CN111199281B - Short wave single station direct positioning deviation compensation method based on geographical coordinate airspace position spectrum - Google Patents

Short wave single station direct positioning deviation compensation method based on geographical coordinate airspace position spectrum Download PDF

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CN111199281B
CN111199281B CN201911326261.1A CN201911326261A CN111199281B CN 111199281 B CN111199281 B CN 111199281B CN 201911326261 A CN201911326261 A CN 201911326261A CN 111199281 B CN111199281 B CN 111199281B
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王鼎
王成
唐涛
张莉
杨宾
李建兵
魏帅
吴志东
李崇
孙晨
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Abstract

The invention discloses a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum, which comprises the steps of establishing an algebraic relation formula of a short-wave correction source geographical coordinate and a two-dimensional arrival direction of a radiation signal of the short-wave correction source geographical coordinate to an observation station, and obtaining the geographical coordinate airspace position spectrum of the short-wave correction source; collecting numerical values near the main peak of the geographical coordinate spatial domain position spectrum of each short wave correction source, constructing a geographical coordinate spatial domain position spectrum matrix according to the numerical values, and training a radial basis function neural network; establishing an algebraic relation between the geographic coordinates of the short-wave target source and the two-dimensional arrival direction of the radiation signal of the short-wave target source to the observation station, and obtaining a spatial domain position spectrum of the geographic coordinates of the short-wave target source; and finally, collecting numerical values near the main peak of the spatial spectrum of the short wave target source geographical coordinate, and inputting the spatial spectrum matrix of the short wave target source geographical coordinate into a radial basis function neural network to obtain a final estimation value of the short wave target source geographical coordinate. The invention can effectively compensate the positioning deviation caused by the ionospheric pseudo-height error and the ionospheric inclination angle deviation.

Description

Short wave single station direct positioning deviation compensation method based on geographical coordinate airspace position spectrum
Technical Field
The invention belongs to the technical field of target radiation source positioning, and particularly relates to a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum.
Background
As is well known, the target radiation source positioning technology has been widely applied to the fields of communication, radar, target monitoring, navigation telemetry, seismic survey, radio astronomy, emergency rescue, safety management, etc., and plays an important role in industrial production and military applications. Target radiation source location techniques refer to the determination of target position parameters (and sometimes velocity parameters) by receiving wireless signals radiated by a target without an active transmission of electromagnetic signals by an observation station (also called a sensor). The technology belongs to the passive positioning category, and the system does not actively transmit electromagnetic signals, so that the technology has the advantages of strong survival capability, long reconnaissance action distance and the like. The radiation source positioning system can be divided into a single-station positioning system and a multi-station positioning system according to the number of the observation stations, wherein the single-station positioning system has the advantages of high flexibility, strong maneuverability, simple system, no need of inter-station communication and synchronization and the like, and the patent mainly relates to a single-station passive positioning system.
In the existing single-station passive positioning system, short-wave single-station positioning is a widely applied positioning method, and the method mainly aims at positioning over-the-horizon short-wave target sources. The basic principle is to locate the short wave radiation source by using the azimuth angle and elevation angle of the signal measured by a single observation station and the ionospheric virtual height parameter. However, in practical applications, the ionospheric pseudo-height parameter is obtained by active probing, and therefore, it is inevitable that there is a certain deviation, and besides, the ionospheric tilt angle also has a certain deviation to the azimuth estimate. It is not difficult to imagine that both ionospheric pseudo-high errors and ionospheric tilt angle deviations have a large effect on short-wave single-station positioning.
