CN112014813B - Method and system for correcting ionosphere pollution of sky wave radar - Google Patents
Method and system for correcting ionosphere pollution of sky wave radar Download PDFInfo
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Abstract
The invention relates to a sky wave radar ionosphere pollution correction method and system. According to the method, spline functions are selected as kernel functions of parameterized time-frequency transformation according to the characteristics of ionosphere pollution, kernel function parameters are estimated for signals after the ionosphere pollution, a pollution sequence is estimated by using the kernel functions, and pollution correction is performed. And for ionosphere pollution under the multimode propagation condition, adopting a mode of filtering after correction, and performing iteration for many times to finish the correction of the pollution. The sky wave radar ionosphere pollution correction method and system provided by the invention can be suitable for pollution under multimode conditions, and can improve correction accuracy.
Description
Technical Field
The invention relates to the field of sky wave radar ionosphere pollution correction, in particular to a method and a system for correcting sky wave radar ionosphere pollution.
Background
The sky wave beyond visual range radar works in a short wave band (3-30 MHz), and utilizes the reflection effect of an ionosphere on short wave electromagnetic waves to finish beyond visual range detection of air or sea/ground targets in thousands of centimeters. Due to its "look down" mode of operation, sea/ground clutter interference is severe when detecting sea/ground targets. The sea/ground target motion speed is slow, and to detect targets from strong sea/ground clutter, a long accumulation time is required to obtain sufficient doppler resolution, and clutter and targets are separated in the doppler domain. For sea/ground target detection, the typical coherent integration time is 10-40s. In this time frame, the ionosphere channel is undulating. The fluctuation of the ionosphere causes fluctuation of the phases of the short wave electric wave signals passing through the ionosphere, so that the coherence among pulses is destroyed, the Doppler frequency spectrum of clutter and targets is widened, and the detection of the sky wave radar sea/ground targets is limited.
Although the sky wave radar can work at a frequency point with relatively small fluctuation under the support of the self-adaptive channel management subsystem, the problem of ionosphere pollution can be relieved to a certain extent, but the pollution cannot be completely avoided. To ensure successful target detection, ionospheric contamination correction is required. The most straightforward method for ionospheric pollution estimation and correction is to arrange transponders in the observation area, which in practice cannot be erected in all observation areas, subject to environmental conditions. For this reason, one uses strong sea/ground clutter information to estimate and correct ionospheric pollution. From the Doppler domain, the ground clutter is mainly concentrated near zero Doppler and can be used for correcting ionosphere pollution, and the sea clutter consists of a pair of first-order Bragg peaks and a plurality of second-order Bragg peaks. The position radar transmitting frequency of the sea clutter Bragg peak, the ionosphere state, the geometrical relation between the sea state and the transceiver station and other factors are related, and the first-order Bragg peak is strong, so that the method can be used for estimating and correcting ionosphere pollution.
The ionosphere phase pollution of the sky wave radar is presented as a interference in a slow time domain, the change of the ionosphere phase pollution is nonlinear, the ionosphere phase pollution cannot be filtered by adopting a conventional time-frequency filter, and in addition, the ionosphere channel has multiple layers, so that the ionosphere channel has different pollution layers and is overlapped at a receiving end, so that the pollution is more complex. For this reason, various ionospheric pollution correction methods have been proposed, which can be basically divided into three steps: 1. extracting characteristic signals for ionosphere pollution correction, which are generally carried out in a frequency domain, and filtering out Doppler spectrum peaks after single-frequency component broadening by adopting band-pass filtering; 2. extracting a pollution signal; 3. and constructing a correction operator to correct pollution. The current research mainly focuses on the second step, the distinction is mainly realized in the second step, and the implementation modes are different, namely, the method mainly comprises the following two types, namely, the method utilizes the relation between frequency and phase to directly estimate instantaneous frequency, and integrates to obtain a pollution phase sequence, such as a phase gradient method, a WVD-based method and the like; secondly, segmenting the data, estimating the instantaneous frequency or phase of each segment, and synthesizing a phase pollution sequence, such as a maximum entropy spectrum method, an estimation method based on singular value decomposition, a segmentation polynomial phase modeling method and the like.
From the specific principles of various pollution correction methods, the phase gradient method, the WVD-based method and the piecewise polynomial phase modeling method require the extraction of a doppler spectrum peak with a single frequency component spread. The phase gradient method is to estimate the pollution signal by calculating the phase gradient in sequence based on the phase of the first data of the signal sequence, and the accuracy is higher only in single-frequency signals. The WVD-based method is a second-order time-frequency method, when the signal to be analyzed is multi-component, a cross term appears, so that the calculation accuracy is reduced sharply, and the segmentation polynomial phase modeling method is also used for estimating polynomial coefficients by modeling the segmented signal phases of the single-component signal through polynomials, so that the pollution estimation and correction are completed by combining multiple segments. When the extracted signal is not a single component signal, the performance of the above algorithms is significantly compromised. The characteristic signals are extracted in Doppler domain, the characteristic signals are extracted accurately without introducing other interference, the clutter filter bandwidth is carefully selected, a clutter filter bandwidth self-adaptive selection algorithm based on minimum entropy is provided for extracting the characteristic signals accurately, li Xue, but the premise is that the frequency spectrums are not overlapped after a plurality of components are spread, and the searching is needed by matching with a pollution correction algorithm, so that the operation amount is large. The difficulty in accurately extracting the characteristic signals causes difficulty in ensuring the correction effect of the pollution algorithm of the single component analysis class.
