CN114791594B - Ionized layer dispersion effect correction method for nonlinear frequency modulation signals - Google Patents
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Abstract
The invention discloses a method for correcting an ionospheric dispersion effect of a nonlinear frequency modulation signal, which comprises the following steps: determining a TEC initial estimation interval by using an international reference ionosphere model; performing range-direction matched filtering processing on the original satellite-borne SAR echo data; utilizing three TEC values of the initial estimation interval, namely interval upper limit TEC for the distance frequency domain signal after the distance direction matched filtering processing up Interval lower limit TEC lo Median value of sum interval TEC mid Respectively compensating phase errors, wherein the compensation model uses an expanded solidified ionosphere model; transforming the compensated distance frequency domain signal back to the time domain, and calculating the image entropy under three TEC values in the initial estimation intervalE up 、E lo AndE mid (ii) a Based on a minimum image entropy criterion, approximating the minimum image entropy in the interval by a binary-like method, thereby obtaining the optimal TEC estimation in the initial estimation interval; and eliminating the nonlinear phase in the original satellite-borne SAR echo data, and compensating the dispersion phase according to the optimal TEC estimation and the expanded solidification ionized layer model.
Description
Technical Field
The invention relates to the field of Synthetic Aperture Radars (SAR), and mainly relates to a technology for correcting ionospheric dispersion effect for nonlinear frequency modulation signals.
Background
Synthetic Aperture Radar (SAR) is an active microwave imaging Radar, has the characteristics of all-time and all-weather operation, and has unique advantages in the aspects of environmental protection, disaster monitoring, marine observation, resource exploration, military reconnaissance and the like.
The basic idea of the SAR is to construct an equivalent long antenna by utilizing coherent accumulation of azimuth signals and a signal processing mode so as to improve the azimuth resolution. On the other hand, the SAR emits a signal with a large bandwidth and a large time width, and a high compression gain and a range-directional resolution are obtained by means of pulse compression, so that the range-directional performance of an image depends on the impulse response of the emitted signal. Conventionally, the SAR employs a Linear Frequency Modulation (LFM) as a transmission signal because the LFM signal has a large time bandwidth and good doppler characteristics, and the generation method is simple and easy to implement. However, conventional SAR systems using LFM signals as the single transmit signal also suffer from a number of difficulties, such as high side lobe and signal-to-noise ratio loss, resolution loss, and range ambiguity. Nonlinear frequency modulation (NLFM) can suppress side lobes without losing the signal-to-noise ratio, and effectively solves the defect of LFM signals, so that the NLFM has been widely applied in the SAR field in recent years.
For many military or civil applications, such as biomass inversion, disaster warning, environmental monitoring, etc., low frequency radar signals (typically less than 2 GHz) with better penetration performance are often used. However, due to the existence of the ionosphere, problems such as dispersion effect and the like can affect the signal, so that the imaging result has problems such as defocusing and geometric deviation. Conventionally, to solve the problem of image defocusing due to the ionospheric dispersion effect, a spectrum segmentation method is generally adopted. However, when the operating band is low or the solar activity is strong, it is difficult to obtain a high-quality SAR image due to the problem of defocusing caused by phase dispersion. However, no study has been made to disclose ionospheric correction techniques for NLFM signals.
Disclosure of Invention
In view of this, the main objective of the present invention is to provide a method for correcting an ionospheric dispersion effect of an NLFM signal, which is to estimate a Total Electron Content (TEC) in the NLFM signal and compensate a dispersion phase by using an extended solidified ionospheric model, so as to solve the problem of the ionospheric dispersion effect faced by the NLFM signal in a low-frequency-band SAR system.
In order to achieve the purpose, the technical scheme of the invention is realized as follows: a method of correcting for ionospheric dispersion effects in a non-chirped signal, the method comprising:
step 101, determining a TEC initial estimation interval by using an international reference ionosphere model;
102, performing range-direction matched filtering processing on original satellite-borne SAR echo data;
step 103, aiming at the distance frequency domain signal after the distance direction matching filtering processing, utilizing three TEC values of the initial estimation interval, namely interval upper limit TEC up Interval lower limit TEC lo Median value of sum interval TEC mid Respectively compensating phase errors, wherein the compensation model uses an expanded solidified ionosphere model;
step 104, converting the compensated distance frequency domain signal back to a time domain, and calculating the image entropy under three TEC values in the initial estimation intervalE up 、E lo AndE mid ；
step 105, based on a minimum image entropy criterion, approximating the minimum image entropy in the initial estimation interval by a binary-like method, so as to obtain the optimal TEC estimation in the initial estimation interval;
and 106, eliminating the nonlinear phase in the original satellite-borne SAR echo data, and compensating the dispersion phase according to the optimal TEC estimation and the expanded solidification ionized layer model.
