CN113255098B - Distributed information source space domain parameter estimation method based on finite information rate - Google Patents
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Abstract
The invention discloses a distributed information source space domain parameter estimation method based on a finite information rate. The invention relates to the technical field of signal processing, which integrates all plane waves of a receiving signal of an antenna along a unit circle, and determines an expansion coefficient of a sub-band taking frequency omega as a center in a measuring antenna array; establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q’ Determining a transformation matrix; constructing a zero filter and reconstructing a source azimuth angle; and (5) carrying out constraint optimization, constructing a linear equation system, and determining a central angle and a diffusion angle. Compared with traditional distributed information source space domain parameter estimation methods such as a DSPE algorithm and a robust Capon beamforming method, the method can reduce the number of array elements and the number of snapshots required by experiments, and is not influenced by the shape of the array under the condition of low signal to noise ratio, so that the complexity of sampling and processing processes is reduced, and the method is simpler and more convenient.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a distributed information source space domain parameter estimation method based on a finite information rate.
Background
Array signal processing is an important branch of signal processing and has important application in many national economic and military fields such as communication, radar, sonar, navigation, geological exploration, oceanography, biomedicine, radio astronomy and the like. Direction of Arrival (DOA) estimation is one of the main research directions in the field of array signal processing.
In the conventional DOA estimation method, for the sake of simple processing, a target signal is generally assumed to be a point signal source, but an actual signal always generates a certain angular spread in space due to factors such as multipath scattering, and the like, and the signal with the angular spread is collectively referred to as a distributed signal source, so that spatial parameters of the distributed signal source include a central arrival direction and a spread angle. For distributed sources, processing with the conventional point source parameter estimation method will cause severe performance degradation and even result in wrong estimation result. It is therefore necessary to build mathematical models that accurately describe the characteristics of the distributed sources and to propose parameter estimation algorithms specific to these models.
At present, the traditional relatively representative space domain parameter estimation methods include DSPE algorithm, robust Capon beamforming method and other algorithms, and can solve the central direction of arrival and the diffusion angle through a distributed information source space domain parameter estimation method, but the above parameter estimation methods at present have the problems of large number of array elements and large number of snapshots, so that the system is complex, and resources are greatly wasted; although the number of required array elements is reduced, the method is affected by the shape of an array and needs to be performed on a spatial grid, so that not only is the amount of calculation increased, but also problems such as model mismatching and the like are caused when the method leaves a grid DOA, and the estimation performance is reduced.
In order to break through the limitation of grids and realize the spatial domain parametric estimation of any array, few array elements and few snapshots, the Finite information Rate (FRI) sampling theory proposed in recent years is a brand-new parametric signal under-nyquist sampling theory, and a special sampling kernel and a sampling structure are designed by using the parametric characteristics of signals, so that the sampling is carried out at a Rate close to the signal new information Rate, and the accurate estimation of unknown parameters is completed through a certain reconstruction algorithm. The innovation rate of the parametric signal is defined as the number of degrees of freedom of the signal per unit time, usually well below its nyquist frequency, thus minimizing the sampling rate.
The FRI sampling theory is directly provided for continuous signals, the problem of discrete grids is avoided, the influence of array shapes is avoided, and the parameter estimation resolution is higher; moreover, the method has the characteristic of direct parameter sampling, and the critical minimum sampling rate and the number of sampling points of DOA estimation are expected to be reached. The FRIDA method recently proposed by Pan Hanjie et al is applicable to any array, is not limited by grids, and can better realize super-resolution under the condition of low SNR or less snapshots.
In conclusion, the FRI technology is applied to spatial domain parameter estimation of the distributed information source, and has important practical application significance. The VPW-FRI signal model based on the Lorentz function has strong fitting capacity to complex pulse signals, has strong spatial parameter description capacity, and can well describe the distributed information source.
Disclosure of Invention
The invention provides a distributed information source airspace parameter estimation method based on limited new information rate, aiming at realizing the airspace parameter estimation of distributed signals without the limitation of spatial grids under the conditions of low signal-to-noise ratio, less snapshots and arbitrary arrays, and the invention provides the following technical scheme:
a distributed information source space domain parameter estimation method based on finite information rate comprises the following steps:
step 1: integrating all plane waves of a receiving signal of an antenna along a unit circle, and determining an expansion coefficient of a sub-band taking frequency omega as a center in a measuring antenna array;
and 2, step: establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q’ Determining a transformation matrix;
and step 3: constructing a zero filter and reconstructing a source azimuth angle;
and 4, step 4: and (5) carrying out constraint optimization, constructing a linear equation set, and determining a central angle and a diffusion angle.