The characteristic signals can be not extracted in advance for a maximum entropy spectrum method, an estimation method based on singular value decomposition and the like, the maximum entropy spectrum method is used for carrying out spectrum analysis on signals of each section by using a modified Burg method after segmenting the signals to be polluted, extracting the spectrum peak of the maximum component, combining the spectrum peak points of each section and carrying out interpolation calculation to estimate and correct pollution, but how to select the AR model order of spectrum estimation is a problem to be solved. The singular value decomposition-based method is to construct a Hankel matrix by using the extracted characteristic signals to perform singular value decomposition by utilizing invariance of pollution frequency in a short time, and to perform rank reduction by retaining large singular values, so as to inhibit noise components, and further to realize extraction and correction of pollution signals after matrix blocking, characteristic decomposition and other operations.
In addition, since the doppler component of the received signal increases in the multimode situation, the current pollution correction method is not applicable to the single mode propagation situation and to the pollution in the multimode situation. The key problem of ionosphere pollution correction is analysis of a polluted signal time-frequency structure, and the difficulty is that the analysis of the polluted signal time-frequency structure is mutually influenced by the multi-component signal, so that accurate analysis is difficult.
Disclosure of Invention
The invention aims to provide a sky wave radar ionosphere pollution correction method and system, which aim at solving the problem that the existing pollution correction method aims at single-mode propagation, and the ionosphere pollution under the multimode propagation condition is corrected by adopting a mode of filtering after correction, so that the pollution correction is completed through multiple iterations. The sky wave radar ionosphere pollution correction method and system provided by the invention can be suitable for pollution under multimode conditions, and can improve correction accuracy.
In order to achieve the above object, the present invention provides the following solutions:
a sky wave radar ionosphere pollution correction method comprises the following steps:
acquiring signals to be corrected of a slow time domain of the sky wave radar after ionosphere pollution;
selecting a spline function as a kernel function, initializing the kernel function, and determining an initialized kernel function;
based on the initialized kernel function, performing generalized parameterized time-frequency transformation on the signal to be corrected, and determining time-frequency distribution of the signal to be corrected;
determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected;
performing curve fitting on the time-frequency ridge position by using a spline function, and taking the fitted curve as a kernel function of next transformation of the signal to be corrected;
Judging whether iteration is terminated or not based on the kernel function of the next transformation and the initialized kernel function, and obtaining a first judgment result;
if the first judgment result indicates that iteration is terminated, determining an estimated value of the instantaneous frequency; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process;
determining a pollution estimated value according to the estimated value of the instantaneous frequency;
determining a pollution-removing signal according to the pollution estimated value; the pollution-removing signal is a corrected ionospheric pollution signal;
if the first judgment result indicates that iteration is not terminated, taking the kernel function of the next transformation as an initialized kernel function, and returning to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function to determine the time-frequency distribution of the signal to be corrected.
Optionally, the performing generalized parametric time-frequency transformation on the signal to be corrected based on the initialized kernel function, and determining the time-frequency distribution of the signal to be corrected specifically includes:
the time-frequency distribution of the signal to be corrected is as follows:
TF(t 0 ,ω；P i ) At t 0 Time-frequency distribution of signals to be corrected at the moment; To transform the kernel parameter vector P in the ith iteration i The determined kernel function, i is the iteration number; t is t 0 Is the initial time; τ is an integral variable; x (τ) is the signal to be corrected; j is an imaginary unit; />Is an initialized kernel function; w (w) σ (t) represents a window function determined by the parameter σ; ω is the angular frequency.
Optionally, the determining, based on the time-frequency distribution, a time-frequency ridge position of the signal to be corrected specifically includes:
the time-frequency ridge positions are as follows:
wherein,the time-frequency ridge position of the ith signal to be corrected; TF (TF) x (t,ω；P i ) The time-frequency distribution of the signal to be corrected at the time t.
Optionally, the determining whether the iteration is terminated based on the kernel function of the next transformation and the initialized kernel function specifically includes:
the termination conditions are:
wherein,the time-frequency ridge position of the i-1 th signal to be corrected; ζ is a positive number below a first threshold; i.e max Is the maximum number of iterations.
Optionally, the determining a pollution estimation value according to the estimation value of the instantaneous frequency specifically includes:
acquiring fixed frequencies of clutter;
according to the formulaDetermining a pollution estimated value; wherein (1)>Is a pollution estimated value; IF (IF) l (t) is an estimated value of the instantaneous frequency, l is the iteration number, l ε i; f (f) B Is a fixed frequency of spurs.
Optionally, the determining the pollution-removing signal according to the pollution estimated value further includes:
ending the correction according to the correction ending condition;
the correction end condition is:
wherein x is l (t) is the signal to be corrected corresponding to the estimated value of the instantaneous frequencyThe method comprises the steps of carrying out a first treatment on the surface of the x (t) is the initial signal to be corrected; epsilon is a positive number below the second threshold.
