CN105188133A - KR subspace DOA estimation method based on quasi stationary signal local covariance match - Google Patents
KR subspace DOA estimation method based on quasi stationary signal local covariance match Download PDFInfo
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Abstract
The invention belongs to the field of array signal processing, and relates to a KR subspace DOA estimation method based on quasi stationary signal local covariance match. The method comprises: dividing a received quasi stationary signal sequence into multiple segments of sub-sequences, establishing the covariance matrix of each segment of sub-sequences, vectorizing the covariance matrix, and establishing a new signal model; carrying out covariance match on the covariance matrix of each segment to obtain a match spectrum, and superposing the match spectrums; carrying out denoising and dimensionality reduction on the new model, carrying out singular value decomposition to obtain a noise subspace, and figuring out a spatial spectrum; and combining the spatial spectrum with the match spectrum to carry out spectrum peak search to obtain a direction of arrival. The method provided by the invention has important practical significance in the case of fewer obtained snapshots resulting from that the number of information sources is larger than the number of array elements and that a target moving speed is faster.
Description
Technical Field
The invention belongs to the field of array signal processing, in particular relates to a DOA (Direction of arrival) estimation technology of an underdetermined array of quasi-stationary signals, and provides a KR subspace DOA estimation method based on quasi-stationary signal local covariance matching, aiming at the problems that when the number of information sources is greater than the number of array elements, the DOA estimation algorithm cannot accurately estimate the DOA and the KR subspace algorithm cannot be applied to a scene with a high target moving speed.
Background
Direction of arrival (DOA) estimation is one of the important research problems in array signal processing, and in the fields of radar, sonar, communication, etc., it is often required to determine the position of a signal source, i.e., to identify and locate the signal source, and the direction of arrival estimation theory is generated in order to meet the requirement. Early DOA estimation performed spatial scanning by rotating a single antenna using a rotating device, which had the disadvantages of slow speed and low accuracy; these problems are not solved to a certain extent until the beam forming method appears; however, resolution limitations are an important drawback of beamforming methods. In recent decades, in order to break through the restriction of rayleigh limit on resolution, high resolution algorithms are receiving more and more attention; compared with the conventional method, the high resolution theory brings a brand-new solution to the DOA estimation problem, and greatly improves the performance of the direction of arrival estimation under the condition of limited array element number; high resolution theory has matured over the past few decades of continuous efforts. Similar to analyzing the time domain with the frequency spectrum, the analysis of the space domain with the conventional angle spectrum is a spatial spectrum estimation, such as maximum likelihood estimation proposed by Capon, maximum entropy method proposed by Burg, harmonic decomposition method proposed by Pisarenko, and the like. Then, like the non-linear processing in the time domain spectral estimation, non-linear methods are also applied in the spatial spectral analysis, such as maximum likelihood estimation, maximum entropy estimation (linear prediction), eigenvalue decomposition of autocorrelation matrices, multiple signal classification (MUSIC), and rotation invariant technique based parameter Estimation (ESPRIT). These new methods further improve the angular resolution.
However, the classical spatial spectrum estimation method cannot process the DOA estimation under the condition of underestimation, that is, under the condition that the number of information sources to be estimated is larger than the number of existing array elements, some common high-resolution methods cannot accurately estimate the direction of the information sources. The Wing-KinMA provides a new method in 2010, a concept of a Khatri-Rao subspace is provided by utilizing array covariance matrix vectorization, a DOA estimation problem under the condition that the number of information sources is larger than the number of array elements is discussed aiming at Quasi-Stationary (Quasi-static) narrow-band signals, and the degree of freedom is greatly improved. However, this method has some drawbacks, such as not being able to process coherent signal sources, being able to process only quasi-stationary signals, requiring a sufficiently large number of fast beats of the signal, being unstable in performance in case of low signal-to-noise ratio, etc. In real life, quasi-stationary signals (non-stationary, which can be considered stationary signals in a short time) are always visible everywhere, such as voice signals, video signals, and the like. The DOA estimation of quasi-stationary signals has wide applications, such as the localization of sound sources by microphone arrays, the localization of video signals by airport systems, etc. Therefore, the DOA estimation problem for researching quasi-stationary signals has important practical significance.
Disclosure of Invention
The invention aims to provide a KR subspace DOA estimation method based on quasi-stationary signal local covariance matching by utilizing the property of quasi-stationary signals local stationary, so as to improve the performance of the conventional KR subspace DOA estimation method.