Preferably, the step 1 specifically comprises:
step 1.1: is determined to be located at gamma q The received signal of the q-th antenna of (1) is the integral of all plane waves along the unit circle, expressed by:
in a measuring antenna array, cross-correlation between the received signals of an antenna pair (q, q'), the average estimate of the received signals is made:
wherein q, q 'belongs to [1, Q ] and q is not equal to q';
V q,q’ estimated by averaging the frames, assuming that the sources are uncorrelated sources, the cross-correlation reduction is expressed by:
wherein the content of the first and second substances,
step 1.2: instead of using the electromagnetic signal y generated by the celestial body q FRI sampling method using correlation V as input q,q’ The number of antennas is reduced, and the measurement times are increased;
step 1.3: solving the relevant expansion coefficients of the sub-band centered on the frequency ω, with p supported on the circle, the signal model being equivalently written as a fourier coefficient expansion, the signal model being represented by:
wherein, Y m (p) is the basis of a Fourier series,
and is
Is the relative expansion coefficient of a subband centered on frequency ω, said expansion coefficient being expressed by:
wherein Δ p represents the amount of change in position in a two-dimensional plane,
preferably, the step 2 specifically comprises:
establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q’ Is represented by the following equation:
wherein (a) is a Jacobi-Anger expansion from the complex index, J m (. Is a Bessel function of the first kind;
solving a transformation matrix G v Vectorization of V q,q’ (ω), q ≠ q' through
Let vector b (ω) be
M ∈ M Fourier coefficients, where M is a set of Fourier coefficients defining a Q (Q-1) xM matrix G v (ω) is:
wherein G is v Is indexed by the microphone pair (q, q'), and G v Is indexed by fourier column m;
will V q,q’ Write as: a (ω) = G (ω) b (ω).
Preferably, the step 3 specifically comprises:
to solve for h m Root of polynomial of
To construct a nulling filter;
is a weighted sum of the uniformly sampled asymmetric pulses VPW-FRI,
a set of nulling filter equations is satisfied:
wherein h is m Is an unknown zero-filter to be recovered, the coefficients of which are formed by a filter h m Root of a given polynomial
The source azimuth angle theta is then reconstructed by root finding with a polynomial k ；
In a multi-band setup, uniform sinusoidal sampling per sub-band
Otherwise, a filter h is determined m Eliminating all omega
Namely:
preferably, the step 4 specifically includes:
step 4.1: and (3) carrying out constraint optimization aiming at the VPW-FRIDA estimation method:
b i *h＝0,i＝1,…,J
wherein, a i ＝c+d*j(j 2 ＝-1)，a i ,b i And G and i is a uniform sinusoidal sampling of the cross-correlations, and a linear mapping of the ith subband, H is the feasible set to which the zero filter coefficients belong
By replacing b i The solution of (a) obtains an optimization for h alone:
when reaching a certain approximate level (epsilon) 2 )：
The solution of the time is the final effective solution;
and 4.2: further perfecting an optimization equation;
wherein β = (G) H G) -1 G H a, R (-) is the right dual of T (-);
step 4.3: building a linear equation set and solving h m ；
To solve for updated h m The following linear system of equations is solved first:
wherein the content of the first and second substances,
and λ is a newly introduced auxiliary variable, where b m The update value can be found by:
according to the obtained zero filter coefficient, solving the root u of the equation k ；
u k ＝roots(h m )
According to the root of the equation, solving the central angle and the spread angle:
the invention has the following beneficial effects:
compared with traditional distributed information source airspace parameter estimation methods such as DSPE (direct sequence evolution) algorithm and robust Capon beamforming method, the method provided by the patent can reduce the number of array elements and snapshot number required by experiments, and is not influenced by the shape of the array under the condition of low signal-to-noise ratioTherefore, the complexity of the sampling and processing process is reduced, and the method is simpler and more convenient. Compared with the FRIDA method, the method provided by the invention is suitable for more complex distributed information sources, has wide application range and strong practicability, respectively depicts the same signal into a point source signal and a distributed signal, and respectively reconstructs by using the FRIDA method and the VPW-FRIDA method provided by the patent under the condition that the signal to noise ratio is 10dB, so that the reconstruction effect of the VPW-FRIDA algorithm is better than that of the FRIDA. (period Δ θ) k For reconstruction error)
Drawings
FIG. 1 is a schematic diagram of an antenna array receiving electromagnetic waves emitted by a celestial body;
FIG. 2 is a graph of the reconstruction errors for two algorithms at different fast beat numbers;
FIG. 3 is a graph of the reconstruction error for two algorithms at different center angles;
FIG. 4 is a waveform of the VPW pulse train in the angular domain;
FIG. 5 is an angular domain waveform diagram of a reconstructed VPW burst;
FIG. 6 is a reconstructed center angle diagram;
fig. 7 is a reconstructed divergence angle.
