CN106569188A - Ionosphere phase pollution correction algorithm based on improved PGA - Google Patents
Ionosphere phase pollution correction algorithm based on improved PGA Download PDFInfo
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- CN106569188A CN106569188A CN201610936364.XA CN201610936364A CN106569188A CN 106569188 A CN106569188 A CN 106569188A CN 201610936364 A CN201610936364 A CN 201610936364A CN 106569188 A CN106569188 A CN 106569188A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/40—Means for monitoring or calibrating
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
- G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
- G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
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- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
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- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses an ionosphere phase pollution correction algorithm based on improved PGA. The algorithm includes at first, performing FFT calculation of the echo data of a distance-azimuth resolution unit and obtaining the frequency spectrum, filtering out the broadening strong Bragg peak by means of an adaptive slide window band pass filter, and performing IFFT on the frequency domain data output by the filter, correcting the echo data by means of the data and at the same time setting the threshold according to the mean value of the data, recording the position of the data whose amplitude is below the threshold, and rejecting the data corresponding to the position in the echo data, and at the end, reconstructing the echo data by means of a CS algorithm and obtaining the frequency spectrum. Therefore, the ionosphere phase pollution is corrected, the sea/ground clutter spectrum peak is sharpened, and the noise floor is reduced. As shown by the simulation result, compared with the PGA algorithm, after the radar echo signal is corrected via the improved PGA algorithm ionosphere, the radar noise floor is reduced about 10dB to be consistent to the radar echo signal noise floor out of the phase pollution.
Description
Technical field
The invention belongs to algorithm of target detection in Radar Technology, and in particular to a kind of based on the ionosphere phase place for improving PGA
Pollution correcting algorithm.
Background technology
Sky-wave OTH radar (OTHR) is operated in high frequency band, is using reflex of the ionosphere to high frequency band signal
And the radar system to target acquisition beyond sighting distance is realized, military field has been applied to.But ionosphere is the shakiness of time-varying
Determine transmission medium, certain phase perturbation can be brought to the echo-signal of OTHR, cause Doppler's spectral peak exhibition of sea/land clutter
Width, covers the Weak target near it.Therefore, for OTHR target detection capabilities are improved, ionosphere phase perturbation correction
It is particularly significant.
Phase gradient algorithm (PGA) is leached at the positive/negative Bragg peaks of broadening using bandpass filter, and to it IFFT is carried out
And the time domain data at acquisition Bragg peaks, it is then assumed that the phase difference between consecutive number strong point is Δ φ, time interval is Δ t, then
It is f (t)=Δ φ/2 π Δ t that its instantaneous frequency can be calculated, and finally just ionosphere phase place can be carried out using this Instantaneous frequency variations
Correction.Although the method is very simple and directly perceived, and can calculate the change of Bragg peaks instantaneous frequency in the short time, real
Border using when find, for the Bragg peaks time domain data for filtering, if the amplitude in a certain data point is relatively low, then
The instantaneous frequency calculated on the aspect occurs a mutation relative to the point of surrounding, increases calculation error, and then reduces PGA
Solution phase place pollutant performance.
Based on more than analyze, PGA algorithms in the case where Bragg peaks miscellaneous noise ratio (CNR) are relatively low, the instantaneous pollution for calculating
Between phase place and actual value have relatively large deviation, it would be desirable to seek a kind of more effective approach, that is, find Bragg peak CNR compared with
Low position, and this position data is carried out using more efficient way solve phase place pollution.
The content of the invention
It is an object of the invention to provide it is a kind of based on the ionosphere phase perturbation correction algorithm for improving PGA, overcome PGA
Algorithm has asking for relatively large deviation in the case where Bragg peaks CNR is relatively low, between the instantaneous pollution phase place for calculating and actual value
Topic.