A sky wave radar ionospheric pollution correction system comprising:
the signal to be corrected acquisition module is used for acquiring signals to be corrected of the sky wave radar slow time domain after ionosphere pollution;
the initialization module is used for selecting a spline function as a kernel function, initializing the kernel function and determining an initialized kernel function;
the time-frequency distribution determining module is used for performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function and determining the time-frequency distribution of the signal to be corrected;
the time-frequency ridge position determining module is used for determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected;
the fitting module is used for performing curve fitting on the time-frequency ridge position by utilizing a spline function, and taking the fitted curve as a kernel function of next transformation of the signal to be corrected;
The first judging module is used for judging whether iteration is ended or not based on the kernel function of the next transformation and the initialized kernel function to obtain a first judging result;
the estimation value determining module of the instantaneous frequency is used for determining the estimation value of the instantaneous frequency if the first judging result indicates that iteration is terminated; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process;
the pollution estimated value determining module is used for determining a pollution estimated value according to the estimated value of the instantaneous frequency;
the pollution-removing signal determining module is used for determining a pollution-removing signal according to the pollution estimated value; the pollution-removing signal is a corrected ionospheric pollution signal;
and the iteration module is used for taking the kernel function of the next transformation as an initialized kernel function if the first judgment result shows that iteration is not terminated, returning to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function, and determining the time-frequency distribution of the signal to be corrected.
Optionally, the time-frequency distribution of the signal to be corrected in the time-frequency distribution determining module is:
TF(t 0 ,ω；P i ) At t 0 Time-frequency distribution of signals to be corrected at the moment;to transform the kernel parameter vector P in the ith iteration i The determined kernel function, i is the iteration number; t is t 0 Is the initial time; τ is an integral variable; x (τ) is the signal to be corrected; j is an imaginary unit; />Is an initialized kernel function; w (w) σ (t) represents a window function determined by the parameter σ; ω is the angular frequency.
Optionally, the time-frequency ridge position in the time-frequency ridge position determining module is:
wherein,the time-frequency ridge position of the ith signal to be corrected; TF (TF) x (t,ω；P i ) The time-frequency distribution of the signal to be corrected at the time t.
Optionally, the termination condition in the first judging module is:
wherein,the time-frequency ridge position of the i-1 th signal to be corrected; ζ is a positive number below a first threshold; i.e max Is the maximum number of iterations.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a sky wave radar ionosphere pollution correction method and system, which are characterized in that spline functions are selected as kernel functions of parameterized time-frequency transformation according to the characteristics of ionosphere pollution, kernel function parameters are estimated for signals after the ionosphere pollution, and pollution sequences are estimated by using the kernel functions to carry out pollution correction. And for ionosphere pollution under the multimode propagation condition, a mode of filtering after correction is adopted, so that the correction of the pollution is completed through multiple iterations, and the correction precision is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for correcting ionospheric pollution of a sky wave radar provided by the invention;
FIG. 2 is a simplified ionospheric pollution correction flow chart provided by the present invention;
FIG. 3 is a block diagram of a sky wave radar ionosphere pollution correction system provided by the invention;
fig. 4 is a graph showing ionospheric pollution estimation and correction effects in the case of small-amplitude pollution (b=1) provided by the present invention; fig. 4 (a) is a pollution phase estimation diagram in the case of small-amplitude pollution (b=1), and fig. 4 (B) is a pollution correction result diagram in the case of small-amplitude pollution (b=1);
fig. 5 is a graph showing ionospheric pollution estimation and correction effects in the case of a large scale pollution (b=5) provided by the present invention; fig. 5 (a) is a pollution phase estimation diagram, and fig. 5 (b) is a pollution correction result diagram;
FIG. 6 is a graph of absolute error of pollution estimation under different signal-to-noise ratios, pollution amplitudes and frequencies provided by the present invention; FIG. 6 (a) is a graph showing the influence of the signal-to-noise ratio on the absolute error, and FIG. 6 (b) is a graph showing the influence of the contamination amplitude on the absolute error; FIG. 6 (c) is a graph showing the effect of contamination frequency on absolute error;
FIG. 7 is a graph showing ionospheric pollution estimation and correction effects under simulated sea clutter provided by the invention; fig. 7 (a) is a graph of pollution phase estimation under simulated sea clutter, and fig. 7 (b) is a graph of pollution correction result under simulated sea clutter;
FIG. 8 is a graph of absolute error of pollution estimation under different noise ratios, pollution amplitudes and frequencies provided by the present invention; FIG. 8 (a) is a graph showing the effect of the noise ratio on the absolute error under the simulation of sea clutter, FIG. 8 (b) is a graph showing the effect of the pollution amplitude on the absolute error under the simulation of sea clutter, and FIG. 8 (c) is a graph showing the effect of the pollution frequency on the absolute error under the simulation of sea clutter;
FIG. 9 is a graph of ionospheric pollution correction under multimode conditions provided by the present invention; fig. 9 (a) is a graph comparing the original signal with the pollution signal, and fig. 9 (b) is a graph showing the result of the pollution correction under multimode conditions.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a sky wave radar ionosphere pollution correction method and system, which can be suitable for pollution under multimode conditions and improve correction precision.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method for correcting ionospheric pollution of a sky wave radar according to the present invention, as shown in fig. 1, a method for correcting ionospheric pollution of a sky wave radar includes:
step 101: and acquiring signals to be corrected of the sky wave radar slow time domain after ionosphere pollution.
In view of the multilayering of the ionosphere, the echo signal of the slow time domain of the sky-wave radar during the coherent accumulation time can be expressed as:
where t=1, …, T denotes the slow time series of echo processing, c (T) denotes clutter echo, n (T) denotes receive noise,a pollution signal representing the M (m=1, …, M) th ionosphere propagation mode, +.>The phase fluctuations of the corresponding propagation modes are represented, and M represents the number of ionospheric propagation modes.