On the other hand, the conventional passive positioning technology mostly adopts a two-step estimation method, that is, firstly, relevant parameters (mainly including parameters of a space domain, a time domain, a frequency domain, an energy domain and the like) for positioning are extracted from a received signal, and then, a target position parameter or a target speed parameter is determined by using the intermediate parameters. Although this two-step positioning mode is widely used in modern passive positioning systems, israeli A.J.Weiss and A.Amar address the drawbacks that exist therein and propose the idea of direct positioning (Amar A, weiss A J.Localization of nano radio based on Doppler frequency shift [ J ]. IEEE Transactions on Signal Processing,2008,56 (11): 5500-5508) (Weiss A J.direct gel of wireless based on Signal Processing,2011,59 (6): 2513-5520.) which basically is the idea of estimating the position parameters of an object directly from the Signal-acquired data field without the need for estimating other intermediate positioning parameters. Obviously, the direct positioning system is also suitable for a short-wave single-station positioning scene. Unfortunately, short-wave single-station direct positioning methods still suffer from ionospheric pseudo-high errors and ionospheric tilt angle deviations, resulting in large positioning deviations. Aiming at the problem, the short wave correction source information of the short wave target source nearby area is utilized, the short wave single station direct positioning deviation compensation method based on the geographical coordinate airspace position spectrum is provided, and the short wave single station direct positioning precision can be greatly improved.
Disclosure of Invention
The invention provides a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum, aiming at the problem that a short-wave single-station direct positioning method in the existing single-station passive positioning method is influenced by ionosphere virtual height errors and ionosphere inclination angle deviations, so that larger positioning deviation can be generated.
In order to achieve the purpose, the invention adopts the following technical scheme:
a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum comprises the following steps:
step 1: n short wave correction sources with accurately known geographic coordinates are placed in a time-sharing mode in the region of the airspace position where the short wave target source is located;
and 2, step: establishing an algebraic relation between the geographic coordinate of the nth short-wave correction source and the two-dimensional arrival direction of the radiation signal of the nth short-wave correction source reaching the observation station by using the geographic coordinate of the observation station and the known ionospheric virtual height information, wherein N is more than or equal to 1 and less than or equal to N;
and 3, step 3: aiming at the nth short wave correction source, receiving and collecting a radiation signal of the nth short wave correction source by using a K-element uniform circular array, and obtaining a geographical coordinate airspace position spectrum of the nth short wave correction source by using a subspace method;
and 4, step 4: aiming at the nth short wave correction source, gridding the region where the main peak of the geographical coordinate airspace position spectrum of the nth short wave correction source is located according to a certain step length, and constructing a geographical coordinate airspace position spectrum matrix of the nth short wave correction source by using the spectrum value of each grid
Figure BDA0002328462270000021
And 5: training a radial basis function neural network by using the geographical coordinate airspace position spectrum matrix of the N short wave correction sources and the corresponding real geographical coordinates;
and 6: establishing an algebraic relation between the geographical coordinates of the short-wave target source and the two-dimensional arrival direction of the radiation signal of the short-wave target source to the observation station by using the geographical coordinates of the observation station and the ionosphere virtual height information;
and 7: aiming at a short wave target source, a K-element uniform circular array is utilized to receive and collect a radiation signal of the short wave target source, and a subspace method is utilized to obtain a spatial domain position spectrum of the short wave target source in terms of geographic coordinates;
and 8: aiming at the short wave target source, gridding the region near the main peak of the geographical coordinate airspace position spectrum of the short wave target source according to the step length in the step 4, and constructing a geographical coordinate airspace position spectrum matrix P related to the short wave target source by using the spectrum value of each grid (e) ；
And step 9: and (5) converting the spatial domain position spectrum matrix of the geographical coordinates obtained in the step (8) into vectors by using a vectorization operator, and inputting the vectors into the radial basis function neural network trained in the step (5), wherein the output value of the network is the final estimated value of the geographical coordinates of the short-wave target source.
Further, in step 2, an algebraic relation between the geographic coordinates of the nth short-wave correction source and the two-dimensional arrival direction of the radiation signal at the observation station is as follows:
Figure BDA0002328462270000031
Figure BDA0002328462270000032
in the formula
Figure BDA0002328462270000033
Figure BDA0002328462270000034
Figure BDA0002328462270000035
Wherein theta is (s) And beta (s) The longitude and the latitude of the observation station are respectively,
Figure BDA0002328462270000036
the longitude and latitude of the nth short wave correction source are respectively, H is the virtual height of the ionosphere, R is the radius of the earth,
Figure BDA0002328462270000037
respectively correcting azimuth angle and elevation angle, t, of source radiation signal arriving at observation station for nth short wave 1 、t 2 In order to convert the vector into a coordinate system,
Figure BDA0002328462270000038
is 1/2 of the geocentric angle between the short-wave single station and the short-wave target source.