Detailed Description
The present invention is described in detail below with reference to specific examples.
The first embodiment is as follows:
as shown in fig. 1 to 7, the present invention provides a finite-information-rate-based distributed source-space parameter estimation method, which includes the following steps:
a distributed information source airspace parameter estimation method based on finite information rate comprises the following steps:
as shown in FIG. 1, the antenna array has Q antennas located thereon
Celestial bodies transmit k monochromatic and uncorrelated distributed signals. Each propagating p in the direction of the unit vector k ＝[cosθ k ,sinθ k ] T Wherein theta k Is the azimuth angle of the kth electromagnetic wave.
The baseband representation of the signal from the direction p e S within a narrow band centered on the frequency w
Wherein
Is the signal resulting from radio interference received by the antenna at p and frequency w. From an energy perspective, it can be described as:
wherein
Is the energy of the kth source, δ (p) is the Dirac delta function on S.
The signal model can therefore be written as:
step 1: integrating all plane waves of a receiving signal of an antenna along a unit circle, and determining an expansion coefficient of a sub-band taking frequency omega as a center in a measuring antenna array;
the step 1 specifically comprises the following steps:
step 1.1: is determined to be located at gamma q The received signal of the q-th antenna of (1) is the integral of all plane waves along the unit circle, expressed by:
in a measuring antenna array, cross-correlation between the received signals of an antenna pair (q, q'), the average estimate of the received signals is made:
wherein q, q 'belongs to [1, Q ] and q is not equal to q';
V q,q’ estimated by averaging the frames, assuming the sources are uncorrelated sources, the cross-correlation reduction is represented by:
wherein the content of the first and second substances,
step 1.2: instead of generating electromagnetic signals y from celestial bodies q FRI sampling method using as input, using correlation V q,q’ The number of antennas is reduced, and the measurement times are increased;
step 1.3: solving the relevant expansion coefficient of the sub-band centered on the frequency ω, with p supported on the circle, the signal model being equivalently written as a fourier coefficient expansion, the signal model being represented by:
wherein, Y m (p) is the basis of a Fourier series,
and is provided with
Is the relative expansion coefficient of the subband centered on frequency ω, said expansion coefficient being represented by:
wherein Δ p represents the amount of change in position in a two-dimensional plane,
and 2, step: establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q’ Determining a transformation matrix;
the step 2 specifically comprises the following steps:
establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q’ Is represented by the following equation:
wherein (a) is a Jacobi-Anger expansion from the complex index, J m (. Is a Bessel function of the first kind;
solving a transformation matrix G v Vectorization of V q,q’ (ω), q ≠ q' through
Let vector b (ω) be
M ∈ Fourier coefficient of M, where M is a set of Fourier coefficients, defining a Q (Q-1) x M matrix G v (ω) is:
wherein G is v Is indexed by the microphone pair (q, q'), and G v Is indexed by fourier column m;
will V q,q’ Writing as follows: a (ω) = G (ω) b (ω).
And step 3: constructing a zero filter and reconstructing a source azimuth angle;
the step 3 specifically comprises the following steps:
to solve for h m Root of polynomial of
To construct a nulling filter;
is a weighted sum of the uniformly sampled asymmetrical pulses VPW-FRI,
a set of nulling filter equations is satisfied:
wherein h is m Is an unknown zero-filter to be recovered, the coefficients of which are passed through a filter h m Root of a given polynomial
The source azimuth angle theta is then reconstructed by root finding with a polynomial k ；
In a multi-band setup, uniform sinusoidal sampling per sub-band
Otherwise, a filter h is determined m Eliminating all omega
Namely:
and 4, step 4: and (5) carrying out constraint optimization, constructing a linear equation set, and determining a central angle and a diffusion angle.