Has the advantages that:
aiming at the problem that no correction method aiming at the ionospheric dispersion effect of NLFM signals exists in the prior art, the influence of the ionospheric dispersion effect on the NLFM signals in a low-frequency (L-band and P-band) satellite-borne SAR system cannot be effectively inhibited or reduced. The invention can eliminate the problem of ionospheric dispersion effect faced by NLFM signals in a low-frequency band SAR system to a great extent, obtain a fine focus imaging result and greatly reduce phase errors introduced by an ionosphere in subsequent application processing.
Drawings
FIG. 1A is a flow chart of a calibration method of the present invention;
FIG. 1B is a detailed flowchart of the calibration method of the present invention;
FIG. 2 is a schematic diagram of a spectrum of an NLFM signal under a specific parameter;
FIG. 3 is a schematic diagram of the frequency spectrum of an LFM signal under a specific parameter;
FIG. 4 shows a simulation result of an L-band spaceborne SAR point target under a specific parameter;
FIG. 5A is a two-dimensional contour diagram of the imaging result of the scene edge point P when the ionospheric dispersion effect is not compensated;
FIG. 5B is a cross-sectional view of the imaging result distance of the scene edge point P without compensating the ionospheric dispersion effect;
FIG. 6A is a two-dimensional contour diagram of the imaging result of the scene edge point P after correcting the ionospheric dispersion effect by the method;
FIG. 6B is a distance profile of the imaging result of the scene edge point P after correcting the ionospheric dispersion effect by this method.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person skilled in the art based on the embodiments of the present invention belong to the protection scope of the present invention without creative efforts.
The invention provides a method for correcting an ionized layer dispersion effect of a nonlinear frequency modulation signal, and a flow chart of the correction method is shown as figure 1A and comprises the following steps:
step 101, determining a TEC initial estimation interval by using an international reference ionosphere model;
102, performing range-direction matched filtering processing on original satellite-borne SAR echo data;
step 103, aiming at the distance frequency domain signal after the distance direction matched filtering processing, utilizing three TEC values of the initial estimation interval, namely the interval upper limit TEC up Interval lower limit TEC lo And median TEC of range mid Separately compensating for phase error, wherein the compensation model uses extended freeze ionizationA layer model;
step 104, converting the compensated distance frequency domain signal back to a time domain, and calculating the image entropy under three TEC values in the initial estimation intervalE up 、E lo AndE mid ；
step 105, based on a minimum image entropy criterion, approximating the minimum image entropy in the initial estimation interval by a binary-like method, so as to obtain the optimal TEC estimation in the initial estimation interval;
and 106, eliminating the nonlinear phase in the original satellite-borne SAR echo data, and compensating the dispersion phase according to the optimal TEC estimation and the expanded solidification ionized layer model.
Referring to fig. 1B, the method specifically includes the following steps:
step 101: an International Reference Ionosphere (IRI) model is used to determine the TEC initial estimation interval.
Acquiring TEC distribution condition of an imaging target region by utilizing an IRI model to obtain an initial estimation interval [ TEC up ,TEC lo ]Therein TEC up ,TEC lo Denotes the upper and lower interval limits, subscriptsupAndlothe upper and lower limits are indicated; calculating median of interval
SubscriptmidThe meaning of median is represented. The initial estimation interval contains the optimal TEC value.
Step 102: and performing range-direction matched filtering processing on the original satellite-borne SAR echo data.
Assuming that the radar transmits NLFM signals, the radar reception echo signals after quadrature demodulation can be expressed as:
s N (τ,η)=A 1 ·ω r (τ-2R(η)/c)·ω a (η-η c )·exp(-j4πf c R(η)/c)·exp(jθ(η-2R(η)/c)) (1)
wherein the content of the first and second substances,cis the speed of light;f c is the carrier frequency; a. the 1 Is the backscattering coefficient of the point target;τandηdistance direction time and azimuth direction time, respectively;ω r (. a) andω a (. h) is a distance window function and an orientation window function, respectively, and rectangular windows are uniformly adopted here for convenience;R(η) Is the slope course corresponding to the point target;η c the moment when the beam center passes through the point target;θ(. cndot.) is the modulation phase of the NLFM signal, and j is the imaginary unit.