Further, the step 3 comprises:
step 3.1: receiving and collecting the radiation signal of the nth short wave correction source by using a K-element uniform circular array, and collecting M signal sample points
Figure BDA0002328462270000041
And constructing a covariance matrix
Figure BDA0002328462270000042
Step 3.2: for covariance matrix
Figure BDA0002328462270000043
Singular value decomposition is carried out, singular values are arranged from large to small, and a matrix is constructed by utilizing left singular vectors corresponding to K-1 small singular values behind the singular values
Figure BDA0002328462270000044
Step 3.3: by passing
Figure BDA0002328462270000045
Constructing a spectrum function of short wave correction source geographic coordinate airspace position
Figure BDA0002328462270000046
Thereby deriving a spatial location spectrum with respect to the shortwave corrected source geographic coordinates, where b (θ, β) represents an array manifold vector as a function of the shortwave corrected source geographic coordinates.
Further, the step 5 comprises:
utilizing vectorization operator vec (-) to correct the spectrogram matrix corresponding to the nth short wave correction source
Figure BDA0002328462270000047
Conversion into vectors
Figure BDA0002328462270000048
And will be
Figure BDA0002328462270000049
As an input value to the radial basis function neural network, and then correcting the true longitude of the nth shortwave correction source
Figure BDA00023284622700000410
And latitude
Figure BDA00023284622700000411
As the output value of the radial basis function neural network, the self-organizing center selection method is adopted to carry out the radial basis function neural networkAnd (5) training the network.
Further, in step 6, an algebraic relation between the geographic coordinates of the short-wave target source and the two-dimensional arrival direction of the radiation signal at the observation station is as follows:
Figure BDA00023284622700000412
Figure BDA00023284622700000413
in the formula
Figure BDA00023284622700000414
Figure BDA0002328462270000051
Wherein theta is (e) And beta (e) Longitude and latitude of short wave target source respectively, alpha (e) 、γ (e) Respectively the azimuth angle and the elevation angle of the short wave target source radiation signal reaching the observation station.
Further, the step 7 includes:
step 7.1: receiving and collecting radiation signals of the nth short-wave correction source by using a K-element uniform circular array, and collecting M signal sample points { x (e) (t m )} 1≤m≤M And constructing a covariance matrix
Figure BDA0002328462270000052
Step 7.2: to covariance matrix
Figure BDA0002328462270000053
Singular value decomposition is carried out, singular values are arranged from large to small, and a matrix is constructed by using left singular vectors corresponding to K-1 small singular values behind the singular values
Figure BDA0002328462270000054
Step 7.3: by passing
Figure BDA0002328462270000055
Constructing a spectrum function of short wave correction source geographic coordinate airspace position
Figure BDA0002328462270000056
Thereby deriving a spatial location spectrum with respect to the short wave correction source geographic coordinates.
Further, the step 9 includes:
p obtained in step 8 is transformed by using vectorization operator vec (-) (e) Conversion to vector p (e) And inputting the data into the radial basis function neural network trained in the step 5, wherein the output value of the network is the final estimated value of the short-wave target source geographic coordinate, and the positioning deviation caused by the ionospheric virtual height error and the ionospheric inclination angle deviation is compensated through the estimated value.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a short wave single station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum, which trains a radial basis neural network by utilizing a short wave correction source geographical coordinate airspace position spectrum matrix near a short wave target source, and can effectively eliminate positioning deviation caused by ionospheric virtual height error and ionospheric inclination angle deviation based on the neural network, thereby greatly improving the precision of short wave single station direct positioning.