The technical solution for realizing the object of the invention is：It is a kind of to be calculated based on the ionosphere phase perturbation correction for improving PGA
Method, method and step is as follows：
Step 1：The strong sea clutter Bragg peaks of energy in echo-signal are extracted using sliding window bandpass filter, Bragg is obtained
Time domain data s (n) at peak, according to radar transmitter frequency Bragg peaks frequency f is calculatedb, so as to s (n) is moved to zero-frequency,
Calculate phase errorn。
Step 2：Echo data phase place is corrected according to formula (5), and calculates self adaptation magnitude threshold T：
X '=Ψ x (5)
Wherein, in a CIT, the echo data of a certain range-azimuth resolution cell, after the pollution of ionosphere phase place
The vector x tieed up with N × 1 is represented；N × 1 n dimensional vector n x ' represents the echo data after phasing；N × N-dimensional matrix Ψ represents ionization
Layer phasing matrix, and phase calibration is in the diagonal positions of matrix；
Self adaptation magnitude threshold T=meanamη, meanamThe average of s (n) amplitude is represented, η is represented and met a certain specific mistake
Difference and the scale factor that sets, are usually arranged as 0.6.
Step 3：According to formula (6) and self adaptation magnitude threshold T, data y after being rejected.
Step 4：Data y after rejecting are reconstructed according to formula (7) and formula (8), obtain reconstruct data spectrum
Calculated value θ 'y。
Compared with prior art, its remarkable advantage is the present invention：
(1) the positive/negative Bragg peaks of sea clutter are more accurately extracted.
(2) in the case where Bragg peaks CNR is relatively low, also can be corrected to polluting echo data.
Description of the drawings
Fig. 1 is the frequency-amplitude figure of the sliding window bandpass filter of the present invention.
Fig. 2 is the calculation flow chart of the adaptive sliding window bandpass filter that the present invention is adopted.
Fig. 3 is simulation result figure, wherein figure (a) is comparison diagram before and after the pollution of ionosphere phase place, figure (b) is adaptive threshold
Detection data figure, figure (c) PGA calculates phase place and actual value comparison diagram, and figure (d) is PGA and improves Frequency spectrum ratio after PGA phase calibrations
Relatively scheme.
Fig. 4 is algorithm flow chart of the present invention based on the ionosphere phase perturbation correction algorithm for improving PGA.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
In a CIT, the echo data of a certain range-azimuth resolution cell, by ionosphere phase place pollution after with N ×
The vector x of 1 dimension represents that wherein N represents the umber of pulse in a CIT.Sea/land clutter, target and noise are included in x, wherein sea
The Bragg peak energy amounts of clutter are most strong.Assume that P (f) represents the FFT of x, because by the pollution of ionosphere phase place, clutter spectrum is tight
Weight broadening.With reference to Fig. 4, introduce based on the ionosphere phase perturbation correction algorithm for improving PGA, method and step is as follows：
Step 1：The strong positive/negative Bragg peaks of energy in echo-signal are extracted using sliding window bandpass filter, Bragg is obtained
Time domain data s (n) at peak, according to radar transmitter frequency Bragg peaks frequency f is calculatedb, so as to s (n) is moved to zero-frequency,
Calculate phase errorn：
The position of maximum in echo data frequency spectrum is determined first, is judged to extract positive/negative Bragg according to maximum value position
Peak, is described below the Bragg peaks that broadening how is extracted using adaptive sliding window bandpass filter, it is assumed that extract the negative Bragg in left side
Peak.
1. echo data noise power average is calculated using following formula.
WhereinRepresent noise power average；P (f) represents the frequency spectrum of echo data；[fu,fd] represent that Frequency Domain Integration is calculated
Interval, generally select front end without clutter and target area.
2. one group of rectangular filter is designed.The center frequency points of first wave filter are f1, initial and cut-off frequency difference
For f1- m Δ f and f1+ m Δ f, wherein m are a positive integers, represent the size of wave filter window, and Δ f represents that frequency domain sample is spaced.Adopt
The frequency spectrum for starting to extract echo data from the negative frequency section leftmost side with this wave filter, extracts general power P of portions of the spectrum1For
And then in the clutter general power P ' of this frequency range1Can be calculated by following formula
The centre frequency of second wave filter is f2=f1+ Δ f, starting and cut-off frequency are respectively f2- m Δ f and f2+mΔ
F, using formula (2) and formula (3) identical computational methods P ' can be obtained2, and then others P 'i(i=3,4 ..., N/2-2m) with same
Mode can be calculated, wherein N is the total sampling number of echo data (umber of pulse in a CIT).Window sliding cut
Only Frequency point is spectral centroid point 0Hz, and concrete condition is as shown in Figure 1.One group of clutter general power is obtained by the slip of window,
The maximum in this group of clutter general power is searched, and uses L1It is represented, and records the corresponding center frequency points of maximum, be designated as
fmid1。
3. sliding window width is converted, if m=m+ is λ, wherein λ is positive integer, and then calculates clutter general power maximum L2, and remember
The corresponding center frequency points of record maximum, are designated as fmid2。
If 4. L2/L1(δ is a decision threshold to≤δ satisfactions, and typically smaller than 1.1), the window width m of filtering is optimum
, i.e., this window width is suitable with the sea clutter Bragg peaks of extension, if conversely, L2/L1>δ, then L1=L2, repeat the above-mentioned 3rd
Step is calculated, and the calculation process of adaptive sliding window bandpass filter is as shown in Figure 2.