Phase fluctuation caused by ionosphere pollutionThe method can be regarded as linear change in a short time, only spectrum deviation is caused at the moment, target detection is not affected, phase pollution is changed in a nonlinear manner when coherent accumulation time is long, for example, 10-40s required by sea surface target detection is reached, coherence between pulses is destroyed, and target and clutter Doppler spectrum expansion possibly causes target detection to be impossible. The ionospheric pollution is corrected by estimating the amount of phase fluctuation, noted as Constructing the corresponding decontamination sequence->The corresponding components are multiplied to complete the correction of pollution.
Step 102: and selecting a spline function as a kernel function, initializing the kernel function, and determining the initialized kernel function.
Step 103: and carrying out generalized parametric time-frequency transformation on the signal to be corrected based on the initialized kernel function, and determining the time-frequency distribution of the signal to be corrected.
The generalized parameterized time-frequency transform is defined as follows:
wherein the method comprises the steps ofP represents a transform kernel parameter vector, ">Representing a frequency rotation operator associated with the kernel function, < +.>Representing t 0 Frequency translation operator, k, defined by parameter P for time period around moment p (t)∈L 2 (R) represents an integrable function, w σ (t) represents a window function determined by the parameter sigma.
For non-stationary signals, the function of the frequency rotation operator is to at t 0 Rotating the time-frequency ridge of the signal within a time window around the moment, i.e. subtracting k from the instantaneous frequency of the signal p (t) frequency shift operator is to shift the frequency spectrum of the signal in the time window, the shifted frequency scale is k p (t 0 ). From the definition of the transformation, the transformation is to make the original signal x (t) be at t 0 A section near the moment is subjected to Fourier transformation after rotation, frequency shift and windowing, and the frequency resolution after transformation is determined by the window width and a kernel function k p (t) It is determined that when the difference between the kernel function and the time-frequency distribution of the original signal is constant, the frequency resolution of the transform is determined only by the window width, which is the best time-frequency concentration. When k is p When (t) =0, the transform is degraded into a short-time fourier transform.
Step 104: determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; and the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected.
Step 105: and performing curve fitting on the time-frequency ridge position by using a spline function, and taking the fitted curve as a kernel function of next transformation of the signal to be corrected.
Step 106: based on the kernel function of the next transformation and the initialized kernel function, it is determined whether iteration is terminated, if yes, step 107 is executed, and if not, step 110 is executed.
From the definition formula of generalized parameterized time-frequency transformation, it can be seen that the more similar the transformation kernel function is to the time-frequency characteristics of the signal, the more concentrated the time-frequency distribution of the signal after generalized parameterized time-frequency transformation is, and the more accurate the time-frequency characteristics of the signal are depicted. To make the time-frequency concentration of the transformation best, a suitable kernel function and kernel function parameters are selected according to the characteristics of the signal, so that the difference between the kernel function and the time-frequency distribution of the original signal is constant. The kernel function is selected and its parameters are set so that it can better approximate the time-frequency characteristics of the signal to be decomposed.
According to the difference of the time-frequency distribution characteristics of the signals, three forms of kernel functions are respectively given: polynomial functions, spline functions and Fourier series, and provides a corresponding kernel function parameter calculation method. For the sky wave radar received signal after ionosphere pollution, the time-frequency curve change of the received signal is complex due to the randomness of the phase change, if a polynomial function is adopted, a high-order polynomial is needed, and a 'Dragon' phenomenon can appear in the high-order polynomial in fitting, so that the numerical value is unstable, spline functions or Fourier series are adopted, the Fourier series better approximates to the pollution signal with random fluctuation, a longer series is needed, and the parameters to be estimated are more, so that spline functions are adopted.
The kernel function parameters are estimated according to the time-frequency characteristics of the signals to be converted, the accuracy of the time-frequency characteristics of the signals to be converted is related to the value of the kernel function parameters, the more proper the value is, the more centralized the time-frequency distribution after conversion is, the more accurate the time-frequency characteristics are, the more proper the estimated parameters are, and the process of repeating the above steps to form a cyclic approximation process is finally carried out, so that the kernel function parameters are finally obtained. Taking the single component signal as an example, the estimation of the kernel parameters can be summarized as:
(1) In the i (i=1, 2, …, i max ) In the iteration, the generalized parameterized time-frequency transformation is adopted to obtain the time-frequency distribution of the signal, namely
Wherein the method comprises the steps ofRepresenting the parameter P in the ith iteration i The determined transformation kernel. Initial transformation can make kernel function k p (t) =0, i.e. using a short-time fourier transform to obtain an initial time-frequency distribution;
(2) Peak detection of frequency along time axis in time-frequency distribution can obtain time-frequency ridge position of signal, and uses it as estimated value of time-frequency characteristic of signal, i.e
(3) By spline function pairsPerforming curve fitting, and recording the parameters obtained by the fitting as +.>Fitting the resulting curve as a kernel function of the (i+1) -th transformation>
(4) Judging iteration to beIf not, the criterion isWherein ζ is a small positive number. When i=1, _a->If the criterion is not satisfied, turning to (1), and if the criterion is satisfied, outputting the kernel function parameter.
Step 107: if the first judgment result indicates that iteration is terminated, determining an estimated value of the instantaneous frequency; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process.
Step 108: and determining a pollution estimated value according to the estimated value of the instantaneous frequency.
Step 109: determining a pollution-removing signal according to the pollution estimated value; the pollution-removing signal is a corrected ionospheric pollution signal.
Step 110: and taking the kernel function of the next transformation as an initialized kernel function, and returning to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function to determine the time-frequency distribution of the signal to be corrected.