The solution of the invention is: a KR subspace DOA estimation method based on quasi-stationary signal local covariance matching comprises the following steps:
(1) dividing a received signal sequence into a plurality of signal subsequence segments, and estimating a local covariance matrix of each segment of signal subsequence;
(2) on one hand, vectorizing a local covariance matrix of each signal subsequence to construct a new model, and after denoising and dimensionality reduction processing, performing singular value decomposition on the model to obtain a noise subspace and calculate a spatial spectrum; on the other hand, carrying out covariance matching on the local covariance matrix of each signal subsequence, and solving the reciprocal of the local covariance matrix to obtain a matching spectrum of each local covariance matrix;
(3) and superposing the obtained covariance matching spectrums of all the signal subsequences, combining the covariance matching spectrums with the space spectrum, and searching a spectrum peak to obtain a peak value, namely the direction of arrival of the signal.
Furthermore, the method comprises the following specific steps:
setting parameters: the number of signal sources is K, the number of array elements is N, and the array is assumed to be a uniform linear array;
step 1, receiving signal sequenceDividing a sequence with the length of T into M subsequences, wherein the length of each subsequence is L;
step 2, setting the local covariance matrix of each signal subsequence as:
estimating a local covariance matrix:
in combination with a local covariance matrix expression, RmExpressed as:
Rm＝ADmAH+C
wherein,the covariance matrix of the signal source in the mth section, and C is noise;
step 3, vectorizing a local covariance matrix:
and combining the vectorized vectors into a new matrix
Step 4, matching the covariance matrix of each signal subsequence to obtain a corresponding matching spectrum:
wherein, <math>
<mrow>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
</msub>
<mo>=</mo>
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<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
<mi>H</mi>
</msubsup>
<mo>⊗</mo>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
</msub>
</mrow>
</math>
wherein I is M2×M2Of (a), phi (theta) ═ a*⊙AI*⊙I]；
And 5, noise covariance estimation:
wherein, <math>
<mrow>
<msubsup>
<mi>P</mi>
<msub>
<mn>1</mn>
<mi>M</mi>
</msub>
<mo>⊥</mo>
</msubsup>
<mo>=</mo>
<msub>
<mi>I</mi>
<mi>M</mi>
</msub>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mi>M</mi>
</mfrac>
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<mn>1</mn>
<mi>M</mi>
</msub>
<msubsup>
<mn>1</mn>
<mi>M</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> IMis an identity matrix of M multiplied by M,
step 6, dimensionality reduction
Matrix A*According to decomposition of A into A*⊙A＝GB，
Wherein,is an array flow pattern matrix after dimension reduction,
let W be GTG，W＝Diag(1,2,...,N-1,N,N-1,...,2,1)。
Therefore, the reduced signal covariance matrix is:
step 7, matrix matchingSingular value decompositionWhereinAndrespectively, a left feature matrix and a right feature matrix,is a matrix composed of diagonal elements as singular values; obtaining a noise subspace:
step 8, calculating KR-MUSIC spatial spectrum:
and 9, superposing the covariance matching spectrum of each signal subsequence section and the KR-MUSIC spatial spectrum:
P＝PKR-MUSIC(θ)+F1(θ)+…+FM(θ)
in thatAnd searching spectral peaks to find the largest K spectral peaks, wherein the corresponding angle is the estimated direction of arrival.
The invention provides a direction of arrival estimation method for improving performance of a KR subspace method, which is characterized in that on the basis of utilizing a KR subspace method to vector a local covariance matrix of a quasi-stationary signal to estimate a noise subspace, the local covariance matrix of each section is matched by utilizing the stationary property of a local sequence power spectrum of the quasi-stationary signal; the method for combining the integral MUSIC spatial spectrum and the local matching spectrum when the spatial spectrum is scanned is provided, so that the performance of the KR subspace method is improved; in the KR subspace method, enough signal fast beat numbers are needed, otherwise DOA estimation cannot be carried out, and the method provided by the invention can estimate the arrival direction more accurately under the condition of less signal fast beat numbers. In practical application, the DOA estimation method has important practical significance when the DOA estimation is carried out under the condition that the moving speed of the target is high; compared with the traditional high-resolution method, the method improves the degree of freedom, and can estimate more information source directions by using fewer array elements.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a simulated space spectrogram based on the method of the present invention;
FIG. 3, comparison of the performance of the method of the present invention and KR subspace method at varying numbers of partitions.
