BDA0002723357900000073
wherein (·)HDenotes a conjugate transpose, IηAn identity matrix representing the dimension η × η.
5) Calculating each covariance matrix according to the real-valued extended received data, and obtaining a signal subspace and a noise subspace by using eigen decomposition:
Figure BDA0002723357900000074
wherein,
Figure BDA0002723357900000075
is a diagonal matrix consisting of the first P larger eigenvalues,
Figure BDA0002723357900000076
a signal subspace consisting of the corresponding feature vectors,
Figure BDA0002723357900000077
is a diagonal matrix composed of the remaining QN-P eigenvalues,
Figure BDA0002723357900000078
a noise subspace consisting of its corresponding feature vectors.
6) Firstly, constructing a selection matrix, and obtaining a phase matrix containing DOA according to the rotation invariance of real values and a least square method, wherein the phase matrix can be expressed as:
Figure BDA0002723357900000079
Figure BDA00027233579000000710
wherein, J1＝[0(N-1)×1 IN-1]，Ψi＝(Ti)-1ΦiTi,ΦiIs a phase matrix containing target DOA parameters. Similarly, a phase matrix containing the DOD and the distance can be obtained:
Figure BDA00027233579000000711
Figure BDA00027233579000000712
wherein, J2＝[0(Q-1)×1 IQ-1]，Γi＝(Ti)-1ΣiTi，ΣiIs a phase matrix containing the DOD and the distance.
7) The method of phase matrix matching within a sub-array can be expressed as:
Ψi+jΓi＝T-1{Φi+jΣi}T，i＝1,2,…,K
the automatic matching of each subarray to the target estimation can be realized through the above formula, and the matching method of the phase matrix among different subarrays can be expressed as follows:
Figure BDA0002723357900000081
wherein w1For the 1 st sub-array phi1Is a vector of diagonal elements, wiFor the ith sub-array phiiThe diagonal elements of (a) constitute a vector.
8) According to the obtained phiiThe DOA can be directly solved, and the specific formula is as follows:
Figure BDA0002723357900000082
the equation for decoupling DOD and distance can be expressed as:
Figure BDA0002723357900000083
wherein g is1,g2,...,gKFor phase ambiguity number, for determining ambiguity number, target distance rpIt should satisfy:
Figure BDA0002723357900000084
therefore, the solution can be obtained by using the elimination method
Figure BDA0002723357900000085
Wherein, gi+1-giThe determination conditions of (1) are:
Figure BDA0002723357900000086
thus, rpIs estimated to obtain the fuzzy number giCan be determined by:
Figure BDA0002723357900000087
wherein,
Figure BDA0002723357900000091
indicating a rounding down. After the distance estimation and the fuzzy number are determined, the DOD parameters are also determined, so that a multi-parameter joint estimation is obtained.
The effect of the present invention will be further described with reference to simulation experiments.
In order to evaluate the performance of the method, a bistatic FDA-MIMO radar system is considered, the transmitting array and the receiving array are both uniform linear arrays, the distance between adjacent array elements is half of the maximum wavelength, the number of array elements of the transmitting array is M-12, and the number of array elements of the receiving array is N-8. The number of the sub-arrays is set to be K-3, each transmitting sub-array has 4 array elements, and the carrier frequency of the 1 st transmitting array element, namely the reference array element, is 30 GHz. The frequency increments of the sub-arrays are respectively set to Δ f1＝5000Hz，Δf2＝10000Hz，Δf315000 Hz. The number of monte carlo experiments was 500. In all experiments, the background noise was uniform complex white gaussian noise. Assuming that there are 2 mutually independent targets in the far field, the parameters of the 1 st target are,
Figure BDA0002723357900000092
θ1＝-20°，r250km, the parameter of the 2nd object is
Figure BDA0002723357900000093
θ2＝30°，r210 km. In some experiments, the ESPRIT algorithm applicable to bistatic FDA-MIMO radar was chosen as the comparison algorithm.