Drawings
FIG. 1 is a basic flow chart of a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace location spectrum according to an embodiment of the present invention;
FIG. 2 is a geographical coordinate spatial location spectrum of a short-wave single-station direct positioning deviation compensation method based on the geographical coordinate spatial location spectrum according to an embodiment of the present invention;
FIG. 3 is a direct positioning result distribution comparison diagram of the short-wave single-station direct positioning deviation compensation method based on the geographical coordinate spatial domain position spectrum according to the embodiment of the present invention;
FIG. 4 is a comparison graph of variation curves of short wave target source positioning root mean square error along with signal-to-noise ratio of the short wave single station direct positioning deviation compensation method based on the geographical coordinate airspace location spectrum of the embodiment of the present invention;
FIG. 5 is a comparison graph of short-wave target source positioning root mean square error variation curves with signal sample point numbers in a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate airspace location spectrum according to an embodiment of the present invention;
FIG. 6 is a comparison graph of variation curves of short-wave target source positioning root mean square error along with ionosphere virtual height error of the short-wave single-station direct positioning deviation compensation method based on the geographical coordinate airspace location spectrum of the present invention;
fig. 7 is a comparison graph of variation curves of short-wave target source positioning root mean square error along with ionosphere inclination angle deviation of the short-wave single-station direct positioning deviation compensation method based on the geographical coordinate spatial domain position spectrum of the embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
as shown in fig. 1, a short-wave single-station direct positioning deviation compensation method based on a geographical coordinate spatial domain position spectrum includes:
step S101: and N short wave correction sources with accurately known geographic coordinates are placed in a time-sharing mode near the airspace position where the short wave target source is located.
Step S102: and sequentially establishing an algebraic relation between the geographic coordinates of the N (1 is less than or equal to N and less than or equal to N) th shortwave correction source and the two-dimensional arrival direction of the radiation signal of the shortwave correction source reaching the observation station by using the geographic coordinates of the observation station and the known ionospheric virtual height information (containing errors).
Step S103: and sequentially aiming at the N (N is more than or equal to 1 and less than or equal to N) th shortwave correction source, receiving and collecting the radiation signal by using a K-element uniform circular array, and obtaining a space domain position spectrum of the shortwave correction source in terms of the geographic coordinates by using a subspace method.
Step S104: and sequentially aiming at the N (N is more than or equal to 1 and less than or equal to N) th short-wave correction source, gridding the region near the main peak of the geographical coordinate spatial domain position spectrum according to a certain step length, and constructing a geographical coordinate spatial domain position spectrum matrix of the short-wave correction source by using the spectrum value of each grid.
Step S105: and training the radial basis function neural network by using the geographic coordinate spatial position spectrum matrix of the N short wave correction sources and the real geographic coordinates of the N short wave correction sources.
Step S106: and establishing an algebraic relation between the geographical coordinates of the short-wave target source and the two-dimensional arrival direction of the radiation signal of the short-wave target source to the observation station by using the geographical coordinates of the observation station and the ionosphere virtual height information.
Step S107: and aiming at the short wave target source, receiving and collecting a radiation signal of the short wave target source by using a K-element uniform circular array, and obtaining a spatial domain position spectrum of the short wave target source by using a subspace method.
Step S108: and (5) gridding the region near the main peak of the geographical coordinate spatial domain position spectrum of the short wave target source according to the step size in the step S104, and constructing a geographical coordinate spatial domain position spectrum matrix of the short wave target source by using the spectrum value of each grid.
Step S109: and (5) converting the spatial domain position spectrum matrix of the geographical coordinates obtained in the step (S108) into vectors by using a vectorization operator, and inputting the vectors into the radial basis function neural network trained in the step (S105), wherein the output value of the network is the final estimation value of the geographical coordinates of the short-wave target source.