Detailed Description
The invention is explained in further detail below with reference to the figures and examples; fig. 1 is a schematic flow chart of the present invention, and in combination with the schematic flow chart, the present invention provides a KR subspace DOA estimation method based on quasi-stationary signal local covariance matching, which specifically includes the following steps:
setting parameters: the number of signal sources is K, the number of array elements is N, and the array is assumed to be a uniform linear array.
Step 1, receiving signal sequenceDividing a sequence with the length of T into M subsequences, wherein each subsequence has the length of L.
Let x (t) be [ x ]1(t),...,xN(t)]TThen the model of the received signal is as follows:
x(t)＝As(t)+v(t)，t＝0,1,2,…T
in the above formula, s (t) ═ s1(t),…,sN(t)]TIs a source of the signal or signals,is a noise signal, the array flow pattern is
Wherein,d and lambda are array element spacing and signal wavelength respectively.
The model has several assumptions:
assume that 1: the information sources are zero-mean and are not related to each other;
assume 2: the corresponding signal directions of arrival are not mutually repeated;
assume that 3: the noise v (t) is zero mean stationary with a covariance matrix ofAnd the noise and the source signal are not correlated;
assume 4: for each generalized quasi-stationary signal having a length L of a segment, there are
And 2, estimating a local covariance matrix.
The covariance matrix of the local signal is defined as follows:
m represents the mth segment of the divided signal. The local covariance matrix can be estimated from the local signal average, i.e.
Combining the above assumptions, R can bemExpressed as:
Rm＝ADmAH+C
wherein,is the covariance matrix of the source signal at the mth stage, and C is the noise. Step 3, vectorizing a local covariance matrix:
and combine them into a new matrix
Two important properties of KR product are introduced here, and it should be specifically noted that A, B in the following properties one and two is merely a specific matrix notation for explanation and is not A, B occurring in the context of calculation:
the property one is as follows: suppose thatAnd isD ═ diag (D), then there are
vec(ADBH)＝(B*⊙A)d
Property II: for two matricesAndif krank (A) is equal to or greater than 1 and krank (B) is equal to or greater than 1, the following inequality holds:
krank(A⊙B)≥min{k,krank(A)+krank(B)-1}
from the above properties, a new model can be derived:
wherein, <math>
<mrow>
<mi>Y</mi>
<mover>
<mo>=</mo>
<mi>Δ</mi>
</mover>
<mo>[</mo>
<msub>
<mi>y</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>y</mi>
<mi>M</mi>
</msub>
<mo>]</mo>
<mo>,</mo>
</mrow>
</math> and is
Here, one hypothesis is added:
assume that 5: matrix arrayIs a column full rank matrix.
Step 4, matching the covariance matrix of each section of signal to obtain a corresponding matching spectrum:
in the above formula, the parameter calculation method is as follows:
wherein I is M2×M2Due to the covariance vectorization process of local signals, the identity matrix can be decomposed as follows:
can be found to be Φ (θ) ═ A*⊙AI*⊙I]。
And 5, noise covariance estimation:
wherein,IMan identity matrix of M;
and 6, reducing the dimension.
Matrix A*According to an*According to one embodiment, as defined in claim,
is an array flow pattern matrix after dimensionality reduction, wherein,
let W be GTG，W＝Diag(1,2,...,N-1,N,N-1,...,2,1)。
Therefore, the reduced signal covariance matrix is:
step 7, matrix matchingSingular value decomposition is carried out:
wherein,andrespectively, a left feature matrix and a right feature matrix,is a matrix of diagonal elements as singular values. From this, it can be derived that the noise subspace matrix is:
and 8, obtaining a KR-MUSIC spatial spectrum:
and 9, superposing the covariance matching spectrum of each signal sequence section and the KR-MUSIC spatial spectrum:
P＝PKR-MUSIC(θ)+F1(θ)+…+FM(θ)
in thatAnd carrying out spectrum scanning to find out the largest K spectrum peaks, wherein the corresponding angle is the estimated direction of arrival.
FIG. 2 is a simulated spatial spectrum of a non-coherent narrow band quasi-stationary signal with DOA estimation using the algorithm of the present invention. Wherein the number N of arrays is 4, the number K of sources is 6, and the true direction of arrival angle is { θ }1,…,θK-65 °, -40 °, -20 °,10 °,25 °,42 ° }, half wavelength array element spacing d, total sequence length T7680, signal-to-noise ratio 14 dB. The noise is zero-mean composite white Gaussian noise, the information source is a Gaussian quasi-stationary signal, and the number of divided sections M is 10. The space spectrogram shows that the real direction of arrival angle basically coincides with 6 spectral peaks, and the correctness of the method is proved.