The parameter calculation process of the kernel function is the process of fitting the time-frequency characteristics of the original signal by using the kernel function. From another perspective, the calculated kernel function may be used as an estimate of the time-frequency curve of the original signal. When the signal received by the antenna radar is only a single-component signal (such as only ground clutter or a target signal) of single-mode propagation, the fixed component of the original signal is subtracted from the kernel function, and the integral is performed to obtain the estimated value of the pollution phase sequence.
When the antenna radar receiving signal is a multi-component (single-mode propagation sea clutter or multi-mode propagation) signal, for the single-mode propagation situation, the ionospheric pollution of each component is the same, and a kernel function corresponding to one of the components is estimated; for the case of multimode propagation, there is a possibility that the ionosphere functions in the propagation modes are different, which causes different time-frequency distributions of the corresponding components of the different propagation modes, and the time-frequency distributions need to be estimated one by one and corrected one by one.
Ionospheric pollution correction can be generalized as follows:
(1) In the first (l=1, 2, …) correction, the received signal to be corrected is noted as x l (t), i=1 is the initial signal x (t), and x is estimated l The conversion kernel function corresponding to the largest component (i.e. the time-frequency ridge with the largest peak value is taken during the time-frequency ridge extraction) in (t) is used as the estimated IF of the instantaneous frequency l (t)；
(2) Subtracting the fixed frequency f of the clutter from the prior information of the clutter B And integrating to obtain an estimate of contamination in this modeUnder ideal conditions, the contamination is estimated accurately, +.>
(3) The pollution removing operation is carried out, and the signals after the pollution removing are
(4) Filtering clutter from the signal after decontamination by using the priori information of clutter, filtering and then combining withMultiplying to avoid the influence of the current correction on the next pollution correction, will +.>As a signal to be corrected for the next correction, here +.>Representing sea clutter principal component estimation obtained by using priori information, wherein a filter is adopted in actual operation for representing a form of signal subtraction written in the method;
(5) Judging whether the correction is finished or not, wherein the criterion isIf not, turning to (1).
In the ionospheric pollution correction, in the process of estimating parameters of the transformation kernel function, the time series of the kernel function is not the parameters of the kernel function, but the parameters of the kernel function Thus, in the specific operation of ionospheric pollution correction, p/o can be used>Interpolation is performed in such a way that ∈>The steps of curve fitting and parameter calculation are omitted, and the calculation flow is simplified. The entire ionospheric pollution correction flow is shown in figure 2.
Correction of ionospheric pollution in a sky-wave radar can sharpen the Doppler spectrum of a received signal so as to detect a target and estimate target parameters after suppressing clutter in the Doppler domain. The process of ionospheric pollution correction of fig. 2 is also a process of sea clutter suppression, wherein the suppression of sea clutter is achieved based on corrected clutter signal doppler domain characteristics. Reviewing the definition of generalized parameterized time-frequency transforms, ignoring the frequency shift factor and window function, the representation is as follows:
in terms of expression, in ionospheric pollution correction under multimode conditions, clutter filtering is equivalent to performing after the transformation, i.e. performing a decontamination operation on the signal according to the estimated instantaneous phase of pollution, and then performing fourier transformation. From the perspective of transformation, the operation of decontamination is equivalent to rotating the contaminated signal in the time-frequency domain, so that the instantaneous frequency of the signal is parallel to the time axis, even if the frequency of the signal does not change with time, and finally, the signal can achieve the best accumulation effect in the frequency domain.
When the signal to be decontaminated contains the target signal, the formula can be rewritten as follows:
where s (t) represents the target signal. For sky wave radar, the target signal is generally small relative to clutter, so pollution correction is not affected. Since the target prior information is generally unknown, clutter signals are filtered out after multiple corrections, leaving only target and noise signals. In the correction of fig. 2, in order to prevent the contamination of multiple propagation modes from interfering with each other, after correction and filtering of the contamination, the contamination factor is multiplied back again, so that the target signal after clutter filtering is still the signal after contamination, and in an ideal case, the clutter is completely filtered, which is expressed as follows:
further analysis of the transformation indicated by the equation shows that if the kernel function does not agree with the instantaneous frequency of ionospheric pollution, the transformed Doppler is not concentrated. Therefore, the detection of the signal in pollution can adopt the transformation of the formula, the instantaneous frequency of the pollution of each propagation mode estimated in pollution correction is used as a kernel function to transform the instantaneous frequency, so that the target can be effectively accumulated, and the signal to noise ratio is improved. And the signals after the transformation of the multiple propagation modes are subjected to joint detection, so that the target can be detected more reliably.
FIG. 3 is a block diagram of a system for correcting ionospheric pollution of a sky wave radar according to the present invention, as shown in FIG. 3, a system for correcting ionospheric pollution of a sky wave radar, comprising:
The signal to be corrected obtaining module 301 is configured to obtain a signal to be corrected of a slow time domain of the sky wave radar after ionosphere pollution.
The initialization module 302 is configured to select a spline function as a kernel function, perform initialization processing on the kernel function, and determine an initialized kernel function;
the time-frequency distribution determining module 303 is configured to perform generalized parametric time-frequency transformation on the signal to be corrected based on the initialized kernel function, and determine a time-frequency distribution of the signal to be corrected;
the time-frequency distribution of the signal to be corrected in the time-frequency distribution determining module 303 is:
TF(t 0 ,ω；P i ) At t 0 Time-frequency distribution of signals to be corrected at the moment;to transform the kernel parameter vector P in the ith iteration i The determined kernel function, i is the iteration number; t is t 0 Is the initial time; τ is an integral variable; x (τ) is the signal to be corrected; j is an imaginary unit; />Is an initialized kernel function; w (w) σ (t) represents a window function determined by the parameter σ; ω is the angular frequency.