Specifically, in the step S101, N short-wave correction sources with precisely known geographic coordinates are placed in a time-sharing manner near the airspace position where the short-wave target source is located, wherein the longitude of the N (1 ≦ N ≦ N) short-wave correction source is
Figure BDA0002328462270000071
Latitude of
Figure BDA0002328462270000072
Specifically, in the step S102, it is assumed that the latitudes and longitudes of the observation stations are θ respectively (s) And beta (s) The ionospheric virtual height is H, the earth radius is R, the nth short-wave correction source radiation signal reaches the observation station (N is more than or equal to 1 and less than or equal to N)Respectively in azimuth and elevation of
Figure BDA0002328462270000073
And
Figure BDA0002328462270000074
from the geometrical relationship of signal propagation, the following algebraic relation can be established:
Figure BDA0002328462270000075
Figure BDA0002328462270000076
in the formula
Figure BDA0002328462270000081
Figure BDA0002328462270000082
Figure BDA0002328462270000083
Wherein, t 1 、t 2 In order to convert the vector into a coordinate system,
Figure BDA0002328462270000084
1/2 g () of the geocentric angle between the short-wave single station and the short-wave target source is an intermediate parameter.
Specifically, in step S103, sequentially aiming at the nth (N is greater than or equal to 1 and less than or equal to N) shortwave correction source, the radiation signal is received and collected by using a K-ary uniform circular array, and an array received signal model is as follows:
Figure BDA0002328462270000085
in the formula
Figure BDA0002328462270000086
Representing the received signal of the uniform circular array for the nth short-wave correction source;
Figure BDA0002328462270000087
representing a complex envelope of an nth shortwave corrected source radiation signal;
Figure BDA0002328462270000088
representing array additive noise;
Figure BDA0002328462270000089
representing an array manifold vector taking the two-dimensional direction of arrival of the short-wave correction source radiation signal as a function;
Figure BDA00023284622700000810
representing array manifold vectors as a function of shortwave corrected source geographic coordinates
Figure BDA00023284622700000811
And then obtaining a space domain position spectrum of the shortwave correction source geographic coordinates by using a subspace method, wherein the calculation process is as follows:
(1) Acquiring M signal sample points
Figure BDA00023284622700000812
And constructing a covariance matrix
Figure BDA00023284622700000813
(2) For covariance matrix
Figure BDA00023284622700000814
Singular value decomposition is carried out, singular values are arranged from large to small, and a matrix is constructed by using left singular vectors corresponding to K-1 small singular values behind the singular values
Figure BDA0002328462270000091
(3) Constructing a spectrum function of short wave correction source geographic coordinate airspace position
Figure BDA0002328462270000092
Thereby deriving a spatial location spectrum with respect to the short wave correction source geographic coordinates.
Specifically, in the step S104, sequentially aiming at the N (1 ≦ N ≦ N) th shortwave correction source, the region near the main peak of the geographical coordinate spatial domain position spectrum is gridded according to a certain step length, as shown in FIG. 2, and the geographical coordinate spatial domain position spectrum matrix of the shortwave correction source is constructed by using the spectrum value of each grid
Figure BDA0002328462270000093
In order to reduce the complexity of the operation, the step size can be properly widened without being too fine.
Specifically, in step S105, a vectorization operator vec (-) is used to map a spectrogram matrix corresponding to the nth (1 ≦ N) shortwave correction source
Figure BDA0002328462270000094
Conversion into vectors
Figure BDA0002328462270000095
(namely have
Figure BDA0002328462270000096
) Taking the real longitude and latitude of the nth short wave correction source as an input value of the radial basis function neural network
Figure BDA0002328462270000097
And
Figure BDA0002328462270000098
as the output value of the radial basis function neural network, N groups of input-output pairs are in total, the radial basis function neural network is trained by using the input-output pairs, and a learning algorithm adopts a self-organizing center selection method. Using pairs of radial basis function neural networksAfter the learning sample is trained, the network has an automatic compensation function for the short-wave single-station direct positioning deviation, and can effectively make up for the influence caused by ionospheric pseudo-height errors and ionospheric inclination angle deviations.