Distance Fourier transform is carried out on the formula (1) to obtain:
S N (f τ ,η)=A 2 ·W r (f τ )·ω a (η-η c )·exp(-j4π(f c +f τ )R(η)/c)·exp(jΘ(f τ )) (2)
f τ represents the range-wise frequency;
wherein A is 2 Is a constant, representing the magnitude of the distance spectrum;W r (f τ ) An envelope representing the distance spectrum; Θ (f τ ) Is the spectral phase of the NLFM signal. According to equation (2), a distance-direction matched filter of the NLFM signal is constructed as follows:
H N (f τ )=W r (f τ )·exp(jΘ(f τ ))=[FFT(s(τ))] * (3)
wherein, FFT (-) represents Fourier transform,s(τ) Representing the baseband NLFM signal. Ranging the received echo data using equation (3)And (3) matching filtering operation:
S N 1 (f τ ,η)=S N (f τ ,η)·H N (f τ ) (4)
wherein the content of the first and second substances,S N (f τ ,η) Is the distance frequency domain signal obtained in equation (2);H N (f τ ) Is the distance matched filter obtained in equation (3). After distance direction matching filtering, the modulation phase term Θ of the NLFM signal (b: (b) (b))f τ ) Is eliminated and the process is carried out,S N1 (f τ ,η) Representing the distance frequency domain signal for completion of the distance to focus.
Step 103: for the distance frequency domain signal after the distance direction matched filtering, three TEC values of the estimation interval, namely interval upper limit TEC, are utilized up Interval lower limit TEC lo Median value of sum interval TEC mid And respectively carrying out phase error compensation, wherein the compensation model uses an extended solidified ionosphere model. The specific flow is shown in FIG. 1B.
The expression for the compensation phase is:
wherein the content of the first and second substances,Krepresenting a standard electronic parameter with a value of 40.28m 3 /s 2 ；f τ (τ) Is the instantaneous frequency function of the NLFM signal, which varies non-linearly with time. In the extended coagulated ionosphere model, there aref(τ)=b 0 +b 1 τ+b 2 τ 2 +···b N τ N Whereinb 0 ，b 1 ，···，b N Is a coefficient of each order of a polynomial,Nrepresenting the order of approximation of the polynomial curve fit.
Let the TECs be TECs respectively up 、TEC lo And TEC mid And respectively compensating the distance back to the distance frequency domain signals after the matched filtering:
wherein the content of the first and second substances,S N1 (f τ ,η) A distance frequency domain signal for the completion distance to focus operation obtained in equation (4).
Step 104: converting the compensated distance frequency domain signal back to a time domain, and calculating the image entropy under three TEC values in the intervalE up 、E lo AndE mid 。
transform the compensated signal back to the time domain:
wherein IFFT (·) represents an inverse fourier transform;S com_up (f τ ,η)，S com_lo (f τ ,η)，S com_mid (f τ ,η) The phase-compensated distance frequency domain signals obtained in equation (6) correspond to the upper interval limit, the lower interval limit, and the median interval value, respectively.
The image entropy can be calculated by the following expression:
wherein the content of the first and second substances,Irepresenting an image to be computed; | · | represents a get amplitude operation;NrandNathe number of points representing the image distance direction and the azimuth direction, respectively. Thus, the entropy of the image under the three compensation conditions is calculated asE up 、E lo AndE mid 。na and Nr are two arguments for Na and Nr in the summation symbol.
Step 105: and based on the minimum image entropy criterion, approximating the minimum image entropy in the interval by a binary-like method, thereby obtaining the optimal TEC estimation in the initial estimation interval.
For the three image entropies calculated above, ifE up ≥E lo Then there is deltaE=|E mid -E lo |And TEC up =TEC mid (ii) a Otherwise there is deltaE=|E mid -E up |And TEC lo =TEC mid . When deltaEWhen the value is larger than or equal to the threshold value, the step 103 is returned, and the dichotomy-like operation of the steps 103 and 104 is repeated until the value is deltaEUntil the value is less than the threshold value, the optimal TEC is estimated to be
. The threshold value may be set to 1e in consideration of the number of searches and the requirement of approximation accuracy -16 The threshold may be further reduced by an order of magnitude if a more accurate TEC estimate is desired.
Step 106: and (4) compensating the dispersion phase according to the optimal TEC estimation and the expanded solidification ionized layer model, eliminating a nonlinear phase item in the compensated data, and finally performing normal imaging processing.