A time-frequency ridge position determining module 304, configured to determine a time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected;
the time-frequency ridge position in the time-frequency ridge position determining module 304 is:
Wherein,the time-frequency ridge position of the ith signal to be corrected; TF (TF) x (t,ω；P i ) The time-frequency distribution of the signal to be corrected at the time t.
The fitting module 305 is configured to perform curve fitting on the time-frequency ridge position by using a spline function, and use the fitted curve as a kernel function of next transformation of the signal to be corrected;
a first judging module 306, configured to judge whether iteration is terminated based on the kernel function of the next transformation and the initialized kernel function, to obtain a first judging result;
the termination condition in the first determination module 306 is:
wherein,the time-frequency ridge position of the i-1 th signal to be corrected; ζ is a positive number below a first threshold; i.e max Is the maximum number of iterations.
An estimated value determining module 307 of the instantaneous frequency, configured to determine an estimated value of the instantaneous frequency if the first determination result indicates that iteration is terminated; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process;
a pollution estimate determination module 308 for determining a pollution estimate from the estimate of the instantaneous frequency;
a pollution-free signal determining module 309, configured to determine a pollution-free signal according to the pollution estimation value; the pollution-removing signal is a corrected ionospheric pollution signal;
And an iteration module 310, configured to take the kernel function of the next transformation as an initialized kernel function if the first determination result indicates that iteration is not terminated, and return to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function to determine a time-frequency distribution of the signal to be corrected.
The following numerical simulation experiments were designed to verify the effectiveness and robustness of the present invention:
experiment 1
Typical worker under sea surface target detection mode of sky wave radarAs a parameter, the pulse interval is 0.2s, and the coherent accumulation time is 40s. The working frequency is 18MHz, the incident complementary angle of the sea surface is 25 DEG, the receiving and transmitting quasi-single base is configured, and the first-order Bragg peak frequency of sea clutter is obtainedWhere g represents gravitational acceleration, ψ represents the complementary angle of incidence, and λ represents the wavelength. To verify the performance of the algorithm, it is assumed that the sea clutter consists of only two first order Bragg peaks and noise, i.e. x (t) =2exp (j 2 pi f) B+ t)+exp(j2πf B- t) +n (t), wherein f B+ ，f B- Respectively representing positive and negative first-order Bragg peak frequencies, and adding phase pollution signals in consideration of single-mode propagation conditions>Wherein B is the pollution amplitude, and represents the pollution fluctuation size, f m1 For the pollution frequency, the speed of pollution fluctuation is represented, the signal-to-noise ratio is 30dB, f m1 ＝0.08，/>The pollution phase estimation and pollution correction simulation results of the small-amplitude pollution (B=1) are shown, so that the pollution correction performance is compared, and two typical ionospheric pollution correction methods, namely a maximum entropy spectrum method and a singular value decomposition-based method are provided. The original signal in fig. 4 (b) refers to the signal spectrum without pollution, and the rest is the signal spectrum after adding pollution and adopting corresponding correction. From the results of small-amplitude pollution estimation and correction, the method and the method based on singular value decomposition can well estimate pollution and correct the pollution, and the maximum entropy spectrum method has larger estimation error and poorer correction performance; the pollution phase estimation and pollution correction results of the large-scale pollution (b=5) are shown in fig. 5, wherein the pollution can be estimated with higher precision, the method based on singular value decomposition is almost ineffective, and the maximum entropy spectrometry performance is centered. A comparison of fig. 4 and 5 demonstrates the robustness of the present invention to contamination relief sizes. To further analyze the robustness of the present invention, FIG. 6 shows the absolute error of pollution estimation under different signal-to-noise ratios, pollution amplitudes and frequencies/>Is a simulation result of (a). The standard parameter sets taken in the simulation are: signal to noise ratio 30dB, f m1 =0.08, b=1, one of the parameters varying under different conditions. As can be seen from the general trend of the change in fig. 6 (a) (b) (c): (1) Under the conditions of small-amplitude pollution and high signal-to-noise ratio, the method based on singular value decomposition is slightly better than the method of the invention in performance due to the structure capable of capturing pollution change more accurately, but has poor stability; (2) The maximum entropy spectrometry has slightly better performance than the invention at large signal-to-noise ratio and medium pollution frequency, as shown in fig. 6 (c), but has poorer stability as well; (3) The invention is insensitive to pollution amplitude and frequency variation, shows good robustness, and has obviously increased error and poor stability only when the signal-to-noise ratio is lower than 10dB, but for the situation of sea clutter, the second-order Doppler peak of the sea clutter is usually 20-30dB lower than the first-order Bragg peak, and the invention has better performance in the area, so the phenomenon does not influence the applicability of the method.
Experiment 2
To simulate sea clutter more realistically, the ionospheric pollution correction performance of the present invention was further verified. The sea clutter simulation method is adopted, and the influence of second-order and higher-order spectrum peaks is considered. In the simulation, the sea surface wind speed is set to be 15m/s, the distance resolution is 7.5km, the half-bistatic angle is 0, namely, in the case of a single base, the pulse interval is 0.2s, and the coherent accumulation time is 40s. The working frequency is 18MHz, and the incident complementary angle of the sea surface is 25 degrees. The impurity-to-noise ratio is 30dB, f m1 ＝0.08，Under the condition b=5, the ionospheric phase-contamination estimation and correction results are shown in fig. 7. It can be seen from the figure that the estimation performance of the phase pollution is somewhat reduced compared with that of fig. 5 due to the influence of the second-order and high-order spectrums of the sea clutter, but the invention can still well estimate and correct the pollution. The trend of the absolute error of the pollution estimate under the pollution amplitude and frequency conditions at different noise ratios in fig. 8 also further demonstrates the robustness and performance advantages of the present invention.