Specifically, in the step S106, it is assumed that the longitude and latitude of the short-wave target source is θ (e) And beta (e) The azimuth angle and the elevation angle of the radiation signal reaching the observation station are respectively alpha (e) And gamma (e) From the geometrical relationship of signal propagation, the following algebraic relation can be established:
Figure BDA0002328462270000099
Figure BDA00023284622700000910
in the formula
Figure BDA00023284622700000911
Figure BDA0002328462270000101
Specifically, in step S107, for the short-wave target source, the radiation signal is received and collected by using a K-ary uniform circular array, where the model of the array received signal is
x (e) (t)＝a(α (e) ,γ (e) )s (e) (t)+ξ (e) (t)＝b(θ (e) ,β (e) )s (e) (t)+ξ (e) (t)
In the formula x (e) (t) represents the received signal of the uniform circular array for the short wave target source; s is (e) (t) represents the complex envelope of the short wave target source radiation signal; xi (e) (t) represents array additive noise; a (alpha) (e) ,γ (e) ) Representing two-dimensional direction of arrival of radiation signals from short-wave target sources as a functionAn array manifold vector; b (theta) (e) ,β (e) ) Representing the manifold vector of the array as a function of the geographic coordinates of the short-wave target source, which satisfies b (theta) (e) ,β (e) )＝a(α (e) ,γ (e) )。
And then obtaining a spatial position spectrum of the geographic coordinates of the short wave target source by using a subspace method, wherein the calculation process is as follows:
(1) Acquiring M signal sample points { x (e) (t m )} 1≤m≤M And constructing a covariance matrix
Figure BDA0002328462270000102
(2) For covariance matrix
Figure BDA0002328462270000103
Singular value decomposition is carried out, singular values are arranged from large to small, and a matrix is constructed by using left singular vectors corresponding to K-1 small singular values behind the singular values
Figure BDA0002328462270000104
(3) Constructing a spatial domain position spectrum function of short wave target source geographic coordinates
Figure BDA0002328462270000105
Thereby deriving a spatial location spectrum with respect to the short wave correction source geographic coordinates.
Specifically, in step S108, for the short wave target source, the region near the main peak of the geographical coordinate spatial location spectrum is gridded according to a certain step length, and a geographical coordinate spatial location spectrum matrix P about the short wave target source is constructed by using the spectrum value of each grid (e) . The area range and step size selected here need to be exactly the same as step S104.
Specifically, in the step S109, the geographic coordinate spatial domain position spectrum matrix P obtained in the step S108 is transformed by using the vectorization operator vec (·) (e) Conversion to vectors (i.e. with p) (e) ＝vec(P (e) ) And input into the radial basis function neural network trained in step S105, the netThe output value of the network is the final estimated value of the short wave target source geographic coordinate, and the estimated value can compensate the positioning deviation caused by the ionospheric pseudo-height error and the ionospheric inclination angle deviation.
The longitude of the observation station is assumed to be 112.73 degrees of east longitude, and the latitude is 33.25 degrees of north latitude; the longitude of the short wave target source is 125.52 degrees of east longitude, and the latitude is 28.12 degrees of north latitude. The observation station is provided with a 10-element uniform circular array, the radius-to-wavelength ratio of the array is 1.5, and the virtual height of an ionized layer, which is experienced when a short-wave target source radiation signal reaches the observation station, is 350 kilometers.
(1) The signal-to-noise ratio of the short-wave target source radiation signal is 10dB, the number of signal sample points adopted by the algorithm is 500, the ionosphere virtual height error is 50 kilometers, the ionosphere inclination angle deviation is 0.5 degrees, and a positioning result scatter diagram is given in figure 3. As can be seen from FIG. 3, the short-wave single-station direct positioning deviation compensation method based on the geographical coordinate airspace position spectrum disclosed by the patent can obviously eliminate the influence caused by ionospheric pseudo-height errors and ionospheric inclination angle deviations, thereby obviously improving the positioning accuracy of the short-wave radiation source.
(2) Other experimental conditions are unchanged, and fig. 4 and 5 respectively show the variation curves of the short-wave target source positioning root mean square error along with the signal-to-noise ratio and the number of signal sample points, so that the positioning accuracy of the method disclosed by the patent is greatly improved, and along with the improvement of the signal-to-noise ratio and the number of signal sample points, the positioning accuracy is gradually improved.