The step 4 specifically comprises the following steps:
step 4.1: and (3) carrying out constraint optimization aiming at the VPW-FRIDA estimation method:
b i *h＝0,i＝1,…,J
wherein, a i ＝c+d*j(j 2 ＝-1)，a i ,b i And G and i is a uniform sinusoidal sampling of the cross-correlations, and a linear mapping of the ith subband, H is the feasible set to which the zero filter coefficients belong
By replacing b i The solution of (a) obtains an optimization for h alone:
when reaching a certain approximate level (epsilon) 2 )：
The solution of the time is the final effective solution;
and 4.2: further perfecting an optimization equation;
wherein β = (G) H G) -1 G H a, R (-) is the right pair of T (-);
step 4.3: building a linear equation set and solving h m ；
To solve for updated h m First, the following linear equation set is solved:
wherein the content of the first and second substances,
and λ is a newly introduced auxiliary variable, where b m The update value can be found by:
solving the root u of the equation according to the obtained zero filter coefficient k ；
u k ＝roots(h m )
According to the root of the obtained equation, solving the central angle and the divergence angle:
compared with the traditional distributed information source airspace parameter estimation methods such as a DSPE algorithm, a robust Capon beamforming method and the like, the method provided by the patent can reduce the array element number and the snapshot number required by the experiment, is not influenced by the array shape under the condition of low signal-to-noise ratio,therefore, the complexity of the sampling and processing process is reduced, and the method is simpler and more convenient. Compared with the FRIDA method, the method provided by the patent is suitable for more complicated distributed information sources, is wide in application range and strong in practicability, the same signal is respectively carved into a point source signal and a distributed signal, the FRIDA method and the VPW-FRIDA method provided by the patent are respectively used for reconstruction under the condition that the signal-to-noise ratio is 10dB, and the result is shown in the following figure 2 and figure 3, and the reconstruction effect of the VPW-FRIDA algorithm is superior to that of the FRIDA. (period. DELTA.. Theta.) of k As a reconstruction error).
The setting group is fit to a VPW-FRI signal by four single VPW signals, and the arrival angle theta is i The degree is selected within the range of (-90 degrees and 90 degrees), additive white gaussian noise is superimposed on the signal, the input signal noise is 10dB, the snapshot number is set to 10, and experiments are performed, wherein fig. 4 is the waveform of the original signal angle domain, fig. 5 is the waveform of the reconstructed signal angle domain, fig. 6 is the waveform of the VPW-FRI signal center angle reconstruction, and fig. 7 is the waveform of the signal diffusion angle reconstruction.
The above is only a preferred embodiment of the finite-information-rate-based distributed information source spatial domain parameter estimation method, and the protection range of the finite-information-rate-based distributed information source spatial domain parameter estimation method is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection range of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.
Claims (4)
1. A distributed information source airspace parameter estimation method based on finite information rate is characterized in that: the method comprises the following steps:
step 1: integrating all plane waves of a receiving signal of an antenna along a unit circle, and determining a related expansion coefficient of a sub-band taking frequency omega as a center in a measuring antenna array;
and 2, step: establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q, Determining a transformation matrix;
and step 3: constructing a zero filter and reconstructing a source azimuth angle;
and 4, step 4: performing constraint optimization, constructing a linear equation set, and determining a central angle and a diffusion angle;
the step 4 specifically comprises the following steps:
step 4.1: and (3) carrying out constraint optimization aiming at the VPW-FRIDA estimation method:
b i *h＝0,i＝1,…,J
wherein, a i ＝c+d*j，j 2 ＝-1，a i ,b i And G and i is a uniform sinusoidal sampling of the cross-correlations, and a linear mapping of the ith subband, H is the feasible set to which the nulling filter coefficients belong, H = { H ∈ K+1 :||h|| 2 ＝1}；
By replacing b i The solution of (a) obtains an optimization for h alone:
when a certain approximate level epsilon is reached 2 ：
The solution of the time is the final effective solution;
and 4.2: further perfecting an optimization equation;
wherein, β = (G) H G) -1 G H a, R () is the right-pair of T ();
step 4.3: building a linear equation set and solving h m ；
To solve for updated h m The following linear system of equations is solved first:
wherein, l,
And λ is a newly introduced auxiliary variable, where b m The update value can be found by:
solving the root u of the equation according to the obtained zero filter coefficient k ；
u k ＝roots(h m )
According to the root of the equation, solving the central angle and the spread angle:
2. the finite-information-rate-based distributed source-space-domain parameter estimation method of claim 1, wherein: the step 1 specifically comprises the following steps:
step 1.1: is determined to be located at gamma q Of the q-th antennaThe signal is the integral of all plane waves along the unit circle, and is represented by:
in a measuring antenna array, cross-correlation between the received signals of an antenna pair (q, q'), the average estimate of the received signals is made:
wherein, q, q 'belongs to [1, Q ] and q is not equal to q';
V q,q, estimated by averaging the frames, assuming the sources are uncorrelated sources, the cross-correlation reduction is represented by:
wherein, delta gamma q,q' 
Step 1.2: instead of using the electromagnetic signal y generated by the celestial body q FRI sampling method using correlation V as input q,q, The use of the antenna is reduced, and the measurement times are increased;
step 1.3: solving the relevant expansion coefficient of the sub-band centered on the frequency ω, with p supported on the circle, the signal model being equivalently written as a fourier coefficient expansion, the signal model being represented by:
wherein, Y m (p) is the basis of a Fourier series,
and is
Is the relative expansion coefficient of the subband centred on frequency ω, said expansion coefficient being represented by:
wherein Δ p represents the amount of change in position in a two-dimensional plane,
3. the finite-information-rate-based distributed source-space-domain parameter estimation method of claim 2, wherein: the step 2 specifically comprises the following steps:
establishing Fourier coefficients of slave VPW-FRI model
To a given measured value V q,q, Is represented by the following equation:
wherein (a) is a Jacobi-Anger expansion from the complex index, J m () Is a first type of Bessel function;
solving a transformation matrix G v Vectorization of V q,q, (ω), q ≠ q' through a (ω) epsilon Q(Q-1) Let vector b (ω) be
M ∈ M Fourier coefficients, where M is a set of Fourier coefficients defining a Q (Q-1) x M matrix G v (ω) is:
wherein, G v Is indexed by the microphone pair (q, q'), and G v Is indexed by fourier column m;
will V q,q, Write as: a (ω) = G (ω) b (ω).
4. The finite-information-rate-based distributed source-space-domain parameter estimation method of claim 3, wherein: the step 3 specifically comprises the following steps:
to solve for h m Root of polynomial of
To construct a nulling filter;
is a weighted sum of the uniformly sampled asymmetric pulses VPW-FRI,
a set of nulling filter equations is satisfied:
wherein h is m Is an unknown zero-filter to be recovered, the coefficients of which are formed by a filter h m Root of a given polynomial
The source azimuth angle theta is then reconstructed by root finding with a polynomial k ；
In a multi-band setup, uniform sinusoidal sampling per sub-band
Otherwise, determine oneA filter h m Eliminating all omega
Namely:
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101860748A (en) * | 2010-04-02 | 2010-10-13 | 西安电子科技大学 | Side information generating system and method based on distribution type video encoding |
| CN104237843A (en) * | 2014-09-04 | 2014-12-24 | 电子科技大学 | Estimation method of two-dimensional central direction of arrival for distributed sources |
| CN110658517A (en) * | 2019-10-11 | 2020-01-07 | 深圳大学 | Dimensionality reduction sparse STAP method and device based on uncertain priori knowledge |
| CN110944336A (en) * | 2019-10-18 | 2020-03-31 | 浙江工业大学 | Time-frequency spectrum sensing method based on limited new information rate |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005116686A2 (en) * | 2004-05-28 | 2005-12-08 | Telefonaktiebolaget Lm Ericsson (Publ) | Method and arrangement for determining the spatial frequency of a signal |
| US8787501B2 (en) * | 2009-01-14 | 2014-07-22 | Qualcomm Incorporated | Distributed sensing of signals linked by sparse filtering |
| US20130158420A1 (en) * | 2011-12-14 | 2013-06-20 | Qualcomm Incorporated | System and method for analysis and reconstruction of variable pulse-width signals with finite-rates-of-innovation |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101860748A (en) * | 2010-04-02 | 2010-10-13 | 西安电子科技大学 | Side information generating system and method based on distribution type video encoding |
| CN104237843A (en) * | 2014-09-04 | 2014-12-24 | 电子科技大学 | Estimation method of two-dimensional central direction of arrival for distributed sources |
| CN110658517A (en) * | 2019-10-11 | 2020-01-07 | 深圳大学 | Dimensionality reduction sparse STAP method and device based on uncertain priori knowledge |
| CN110944336A (en) * | 2019-10-18 | 2020-03-31 | 浙江工业大学 | Time-frequency spectrum sensing method based on limited new information rate |
Non-Patent Citations (3)
| Title |
|---|
| Distributed Sampling of Multiple Sinusoids with Finite Rate of Innovation;Zhiliang Wei et al.;《2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)》;20190909;1-5 * |
| Sampling and Exact Reconstruction of Pulses with Variable Width;Gilles Baechler et al;《 IEEE Transactions on Signal Processing》;20170215;第65卷(第10期);2629-2644 * |
| 分布式信源数据域直接位置估计方法;王大鸣等;《电子与信息学报》;20171228;第40卷(第2期);371-377 * |
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