Experiment 3
The sea clutter simulation method in test 2 is still adopted, and the situation of multimode propagation is considered, so that two propagation modes are arranged for convenience, and the Doppler frequency shift brought by an ionosphere between the two modes is different by 0.2Hz. The reason why the frequency shift caused by the ionosphere is not considered in experiment 1 and experiment 2 is that the frequency shift only causes the shift of the frequency spectrum, but does not cause the broadening effect, and only needs to shift according to the symmetry of the sea clutter Doppler spectrum about the zero frequency after the broadening correction. In the simulation, the impurity-to-noise ratio is 30dB, the pollution parameter of the propagation mode 1 is B=1, and f m1 =0.08, the contamination parameter of propagation mode 2 is b=1, f m1 =0.08. The simulated raw signal and the pollution signal are shown in fig. 9 (a), and the pollution correction result is shown in fig. 9 (b). It can be seen from the figure that contamination under both propagation steps according to the step of fig. 2 can be corrected, indicating the effectiveness of the invention. But the interaction between the two causes the correction result to be spread to some extent compared with the original signal.
The generalized parameterized time-frequency transformation method can analyze the time-frequency characteristics of the multi-component signals with high precision, has no influence of factors such as cross terms and the like, and is an excellent time-frequency analysis method. The method is applied to the ionosphere pollution estimation and correction of the sky wave radar. According to the characteristics of ionosphere pollution, spline frequency modulation wavelet is selected as a kernel function of parameterized time-frequency transformation, kernel function parameters are estimated aiming at signals after the ionosphere pollution, a pollution sequence is estimated by using time-frequency characteristic quantity of the kernel function, and a pollution correction operator is constructed to carry out pollution correction. And for ionosphere pollution under the multimode propagation condition, adopting a mode of filtering after correction, and performing iteration for many times to finish the correction of the pollution.
The pollution correction problem is essentially a multi-component signal time-frequency analysis problem, and under the condition of single-mode propagation, the time-frequency structure of a plurality of components (sea clutter is simple, and is not described repeatedly), and the distribution of different components in a time-frequency plane is relatively open, so that the components are relatively easy to separate and estimate respectively. Under the multimode propagation condition, the time-frequency structures of the components do not have similarity, and the time-frequency structures are distributed densely on a time-frequency plane and are difficult to separate and estimate, so that the pollution correction performance is poor. The invention corrects pollution in turn through iteration, and whether the propagation mode is multimode propagation does not affect the actual implementation of the invention.
In fact, the method employed by the present invention in transforming the kernel parameter estimate is a multi-component decomposition method based on the estimated parameters, the resolution of which depends on the window function length employed. The method for analyzing the multi-component signals more accurately can adopt a method based on spectrum concentration, but the method needs to carry out multimodal search on kernel function parameters, and has larger operand. The method can be used for higher-precision pollution correction or searching better multi-component signal time-frequency analysis methods, for example: empirical mode decomposition, empirical wavelet transformation, differential mode decomposition and the like, and on the other hand, from the analysis of the formation mechanism of ionosphere pollution, the method can jump out of a time-frequency analysis pollution corrected nest, and homomorphic filtering technology for inhibiting intrinsic interference and the like can also be adopted.
The invention introduces a generalized parameter time-frequency conversion method to analyze the sky wave radar received signal, and provides a more effective and steady method for estimating and correcting the ionosphere pollution of the sky wave radar. The generalized parameterized time-frequency transformation aims at the signal model to construct a matched kernel function, and kernel function parameters are selected, so that the time-frequency characteristics of the nonstationary signal can be accurately described, and the multi-component signal can be effectively separated. When generalized parameterized time-frequency transformation is used for ionosphere pollution correction, spline frequency modulation wavelet is selected as a kernel function of parameterized time-frequency transformation according to the characteristics of ionosphere pollution, kernel function parameters are estimated for signals after the ionosphere pollution, a pollution sequence is estimated by using time-frequency characteristic quantity of the kernel function, and a pollution correction operator is constructed to carry out pollution correction. The method adopts a mode of filtering after correction for ionosphere pollution under multimode propagation conditions, and the pollution correction is completed through multiple iterations.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. A method for correcting ionospheric pollution in a sky wave radar, comprising:
acquiring signals to be corrected of a slow time domain of the sky wave radar after ionosphere pollution;
selecting a spline function as a kernel function, initializing the kernel function, and determining an initialized kernel function;
based on the initialized kernel function, performing generalized parameterized time-frequency transformation on the signal to be corrected, and determining time-frequency distribution of the signal to be corrected;
Determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected;
performing curve fitting on the time-frequency ridge position by using a spline function, and taking the fitted curve as a kernel function of next transformation of the signal to be corrected;
judging whether iteration is terminated or not based on the kernel function of the next transformation and the initialized kernel function, and obtaining a first judgment result;
if the first judgment result indicates that iteration is terminated, determining an estimated value of the instantaneous frequency; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process;
determining a pollution estimated value according to the estimated value of the instantaneous frequency;
determining a pollution-removing signal according to the pollution estimated value; the pollution-removing signal is a corrected ionospheric pollution signal;
if the first judgment result indicates that iteration is not terminated, taking the kernel function of the next transformation as an initialized kernel function, and returning to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function to determine the time-frequency distribution of the signal to be corrected.