Bragg peaks are extracted according to above-mentioned Bragg peaks extracting method, and then extracts the time domain data at Bragg peaks and be expressed as
Wherein s (n) represents the time domain data at Bragg peaks；J represents imaginary unit；εnRepresent phase error；κn, φ and fbPoint
Biao Shi not amplitude, initial phase and Bragg peaks frequency；- represent that sliding window bandpass filter filters the Bragg peaks in left side ,+represent and slide
Curtain heading tape bandpass filter filters right side Bragg peaks；Δ t represents that time-domain sampling is spaced；V (n) represents noise, and assumes that this kind of noise is
White Gaussian noise；N represents n-ththIndividual hits, N represents total sampling number (umber of pulse in i.e. one CIT).
From formula (4) as can be seen that only including single Bragg peaks in s (n), therefore the phase place in s (n) is calculated using PGA
Error, and phasing is carried out to x with the phase error for calculating, i.e., frequency f at Bragg peaks is calculated according to transmitting carrier frequencyb,
So as to s (n) is moved to zero-frequency, phase error just can be calculatedn。
Step 2：Echo data phase place is corrected according to formula (5), self adaptation magnitude threshold T is calculated.
Calculate phase errornPhasing can be carried out to x, specific updating formula is as follows
X '=Ψ x (5)
Wherein N × 1 n dimensional vector n x ' represents the echo data after phasing；N × N-dimensional matrix Ψ represents ionosphere phase place school
Positive matrices, and phase calibration is in the diagonal positions of matrix.When the CNR at Bragg peaks is larger, impact of the noise to PGA can
To ignore, so as to accurately calculate phase errorn, and the CNR at Bragg peaks it is less when, impact of the noise to PGA can not
Ignore, phase errornCannot accurately calculate, and then cause clutter spectral peak to extend, noise floor is raised.Therefore, ask for above-mentioned
Topic proposes self adaptation magnitude threshold T, searches (the i.e. less position of Bragg peak amplitudes of CNR less positions in Bragg peaks in s (n)
Put), wherein self adaptation magnitude threshold T=meanamη, meanamThe average of s (n) amplitude is represented, η is represented and met a certain specific mistake
Difference and the scale factor (being usually arranged as 0.6) that sets.
Step 3：Data y after being rejected according to formula (6) and self adaptation magnitude threshold T.
Data Position of the amplitude less than self adaptation magnitude threshold T will be recorded in s (n), and by the number of this position in x '
According to rejecting, the data after rejecting are represented by
Y=Fx '=F Ψ x (6)
Wherein y is M × 1 (M ＜ N) n dimensional vector n, represents the remaining data for rejecting the middle amplitude smaller portions of x '；F represents M × N
Dimension rejects matrix, is to extract M rows by row from N × N-dimensional diagonal matrix, and the line number of extraction is with that data point is not rejected in x ' is relative
Should.FFT is carried out to x can obtain x=Φ θx, wherein Φ represents N × N-dimensional IFFT matrix, i.e. Φ=IFFT [I], I represents N × N-dimensional pair
Angular moment battle array, θxRepresent that the spectral vector of x is tieed up in N × 1.