Fig. 4(a) - (c) show the estimation results of DOA, DOD and distance of the target in each monte carlo experiment, respectively, and in the simulation experiment of fig. 4, the SNR is 0dB, the number of snapshots L is 300, and the number of monte carlo experiments is set to 50. As can be seen from the observations of fig. 4(a) - (c), the DOA, DOD and distance of the 2 targets can be accurately estimated and correctly matched in each monte carlo experiment. The results of fig. 4 demonstrate the accuracy of the present invention in estimating multi-objective parameters and solve the problem of parameter mismatch.
Fig. 5(a) - (b) are graphs comparing root mean square error as a function of signal to noise ratio for angles (DOA and DOD) and distances, respectively, for the present invention and comparison algorithms. In the simulation experiment of fig. 5, the snapshot number L is 300, the monte carlo experiment number is 500, and the SNR is increased from 0dB to 20dB, stepping by 5 dB. It can be seen from fig. 5(a) that, under the same signal-to-noise ratio, the DOA root mean square error curve of the present invention is not greatly improved compared with the ESPRIT algorithm, while the DOD root mean square error curve of the present invention is greatly improved compared with the ESPRIT algorithm. As can be seen from the observation of FIG. 5(b), the RMS error curve of the distance parameter of the present invention is much improved compared with the ESPRIT algorithm under the same SNR.
FIGS. 6(a) - (b) are graphs comparing the root mean square error as a function of signal to noise ratio for the angle (DOA and DOD) and distance, respectively, for the present invention and the comparison algorithm. In the simulation experiment of fig. 6, the signal-to-noise ratio SNR is 20dB, the monte carlo experiment number is set to 500, and the snapshot number L is increased from 50 to 500, and stepped to 50. As can be seen from fig. 6(a), as the number of fast beats increases, the root mean square error curve of the DOD of the present invention is approximately consistent with the ESPRIT algorithm, while the root mean square error curve of the DOD of the present invention is greatly improved compared with the ESPRIT algorithm. As can be seen from fig. 5(b), the estimation performance of the distance parameter according to the present invention is better than the ESPRIT algorithm as the number of fast beats increases.
All the above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims (9)

1. A bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method is characterized by comprising the following steps:
step 1: designing a transmitting array of the bistatic FDA-MIMO radar, and dividing M transmitting array elements into K transmitting sub-arrays, wherein frequency increment among the K transmitting sub-arrays is different, and waveforms transmitted by all transmitting true elements are mutually orthogonal;
step 2: carrying out matched filtering processing on the echo signals of the P targets according to the orthogonal characteristic of the transmitting signals to obtain receiving data corresponding to the K transmitting sub-arrays
Figure 197063DEST_PATH_IMAGE001
；
And step 3: to the received data
Figure 909936DEST_PATH_IMAGE001
Spreading to have central Hermite symmetry to produce spread sub-array received data
Figure 272784DEST_PATH_IMAGE002
；
And 4, step 4: receiving data for extended subarrays
Figure 915730DEST_PATH_IMAGE003
Obtaining real-valued sub-matrix received data by using corresponding unitary transformation
Figure 338621DEST_PATH_IMAGE004
；
And 5: according to
Figure 460161DEST_PATH_IMAGE004
To obtain the corresponding covariance matrix
Figure 111853DEST_PATH_IMAGE005
And the characteristic decomposition is adopted to obtain the corresponding signal subspace
Figure 330345DEST_PATH_IMAGE006
；
Step 6: according to the rotation invariant characteristic of the real value, obtaining a phase matrix containing DOA and DOD of the target and distance information;
and 7: matching of a phase matrix containing DOA and a phase matrix containing DOD and distance is completed in the subarrays in a characteristic decomposition mode, and calibration is completed by matching among the subarrays by taking the 1 st subarray as reference;
and 8: and directly solving the DOA parameters, and solving the DOD and the distance parameters according to a decoupling method to obtain the estimation of P parameters.
2. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein in step 1, M transmit array elements are divided into K sub-arrays in a non-overlapping manner, each sub-array comprises Q array elements, a fixed frequency increment exists in each sub-array, and the frequency increments between the sub-arrays are different from each other, thereby effectively decoupling DOD and distance.
3. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein after performing matched filtering processing on the received echo signals in step 2 and partitioning the received data, the received data corresponding to each sub-array is obtained by:
Figure 471608DEST_PATH_IMAGE007
wherein,
Figure 814864DEST_PATH_IMAGE008
representing the transmit-receive joint steering vector,Srepresenting a matrix containing the reflection coefficients of the object after matched filtering,Nrepresenting a complex gaussian noise vector.
4. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein the step 3 expands the received data corresponding to each sub-array, which can be expressed as:
Figure 785094DEST_PATH_IMAGE009
wherein,
Figure 405563DEST_PATH_IMAGE010
and
Figure 170256DEST_PATH_IMAGE011
to represent
Figure 809DEST_PATH_IMAGE012
And
Figure 991374DEST_PATH_IMAGE013
the inverse unit matrix of (a) is,Nin order to receive the number of array elements,Lis a fast beat number.
5. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein the unitary transformation is performed on each of the extended sub-matrix received data in step 4 to obtain real extended sub-matrix received data:
Figure 715617DEST_PATH_IMAGE014
wherein,
Figure 588895DEST_PATH_IMAGE015
and
Figure 719793DEST_PATH_IMAGE016
to represent
Figure 828563DEST_PATH_IMAGE012
And
Figure 282678DEST_PATH_IMAGE017
is used to generate the unitary matrix.
6. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein each covariance matrix is calculated from the real-valued spread received data in step 5, and a signal subspace and a noise subspace are obtained by eigen decomposition:
Figure 139907DEST_PATH_IMAGE018
wherein,
Figure 69686DEST_PATH_IMAGE019
and
Figure 670563DEST_PATH_IMAGE020
representing the signal subspace and the noise subspace respectively,
Figure 244763DEST_PATH_IMAGE021
and
Figure 318899DEST_PATH_IMAGE022
representing the corresponding characteristic value.
7. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein in the step 6, a proper selection matrix is constructed to obtain a real-valued rotation invariance, and then a phase matrix containing DOA, DOD and distance information is obtained according to a least square method:
Figure 421459DEST_PATH_IMAGE023
；
Figure 747398DEST_PATH_IMAGE024
，
Figure 238423DEST_PATH_IMAGE025
；
wherein,
Figure 499771DEST_PATH_IMAGE026
，
Figure 341825DEST_PATH_IMAGE027
,
Figure 471455DEST_PATH_IMAGE028
for the phase matrix to contain the DOA information,
Figure 364456DEST_PATH_IMAGE029
to represent
Figure 249235DEST_PATH_IMAGE030
The identity matrix of (1);
Figure 250689DEST_PATH_IMAGE031
；
Figure 731480DEST_PATH_IMAGE032
，
Figure 259413DEST_PATH_IMAGE033
；
wherein,
Figure 62897DEST_PATH_IMAGE034
，
Figure 286068DEST_PATH_IMAGE035
，
Figure 85397DEST_PATH_IMAGE036
is a phase matrix containing the DOD and the distance.
8. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein the method of phase matrix matching in the sub-array in the step 7 can be expressed as:
Figure 218569DEST_PATH_IMAGE037
the automatic matching of each sub-array to the target estimation can be realized through the above formula, and the matching method of the phase matrix between the sub-arrays can be expressed as follows:
Figure 445151DEST_PATH_IMAGE038
wherein
Figure 234246DEST_PATH_IMAGE039
Is the 1 st sub-array
Figure 509370DEST_PATH_IMAGE040
The vector of diagonal elements of (a) is composed,
Figure 683999DEST_PATH_IMAGE041
is the ith sub-array
Figure 894532DEST_PATH_IMAGE042
The diagonal elements of (a) constitute a vector.
9. The bistatic FDA-MIMO radar multidimensional parameter joint estimation method of claim 1, wherein the DOA in step 8 can be directly solved by the following formula:
Figure 154612DEST_PATH_IMAGE043
wherein,
Figure 233426DEST_PATH_IMAGE044
the speed of light is indicated and is,
Figure 564961DEST_PATH_IMAGE045
indicating the wavelength of the reference array element,
Figure 867766DEST_PATH_IMAGE046
and representing the space between receiving array elements, decoupling the matrix containing DOD and distance phase, solving the estimation of DOD and distance information, and realizing the joint estimation of DOA, DOD and distance of the target.
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