2. The method for correcting ionospheric pollution of a sky-wave radar according to claim 1, wherein said performing a generalized parametric time-frequency transformation on said signal to be corrected based on said initialized kernel function, determining a time-frequency distribution of said signal to be corrected, comprises:
the time-frequency distribution of the signal to be corrected is as follows:
TF(t 0 ,ω；P i ) At t 0 Time-frequency distribution of signals to be corrected at the moment;to transform the kernel parameter vector P in the ith iteration i The determined kernel function, i is the iteration number; t is t 0 Is the initial time; τ is an integral variable; x (τ) is the signal to be corrected; j is an imaginary unit; />Is an initialized kernel function; w (w) σ (t) represents a window function determined by the parameter σ; ω is the angular frequency.
3. The method for correcting ionospheric pollution of a sky-wave radar according to claim 2, wherein said determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution comprises:
the time-frequency ridge positions are as follows:
wherein,the time-frequency ridge position of the ith signal to be corrected; TF (TF) x (t,ω；P i ) The time-frequency distribution of the signal to be corrected at the time t.
4. A method of correcting ionospheric pollution in a sky wave radar according to claim 3, wherein said determining whether an iteration is terminated based on said next transformed kernel function and said initialized kernel function comprises:
The termination conditions are:
or i > i max ；
Wherein,the time-frequency ridge position of the i-1 th signal to be corrected; ζ is a positive number below a first threshold; i.e max Is the maximum number of iterations.
5. The method for ionospheric pollution correction by sky wave radar according to claim 4, wherein said determining a pollution estimate from said estimate of instantaneous frequency comprises:
acquiring fixed frequencies of clutter;
according to the formulaDetermining a pollution estimated value; wherein (1)>Is a pollution estimated value; IF (IF) l (t) is an estimated value of the instantaneous frequency, l is the iteration number, l ε i; f (f) B Is a fixed frequency of spurs.
6. The method of claim 4, wherein determining a solution to the pollution signal from the pollution estimate further comprises: 10006
Ending the correction according to the correction ending condition;
the correction end condition is:
wherein x is l (t) is a signal to be corrected corresponding to the estimated value of the instantaneous frequency; x (t) is the initial signal to be corrected; epsilon is a positive number below the second threshold.
7. A sky wave radar ionospheric pollution correction system, comprising:
the signal to be corrected acquisition module is used for acquiring signals to be corrected of the sky wave radar slow time domain after ionosphere pollution;
The initialization module is used for selecting a spline function as a kernel function, initializing the kernel function and determining an initialized kernel function;
the time-frequency distribution determining module is used for performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function and determining the time-frequency distribution of the signal to be corrected;
the time-frequency ridge position determining module is used for determining the time-frequency ridge position of the signal to be corrected based on the time-frequency distribution; the time-frequency ridge position is an estimated value of the time-frequency characteristic of the signal to be corrected;
the fitting module is used for performing curve fitting on the time-frequency ridge position by utilizing a spline function, and taking the fitted curve as a kernel function of next transformation of the signal to be corrected;
the first judging module is used for judging whether iteration is ended or not based on the kernel function of the next transformation and the initialized kernel function to obtain a first judging result;
the estimation value determining module of the instantaneous frequency is used for determining the estimation value of the instantaneous frequency if the first judging result indicates that iteration is terminated; the estimated value of the instantaneous frequency is a kernel function corresponding to the maximum time-frequency ridge position in the iterative process;
The pollution estimated value determining module is used for determining a pollution estimated value according to the estimated value of the instantaneous frequency;
the pollution-removing signal determining module is used for determining a pollution-removing signal according to the pollution estimated value; the pollution-removing signal is a corrected ionospheric pollution signal;
and the iteration module is used for taking the kernel function of the next transformation as an initialized kernel function if the first judgment result shows that iteration is not terminated, returning to the step of performing generalized parameterized time-frequency transformation on the signal to be corrected based on the initialized kernel function, and determining the time-frequency distribution of the signal to be corrected.
8. The sky wave radar ionospheric pollution correction system of claim 7, wherein the time-frequency distribution of the signal to be corrected in the time-frequency distribution determination module is:
TF(t 0 ,ω；P i ) At t 0 Time-frequency distribution of signals to be corrected at the moment;to transform the kernel parameter vector P in the ith iteration i The determined kernel function, i is the iteration number; t is t 0 Is the initial time; τ is an integral variable; x (τ) is the signal to be corrected; j is an imaginary unit; />Is an initialized kernel function; w (w) σ (t) represents a window function determined by the parameter σ; ω is the angular frequency.
9. The sky-wave radar ionospheric pollution correction system of claim 8, wherein the time-frequency ridge locations in the time-frequency ridge location determination module are:
wherein,the time-frequency ridge position of the ith signal to be corrected; TF (TF) x (t,ω；P i ) The time-frequency distribution of the signal to be corrected at the time t.
10. The sky wave radar ionospheric pollution correction system of claim 9, wherein the termination condition in the first decision module is:
or i > i max ；
Wherein,the time-frequency ridge position of the i-1 th signal to be corrected; ζ is a positive number below a first threshold; i.e max Is the maximum number of iterations.
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