(3) The other experimental conditions are unchanged, and fig. 6 and 7 respectively show the variation curves of short-wave target source positioning root mean square error along with ionospheric virtual height error and ionospheric inclination angle deviation, so that the method disclosed by the patent is slightly influenced by the ionospheric virtual height error and the ionospheric inclination angle deviation, because the method trains a radial basis function neural network by using a short-wave correction source geographical coordinate airspace position spectrum, the positioning deviation generated by the ionospheric virtual height error and the ionospheric inclination angle deviation can be effectively compensated, and the influence cannot be eliminated by a traditional direct positioning method, so that the positioning error shows a linear growth trend.
The above shows only the preferred embodiments of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.

Claims (7)

1. A short wave single station direct positioning deviation compensation method based on a geographical coordinate airspace position spectrum is characterized by comprising the following steps:
step 1: n short wave correction sources with accurately known geographic coordinates are placed in a region where the airspace position where the short wave target source is located in a time-sharing mode;
step 2: establishing an algebraic relation between the geographic coordinate of the nth short-wave correction source and the two-dimensional arrival direction of the radiation signal of the nth short-wave correction source, wherein N is more than or equal to 1 and less than or equal to N, by utilizing the geographic coordinate of the observation station and the known ionosphere virtual height information;
and step 3: aiming at the nth short wave correction source, receiving and collecting a radiation signal of the nth short wave correction source by using a K-element uniform circular array, and obtaining a geographical coordinate airspace position spectrum of the nth short wave correction source by using a subspace method;
and 4, step 4: aiming at the nth short wave correction source, gridding the region where the main peak of the geographical coordinate airspace position spectrum of the nth short wave correction source is located according to a certain step length, and constructing a geographical coordinate airspace position spectrum matrix of the nth short wave correction source by using the spectrum value of each grid
Figure FDA0002328462260000011
And 5: training a radial basis function neural network by using a geographical coordinate airspace position spectrum matrix of N short-wave correction sources and corresponding real geographical coordinates;
step 6: establishing an algebraic relation between the geographical coordinates of the short-wave target source and the two-dimensional arrival direction of the radiation signal of the short-wave target source reaching the observation station by using the geographical coordinates of the observation station and the ionosphere virtual height information;
and 7: aiming at a short wave target source, a K-element uniform circular array is utilized to receive and collect a radiation signal of the short wave target source, and a subspace method is utilized to obtain a spatial domain position spectrum of the short wave target source in terms of geographic coordinates;
and 8: aiming at the short wave target source, gridding the region near the main peak of the geographical coordinate spatial domain position spectrum of the short wave target source according to the step length in the step 4, and constructing a geographical coordinate spatial domain position spectrum matrix P of the short wave target source by using the spectrum value of each grid (e) ；
And step 9: converting the spatial domain position spectrum matrix of the geographical coordinates obtained in the step 8 into vectors by using a vectorization operator, and inputting the vectors into the radial basis function neural network trained in the step 5, wherein the output value of the network is the final estimation value of the geographical coordinates of the short-wave target source.
2. The short-wave single-station direct positioning deviation compensation method based on geographical coordinate spatial domain position spectrum of claim 1, wherein the algebraic relation between the geographical coordinates of the nth short-wave correction source and the two-dimensional arrival direction of its radiation signal at the observation station in step 2 is as follows:
Figure FDA0002328462260000021
Figure FDA0002328462260000022
in the formula
Figure FDA0002328462260000023
Figure FDA0002328462260000024
Figure FDA0002328462260000025
Wherein theta is (s) And beta (s) The longitude and the latitude of the observation station are respectively,
Figure FDA0002328462260000026
the longitude and latitude of the nth short wave correction source are respectively, H is the virtual height of the ionosphere, R is the radius of the earth,
Figure FDA0002328462260000027
respectively correcting azimuth angle and elevation angle, t, of source radiation signal arriving at observation station for nth short wave 1 、t 2 In order to convert the vector into a coordinate system,
Figure FDA0002328462260000028
is 1/2 of the geocentric angle between the short-wave single station and the short-wave target source.