And (3) compensating the obtained optimal TEC estimation aiming at the satellite-borne SAR echo data to eliminate the phase error introduced by the ionospheric dispersion effect:
wherein the content of the first and second substances,S N (f τ ,η) The range frequency domain signal is obtained by performing range-to-Fourier transform on the satellite-borne SAR echo according to the formula (2); phase _ com is a compensation phase represented by formula (5);
the optimal TEC estimated value estimated for the proposed method.
And (3) eliminating the nonlinear phase term in the compensated data, namely performing distance direction matching filtering operation on the compensated data according to the formula (3):
S com (f τ ,η)=S com (f τ ,η)·H N (f τ ) (10)
wherein the content of the first and second substances,H N (f τ ) A distance-wise matched filter is obtained for equation (3).
And finally, performing imaging processing operation to obtain a fine focusing image after the ionospheric dispersion effect is eliminated.
The technical solution of the present invention will be further described in detail with reference to the following specific examples.
Example 1
The method adopts parameters of an actual L-band spaceborne SAR system and takes an NLFM signal as a transmitting signal to generate a target echo. Fig. 2 and 3 show the frequency spectrum diagrams of the NLFM signal and the LFM signal. It can be seen that NLFM signals cannot eliminate the effect of ionospheric dispersion using conventional spectral segmentation methods because the spectral shape no longer appears rectangular. The error of the dispersion effect of the ionized layer is added into the echo signal and then processed by the correction method provided by the invention. FIG. 4 is a simulation result of L-band spaceborne SAR lattice targets corrected by the proposed method under a specific parameter; FIG. 5A is a two-dimensional contour diagram of the imaging result of the scene edge point P when the ionospheric dispersion effect is not compensated, and FIG. 5B is a distance profile of the imaging result of the scene edge point P when the ionospheric dispersion effect is not compensated; fig. 6A is a two-dimensional contour diagram of the imaging result of the scene edge point P after correcting the ionospheric dispersion effect by the method, and fig. 6B is a distance profile diagram of the imaging result of the scene edge point P after correcting the ionospheric dispersion effect by the method.
The above description is only an example of the present invention, and is not intended to limit the scope of the present invention.
Claims (4)
1. A method for correcting ionospheric dispersion effects in a non-chirped signal, the method comprising:
step 101, determining a TEC initial estimation interval by using an international reference ionosphere model; in the step 101, an international reference ionosphere model is used to determine an initial estimation interval of the TEC:
acquiring the TEC distribution condition of an imaging target region by using an international reference ionosphere model to obtain an initial estimation interval [ TEC up ,TEC lo ]And calculating the median TEC of the initial interval mid The optimal TEC estimation value is within the initial estimation interval;
102, performing range-direction matched filtering processing on original satellite-borne SAR echo data;
the step 102 of performing range-wise matched filtering processing on the original satellite-borne SAR echo data specifically includes:
constructing a range-direction matched filter by using the NLFM signal of the baseband, and performing range-direction matched filtering operation on the original satellite-borne SAR echo data by using the matched filter to obtain range-direction focused data;
assuming that the radar transmits NLFM signals, the radar reception echo signals after quadrature demodulation can be expressed as:
s N (τ,η)=A 1 ·ω r (τ-2R(η)/c)·ω a (η-η c )·exp(-j4πf c R(η)/c)·exp(jθ(η-2R(η)/c)) (1)
wherein the content of the first and second substances,cis the speed of light;f c is the carrier frequency; a. the 1 Is the backscattering coefficient of the point target;τandηdistance direction time and azimuth direction time, respectively;ω r (. a) andω a (. h) is a distance window function and an orientation window function, respectively, and rectangular windows are uniformly adopted here for convenience;R(η) Is the slope course corresponding to the point target;η c the moment when the beam center passes through the point target;θ(. is) the modulation phase of the NLFM signal;
distance Fourier transform is carried out on the formula (1) to obtain:
S N (f τ ,η)=A 2 ·W r (f τ )·ω a (η-η c )·exp(-j4π(f c +f τ )R(η)/c)·exp(jΘ(f τ )) (2)
f τ represents the range-wise frequency;
wherein A is 2 Is a constant, representing the magnitude of the distance spectrum;W r (f τ ) An envelope representing a distance spectrum; Θ (f τ ) Is the spectral phase of the NLFM signal; according to equation (2), a distance-direction matched filter of the NLFM signal is constructed as follows:
H N (f τ )=W r (f τ )·exp(jΘ(f τ ))=[FFT(s(τ))] * (3)