Step 4：Data y are reconstructed according to formula (7) and formula (8), obtain the calculated value θ of reconstruct data spectrum
′y。
CS theories show, when some condition is fulfilled, by solving l1Norm optimization problem, can be by unknown sparse letter
Number accurately recover from the data of limited quantity.After the phase perturbation correction of ionosphere, Bragg peaks, land clutter and target
Sparse form is shown as in the whole frequency spectrum of y.Therefore, y is sparse in Doppler domain, and then it is theoretical to recovering number to meet CS
According in some domain can rarefaction representation requirement, formula (6) is rewritable to be
Y=F Φ θy (7)
Wherein F and Φ represent that respectively M × N (M ＜ N) dimensions perceive matrix (rejecting matrix) and N × N-dimensional basis matrix (IFFT
Matrix)；θyComplete Doppler frequency spectrum is represented, including sea/land clutter, target and noise.For a U-shaped sparse signal
(special aobvious point number U meets U ＜ ＜ N i.e. in frequency spectrum), if dictionary matrix Θ=F Φ meet limited equidistant characteristics (RIP) and M >=
Ο (UlogN), by solving following convex problem, θyCan be from precise restoration in finite data y
min(||θ′y||1),subject to||Θθ′y-y||2≤τ (8)
Wherein θ 'yRepresent θyCalculated value；||·||pRepresent and calculate lpNorm；Min () is represented and is asked for minimum of a value.Perceive
Matrix F and basis matrix Φ respectively reject matrix and IFFT matrixes.Generally perceive any two row in matrix F and basis matrix Φ
Between correlation it is less, therefore dictionary matrix Θ meet RIP requirement.τ represents radar noise substrate, and this value can only be received in radar
Accurately calculate during noise.It follows that CS algorithm primary conditions have met, data spectrum can be reconstructed by CS algorithms.
Embodiment 1
The validity of this patent algorithm is verified using ground wave OTHR measured data, wherein ground wave OTHR is returned
Wave number is according in comprising sea, land clutter and target.Emission signal frequency is 7.5MHz, transmission signal period Δ t=0.7264, single
Umber of pulse in CIT is N=128, and scale factor η=0.6 when thresholding is selected, phase place pollutes function of εn=1.2exp (0.01n
Δt)sin(0.15nΔt)。
With reference to Fig. 3, sea/land clutter spectral contrast result such as Fig. 3 a before and after the pollution of ionosphere phase place) shown, wherein solid line
Expression is not added with the sea/land clutter frequency spectrum of ionosphere phase place pollution, and corresponding dotted line represents that addition ionosphere phase place is dirty
Sea after dye/land clutter frequency spectrum, near zero-frequency, sea clutter Bragg peaks are respectively in ± 0.3Hz positions, target for land clutter frequency spectrum
In -0.6Hz positions.Fig. 3 a) show that phase place pollution can cause sea/land clutter and target spectral peak broadening, radar noise substrate from
60dB is lifted to 70dB, and then causes SOTHR target detection performances to reduce.In Fig. 3 b) in, solid line is represented to be filtered by sliding window band logical
Ripple device filters the time domain waveform at stronger Bragg peaks, and dotted line represents adaptive threshold value T, less than this threshold value explanation Bragg peaks
CNR is relatively low.Comparative result such as Fig. 3 c between the instantaneous pollution phase place and actual value of PGA calculating) shown in, start bit is displayed in figure
Put larger with the difference between the calculated value in centre position and actual value, this kind of situation and Fig. 3 b) in position below thresholding be consistent,
Therefore explanation adaptive threshold can determine BraggCNR relatively low position in peak, and then reject the echo data of this position.Figure
It is 3d) PGA algorithms and the echo-signal frequency spectrum after the solution pollution of improvement PGA algorithms, PGA algorithms is shown in figure and PGA algorithms are improved
Ionosphere phase place pollution can be corrected, but improves the solution pollutant performance of PGA algorithms and be better than PGA algorithms, its echo-signal frequency spectrum
Make an uproar make an uproar bottom (70dB) about 10dB of the bottom (60dB) less than PGA algorithms solution pollution back echo signal spectrum, with pollution pre-echo signal
Frequency spectrum bottom (60dB) of making an uproar is consistent.This is because, the CNR relatively low position in Bragg peaks, the phase place that PGA algorithms are calculated is dirty
Dye is inaccurate, and then reduces its solution phase place pollutant performance, and improves PGA algorithms and there are no the problems referred to above, is answered the door using adaptive
Limit finds position CNR relatively low in Bragg peaks, and is rejected, and is recovered data are rejected well using CS algorithms, is increased with this
It is strong to understand phase place pollution capacity, so as to be effectively improved SOTHR target detection performances.