Fig. 3 shows the variation of the RMSE of the two algorithms with an increasing number of segments and a signal-to-noise ratio of 14 dB. From the simulation results, it can be seen that when the number of divided segments is small, i.e. the number of fast beats of the signal is small, the algorithm of the present invention can obtain better estimation performance than the KR subspace method.
The experimental results show that the algorithm can estimate the direction of arrival under the condition that the number of quasi-stationary signal information sources is greater than the number of array elements, and the degree of freedom of the algorithm is greatly improved. Compared with the KR subspace algorithm, the performance of the KR subspace algorithm is improved, particularly in the KR subspace algorithm, enough signal fast beats are needed, otherwise DOA estimation cannot be carried out, and the algorithm provided by the invention can more accurately estimate the signal wave arrival direction under the condition of less signal fast beats, and has important practical significance for application scenes with higher target moving speed in practical application.
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (2)
1. A KR subspace DOA estimation method based on quasi-stationary signal local covariance matching comprises the following steps:
(1) dividing a received signal sequence into a plurality of signal subsequence segments, and estimating a local covariance matrix of each segment of signal subsequence;
(2) on one hand, vectorizing a local covariance matrix of each signal subsequence to construct a new model, and after denoising and dimensionality reduction processing, performing singular value decomposition on the model to obtain a noise subspace and calculate a spatial spectrum; on the other hand, carrying out covariance matching on the local covariance matrix of each signal subsequence, and solving the reciprocal of the local covariance matrix to obtain a matching spectrum of each local covariance matrix;
(3) and superposing the obtained covariance matching spectrums of all the signal subsequences, combining the covariance matching spectrums with the space spectrum, and searching a spectrum peak to obtain a peak value, namely the direction of arrival of the signal.
2. The method for estimating the KR subspace DOA based on the quasi-stationary signal local covariance matching as claimed in claim 1, wherein the method comprises the following specific steps:
setting parameters: the number of signal sources is K, the number of array elements is N, and the array is assumed to be a uniform linear array;
step 1, receiving signal sequenceDividing a sequence with the length of T into M subsequences, wherein the length of each subsequence is L;
step 2, setting the local covariance matrix of each signal subsequence as:
estimating a local covariance matrix:
in combination with a local covariance matrix expression, RmIs shown as
Rm＝ADmAH+C
Wherein,the covariance matrix of the signal source in the mth section, and C is noise;
step 3, vectorizing a local covariance matrix:
and combining the vectorized vectors into a new matrix
Step 4, matching the covariance matrix of each signal subsequence to obtain a corresponding matching spectrum:
wherein, <math>
<mrow>
<msub>
<mover>
<mi>Q</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
</msub>
<mo>=</mo>
<msubsup>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
<mi>H</mi>
</msubsup>
<mo>⊗</mo>
<msub>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mi>m</mi>
</msub>
</mrow>
</math>
wherein I is M2×M2Of (a), phi (theta) ═ a*⊙AI*⊙I]；
And 5, noise covariance estimation:
wherein, <math>
<mrow>
<msubsup>
<mi>P</mi>
<msub>
<mn>1</mn>
<mi>M</mi>
</msub>
<mo>⊥</mo>
</msubsup>
<mo>=</mo>
<msub>
<mi>I</mi>
<mi>M</mi>
</msub>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mi>M</mi>
</mfrac>
<msub>
<mn>1</mn>
<mi>M</mi>
</msub>
<msubsup>
<mn>1</mn>
<mi>M</mi>
<mi>T</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> IMis an identity matrix of M multiplied by M,
step 6, dimensionality reduction
Matrix A*According to decomposition of A into A*⊙A＝GB，
Wherein, is an array flow pattern matrix after dimension reduction,
let W be GTG，W＝Diag(1,2,...,N-1,N,N-1,...,2,1)；
Therefore, the reduced signal covariance matrix is:
step 7, matrix matchingSingular value decompositionWhereinAndrespectively, a left feature matrix and a right feature matrix,is a matrix composed of diagonal elements as singular values; obtaining a noise subspace:
step 8, calculating KR-MUSIC spatial spectrum:
and 9, superposing the covariance matching spectrum of each signal subsequence section and the KR-MUSIC spatial spectrum:
P＝PKR-MUSIC(θ)+F1(θ)+…+FM(θ)
in thatAnd searching spectral peaks to find the largest K spectral peaks, wherein the corresponding angle is the estimated direction of arrival.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105403874A (en) * | 2015-12-25 | 2016-03-16 | 西安电子科技大学 | Evaluation method of undetermined wave arrival direction of non-uniform array |
| CN106291449A (en) * | 2016-08-04 | 2017-01-04 | 大连大学 | Direction of arrival angular estimation new method under symmetric-stable distribution noise |
| CN107483130A (en) * | 2017-10-13 | 2017-12-15 | 电子科技大学 | One kind joint broader frequency spectrum perceives and angle-of- arrival estimation method |
| CN110095750A (en) * | 2019-05-28 | 2019-08-06 | 中国人民解放军国防科技大学 | Quick two-dimensional underdetermined angle measurement method based on quasi-stationary signal sparse reconstruction |
| CN111521968A (en) * | 2020-05-22 | 2020-08-11 | 南京理工大学 | Underdetermined DOA estimation method based on target space diversity |
| CN113325376A (en) * | 2021-05-27 | 2021-08-31 | 重庆邮电大学 | Method for correcting electromagnetic cross coupling error of phase control array under color noise |
| WO2021196165A1 (en) * | 2020-04-03 | 2021-10-07 | 华为技术有限公司 | Frequency analysis method, device and radar |
| CN114938536A (en) * | 2022-06-08 | 2022-08-23 | 南京航空航天大学 | Direct positioning method for steady signal of pin alignment under nested array |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015017009A1 (en) * | 2013-08-01 | 2015-02-05 | Raytheon Company | Detection of a material of interest in a compressed hyperspectral scene |
| CN104749554A (en) * | 2015-03-20 | 2015-07-01 | 江苏大学 | Recursive rank loss based amplitude phase error calibrating and wave arrival direction estimating method |
| CN104793176A (en) * | 2015-04-28 | 2015-07-22 | 周林 | FPGA (field programmable gate array) based DOA (direction of arrival) estimation fast-implementing method |
-
2015
- 2015-08-11 CN CN201510491435.5A patent/CN105188133B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015017009A1 (en) * | 2013-08-01 | 2015-02-05 | Raytheon Company | Detection of a material of interest in a compressed hyperspectral scene |
| CN104749554A (en) * | 2015-03-20 | 2015-07-01 | 江苏大学 | Recursive rank loss based amplitude phase error calibrating and wave arrival direction estimating method |
| CN104793176A (en) * | 2015-04-28 | 2015-07-22 | 周林 | FPGA (field programmable gate array) based DOA (direction of arrival) estimation fast-implementing method |
Non-Patent Citations (3)
| Title |
|---|
| 林文凤: "阵列快速DOA估计算法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 贺红兵: "GNSS匹配谱干扰的来波方向估计算法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 高龙超: "分布式阵列特性及其波达方向估计算法研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105403874A (en) * | 2015-12-25 | 2016-03-16 | 西安电子科技大学 | Evaluation method of undetermined wave arrival direction of non-uniform array |
| CN106291449A (en) * | 2016-08-04 | 2017-01-04 | 大连大学 | Direction of arrival angular estimation new method under symmetric-stable distribution noise |
| CN106291449B (en) * | 2016-08-04 | 2018-10-16 | 大连大学 | Direction of arrival angle method of estimation under symmetric-stable distribution noise |
| CN107483130A (en) * | 2017-10-13 | 2017-12-15 | 电子科技大学 | One kind joint broader frequency spectrum perceives and angle-of- arrival estimation method |
| CN110095750A (en) * | 2019-05-28 | 2019-08-06 | 中国人民解放军国防科技大学 | Quick two-dimensional underdetermined angle measurement method based on quasi-stationary signal sparse reconstruction |
| WO2021196165A1 (en) * | 2020-04-03 | 2021-10-07 | 华为技术有限公司 | Frequency analysis method, device and radar |
| CN111521968A (en) * | 2020-05-22 | 2020-08-11 | 南京理工大学 | Underdetermined DOA estimation method based on target space diversity |
| CN113325376A (en) * | 2021-05-27 | 2021-08-31 | 重庆邮电大学 | Method for correcting electromagnetic cross coupling error of phase control array under color noise |
| CN114938536A (en) * | 2022-06-08 | 2022-08-23 | 南京航空航天大学 | Direct positioning method for steady signal of pin alignment under nested array |
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