wherein, FFT (-) represents Fourier transform,s(τ) NLFM signals representing baseband; performing range-wise matched filtering on the received echo data using equation (3): j is an imaginary unit;
S N 1 (f τ ,η)=S N (f τ ,η)·H N (f τ ) (4)
wherein the content of the first and second substances,S N (f τ ,η) Is the distance obtained in equation (2)A frequency domain signal;H N (f τ ) Is the distance matched filter obtained in equation (3); after distance direction matching filtering, the modulation phase term Θ of the NLFM signal (b: (b) (b))f τ ) Is eliminated by the device and the method,S N1 (f τ ,η) A distance frequency domain signal representing distance toward completion of focusing;
step 103, aiming at the distance frequency domain signal after the distance direction matched filtering processing, utilizing three TEC values of the initial estimation interval, namely the interval upper limit TEC up Interval lower limit TEC lo Median value of sum interval TEC mid Respectively compensating phase errors, wherein the compensation model uses an expanded solidified ionosphere model;
step 104, converting the compensated distance frequency domain signal back to a time domain, and calculating the image entropy under three TEC values in the initial estimation intervalE up 、E lo AndE mid ；
105, on the basis of a minimum image entropy criterion, approximating the minimum image entropy in the initial estimation interval by a dichotomy-like method, so as to obtain the optimal TEC estimation in the initial estimation interval;
106, eliminating a nonlinear phase in the original satellite-borne SAR echo data, and compensating a dispersion phase according to the optimal TEC estimation and the expanded solidification ionized layer model;
in the step 106, the dispersion phase is compensated according to the optimal TEC estimation and the extended solidified ionosphere model, then the nonlinear phase term of the compensated signal is eliminated, and finally normal imaging processing is performed, specifically as follows:
utilizing the optimal TEC estimation and the expanded solidified ionosphere model to perform ionosphere dispersion phase error compensation on the original data, and then performing nonlinear phase item elimination operation, namely distance direction matched filtering operation on the compensated signals; and finally, imaging the obtained signal to obtain a fine focusing image after the ionospheric dispersion effect is eliminated.
2. The method of claim 1, wherein step 103 is performed for distance-wise matched filteringThe subsequent distance frequency domain signal utilizes three TEC values of the initial estimation interval, namely interval upper limit TEC up And interval lower limit TEC lo Median value of sum interval TEC mid Respectively, performing phase error compensation, wherein the compensation model uses an extended solidified ionosphere model:
three TEC values in the initial estimation interval, namely interval upper limit TEC up And interval lower limit TEC lo Median value of sum interval TEC mid And substituting the instantaneous frequency function obtained by a polynomial curve fitting method into the expanded solidified ionosphere model to obtain compensation phases, and multiplying the compensation phases by the distance frequency domain signals after matched filtering respectively to obtain compensation signals corresponding to the three TEC values.
3. The method of claim 1, wherein step 104, the compensated distance frequency domain signal is transformed back to the time domain, and the image entropy at three TEC values of the initial estimation interval is calculatedE up 、E lo AndE mid ：
based on the image minimum entropy criterion, for all TEC values in the initial estimation interval, the image compensated by the optimal TEC estimation value has the minimum entropy, three compensated time domain signals are obtained, and the corresponding image entropy is calculatedE up 、E lo AndE mid 。
4. the method of claim 1, wherein step 105 approximates the minimum image entropy within the initial estimation interval by a dichotomy-like approach based on a minimum image entropy criterion to obtain the optimal TEC estimate within the initial estimation interval as follows:
carrying out a dichotomy-like operation: if it is notE up ≥E lo Then there is deltaE=|E mid -E lo I and TEC up =TEC mid (ii) a Otherwise there is DeltaE=|E mid -E up I and TEC lo =TEC mid (ii) a When deltaEWhen the current estimation value is larger than or equal to the threshold value, updating the TEC initial estimation interval and carrying out compensation operation again; repeating the above dichotomy-like operation until deltaELess than the threshold.
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| CN111583267A (en) * | 2020-05-13 | 2020-08-25 | 中国科学院空天信息创新研究院 | Generalized fuzzy C-means clustering-based fast SAR image sidelobe suppression method |
| CN113406632A (en) * | 2021-05-26 | 2021-09-17 | 中国科学院空天信息创新研究院 | L-band spaceborne bistatic SAR signal processing method and device and storage medium |
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