3. The short-wave single-station direct positioning deviation compensation method based on the geographical coordinate spatial domain position spectrum according to claim 2, wherein the step 3 comprises the following steps:
step 3.1: receiving and collecting the radiation signal of the nth short wave correction source by using a K-element uniform circular array, and collecting M signal sample points
Figure FDA0002328462260000029
And constructing a covariance matrix
Figure FDA00023284622600000210
Step 3.2: to covariance matrix
Figure FDA00023284622600000211
Singular value decomposition is carried out, and a matrix is constructed by utilizing left singular vectors corresponding to K-1 small singular values behind the singular value decomposition
Figure FDA00023284622600000212
Step 3.3: by passing
Figure FDA00023284622600000213
Constructing a spectrum function of short wave correction source geographic coordinate airspace position
Figure FDA0002328462260000031
Thereby deriving a spatial location spectrum with respect to the shortwave corrected source geographic coordinates, where b (θ, β) represents an array manifold vector as a function of the shortwave corrected source geographic coordinates.
4. The short-wave single-station direct positioning deviation compensation method based on geographical coordinate spatial domain position spectrum according to claim 2, wherein the step 5 comprises:
utilizing vectorization operator vec (-) to correct the spectrogram matrix corresponding to the nth short-wave correction source
Figure FDA0002328462260000032
Conversion into vectors
Figure FDA0002328462260000033
And will be
Figure FDA0002328462260000034
As an input value to the radial basis function neural network, and then correcting the true longitude of the nth shortwave correction source
Figure FDA0002328462260000035
And latitude
Figure FDA0002328462260000036
And as an output value of the radial basis function neural network, training the radial basis function neural network by adopting a self-organizing center selection method.
5. The short-wave single-station direct positioning deviation compensation method based on geographical coordinate spatial domain position spectrum of claim 3, wherein the algebraic relation between the geographical coordinates of the short-wave target source and the two-dimensional arrival direction of its radiation signal to the observation station in step 6 is as follows:
Figure FDA0002328462260000037
Figure FDA0002328462260000038
in the formula
Figure FDA0002328462260000039
Figure FDA00023284622600000310
Wherein theta is (e) And beta (e) Longitude and latitude of short wave target source respectively, alpha (e) 、γ (e) Respectively the azimuth angle and the elevation angle of the short wave target source radiation signal reaching the observation station.
6. The short wave single station direct positioning deviation compensation method based on geographical coordinate spatial domain position spectrum according to claim 5, wherein said step 7 comprises:
step 7.1: receiving and collecting radiation signals of the nth short-wave correction source by using a K-element uniform circular array, and collecting M signal sample points { x (e) (t m )} 1≤m≤M And constructing a covariance matrix
Figure FDA0002328462260000041
Step 7.2: for covariance matrix
Figure FDA0002328462260000042
Singular value decomposition is carried out, singular values are arranged from large to small, and a matrix is constructed by using left singular vectors corresponding to K-1 small singular values behind the singular values
Figure FDA0002328462260000043
Step 7.3: by passing
Figure FDA0002328462260000044
Constructing a spectrum function of short wave correction source geographic coordinate airspace position
Figure FDA0002328462260000045
Thereby deriving a spatial location spectrum with respect to the short wave correction source geographic coordinates.
7. The short-wave single-station direct positioning deviation compensation method based on geographical coordinate spatial domain position spectrum according to claim 6, wherein said step 9 comprises:
using vectorization operator vec (-) to apply P obtained in step 8 (e) Conversion to vector p (e) And inputting the data into the radial basis function neural network trained in the step 5, wherein the output value of the network is the final estimation value of the short-wave target source geographic coordinates, and the positioning deviation caused by the ionospheric virtual height error and the ionospheric inclination angle deviation is compensated through the estimation value.
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