This project is not suitable for phase perturbation correction problem in ionosphere under the conditions of low CNR for PGA algorithms, has carried out algorithm
Research, it is proposed that improve PGA algorithms, it breaches the restriction of the high miscellaneous noise ratio scope of application.Added using actual measurement higher-frequency radar data
The mode of applying aspect pollution verifies the validity for improving PGA algorithms, and simulation result shows, improving PGA algorithms can effectively carry
Sea clutter Bragg peaks are taken, the position of low CNR in echo-signal, compared with PGA algorithms, radar is detected, rejects and repair exactly
After the improved PGA algorithms ionosphere corrections of echo-signal, radar noise substrate reduces about 10dB, with the thunder for not adding applying aspect pollution
It is consistent up to echo-signal noise floor.
Claims (3)
1. it is a kind of based on the ionosphere phase perturbation correction algorithm for improving PGA, it is characterised in that method and step is as follows：
Step 1：The strong sea clutter Bragg peaks of energy in echo-signal are extracted using sliding window bandpass filter, Bragg peaks are obtained
Time domain data s (n), according to radar transmitter frequency Bragg peaks frequency f is calculatedb, so as to s (n) is moved to zero-frequency, calculate
Go out phase errorn；
Step 2：Echo data phase place is corrected according to formula (5), and calculates self adaptation magnitude threshold T：
X '=Ψ x (5)
Wherein, in a CIT, the echo data of a certain range-azimuth resolution cell is used N by after the pollution of ionosphere phase place
The vector x of × 1 dimension is represented；N × 1 n dimensional vector n x ' represents the echo data after phasing；N × N-dimensional matrix Ψ represents ionosphere
Phasing matrix, and phase calibration is in the diagonal positions of matrix；
Self adaptation magnitude threshold T=meanamη, meanamRepresent the average of s (n) amplitude, η represent meet a certain certain errors value and
The scale factor of setting, is usually arranged as 0.6；
Step 3：According to formula (6) and self adaptation magnitude threshold T, data y after being rejected；
Step 4：Data y after rejecting are reconstructed according to formula (7) and formula (8), obtain the calculating of reconstruct data spectrum
Value θ 'y。
2. according to claim 1 based on the ionosphere phase perturbation correction algorithm for improving PGA, it is characterised in that：It is above-mentioned
In step 3, step is as follows：
Data Position of the amplitude less than self adaptation magnitude threshold T will be recorded in s (n), and pick the data of this position in x '
Remove, data y after rejecting are expressed as
Y=Fx '=F Ψ x (6)
Wherein y represents M × 1 (M ＜ N) n dimensional vector n, represents and rejects remaining data of the middle amplitudes of x ' less than T；F represents that M × N-dimensional is rejected
Matrix, is to extract M rows by row from N × N-dimensional diagonal matrix Ψ, and the line number of extraction is with that data point is not rejected in x ' is corresponding.
3. according to claim 1 based on the ionosphere phase perturbation correction algorithm for improving PGA, it is characterised in that：It is above-mentioned
In step 4, step is as follows：
Y=F Φ θy (7)
Wherein F represents that M × N-dimensional rejects matrix, and Φ represents N × N-dimensional IFFT matrix, i.e. Φ=IFFT [I], I represents N × N-dimensional pair
Angular moment battle array；θyComplete Doppler frequency spectrum is represented, including sea/land clutter, target and noise；Sparse letter U-shaped for one
Number, i.e., special aobvious point number U meets U ＜ ＜ N in frequency spectrum, if dictionary matrix Θ=F Φ meet limited equidistant characteristics and M >=Ο
(UlogN), by solving following convex problem, θyPrecise restoration in finite data y from after rejecting
min(||θ′y||1),subject to||Θθ′y-y||2≤τ (8)
Wherein θ 'yRepresent θyCalculated value；||·||pRepresent and calculate lpNorm, p=1,2；Min () is represented and is asked for minimum of a value, τ
Represent radar noise substrate.
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