CN108549064A - External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins - Google Patents
External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins Download PDFInfo
- Publication number
- CN108549064A CN108549064A CN201810820543.6A CN201810820543A CN108549064A CN 108549064 A CN108549064 A CN 108549064A CN 201810820543 A CN201810820543 A CN 201810820543A CN 108549064 A CN108549064 A CN 108549064A
- Authority
- CN
- China
- Prior art keywords
- signal
- target
- echo signal
- frequency
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 238000004422 calculation algorithm Methods 0.000 title abstract description 5
- 210000001367 artery Anatomy 0.000 title abstract 4
- 210000003462 vein Anatomy 0.000 title abstract 4
- 238000009825 accumulation Methods 0.000 claims description 12
- 230000005855 radiation Effects 0.000 claims description 12
- 238000013508 migration Methods 0.000 claims description 8
- 230000005012 migration Effects 0.000 claims description 8
- 238000001228 spectrum Methods 0.000 claims description 8
- 230000009466 transformation Effects 0.000 claims description 8
- 230000006835 compression Effects 0.000 claims description 7
- 238000007906 compression Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000000034 method Methods 0.000 claims description 6
- 230000001427 coherent effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 230000011218 segmentation Effects 0.000 claims description 2
- 238000000926 separation method Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000002301 combined effect Effects 0.000 abstract 1
- 238000012544 monitoring process Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Classifications
-
- 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
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention belongs to external illuminators-based radar field of locating technology, are related to a kind of external sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins.The present invention realizes the improvement to the gain under doppler ambiguity by compensating doppler ambiguity in arteries and veins；The influence for reducing doppler ambiguity there are the system gain under Doppler frequency ambiguity to system output gain is improved by being combined effect with doppler ambiguity compensation between arteries and veins.
Description
Technical Field
The invention belongs to the technical field of radar positioning of external radiation sources, and relates to an external radiation source moving target detection method based on intra-pulse Doppler frequency fuzzy compensation.
Background
The radar (fig. 1) with an external radiation source, also called passive radar, is a passive radar system that does not emit electromagnetic wave signals, but uses the existing signals of broadcast, television, base station, etc. as radiation sources, and has the advantages of low-altitude penetration resistance, strong survivability, anti-stealth, etc., and is one of the hotspots of the current domestic research. The existing external radiation source radar system mostly adopts a single radiation source signal, and the available signal power is limited, the acting distance, the positioning precision, the detection performance and the like are limited. Therefore, expanding the number of illumination sources and increasing the available signal power to improve the detection performance of the system become an important development direction of the external radiation source radar.
DVB-S signals are satellite-based digital video broadcast transmission systems that specify a satellite digital broadcast modulation standard. DVB-S has become the mainstream standard internationally.
And adopting a QPSK modulation mode for the DVB-S signals. The steps of generating the DVB-S signal are mainly divided into two steps, wherein in the first step, an MPEG-2 code stream is adopted to encode a signal source so as to realize the multiplexing of the digital television signal after the multiplexing of video and audio. And the second step adopts the channel coding of the antecedent error correction coding and up-conversion.
The DVB-S signal may be represented as:
where T is the reciprocal of the symbol rate of the QPSK signal, g (T) is the impulse response of the square root raised cosine roll-in filter with a roll-in coefficient of α, with a duration of T, and α is typically 0.35. ω0Is the carrier angular frequency (c) of the carrier,is the phase of the nth symbol, and N is the number of symbols.
For a DVB-S signal with a complex envelope the above equation can be converted into:
wherein, the frequency domain transfer function of the raised cosine filter has according to definition:
wherein f isN＝1/2TsPhase ofAre approximately uniformly distributed under the values { pi/4, 3 pi/4, 5 pi/4, 7 pi/4 } and are independent of each other.
For the detection of a high-speed moving target by an external radiation source radar, the main problem to be solved is that due to long-time accumulation, the target moves to cause serious range migration, and the accumulation and the detection of target energy are influenced.
Disclosure of Invention
The invention aims to solve the problem of Doppler frequency ambiguity caused by undersampling due to high speed and low equivalent Pulse Repetition Frequency (PRF) of a target, and researches a coherent accumulation method for external radiation source moving target detection based on intra-pulse Doppler frequency ambiguity compensation. And compensating the coherent result with Doppler frequency ambiguity, and improving the coherent accumulation result.
For convenience of understanding, the technology adopted by the invention is introduced, and the invention is based on a Keystone transformation method and realizes coherent accumulation:
the Keystone transform algorithm steps (FIG. 2) are as follows:
mixing direct wave signals received by a monitoring antenna to a baseband to obtain st(t) mixing the echo signal received by the main antenna to the baseband to obtain sr(t) and having:
wherein, tau0For a target initial delay, aτIs the rate of change of the delay.
(II) simultaneously carrying out segmentation processing on the received direct wave data of the monitoring antenna and the received echo data of the main antenna:
wherein s ist(n) as received direct wave baseband data, the direct wave data is averaged to MsegSegments, each segment having a data length of LsegAdding a length T to the tail of each segment of datadmax0, m represents a slow time,indicating a fast time.
Wherein s isr(n) as received target echo baseband data, the echo data is averaged to MsegSegments, each segment having a data length of LsegAdding a length T to the tail of each segment of datadmaxOf echo data of (L)T＝Lseg+TdmaxFor the length of the added segment, m represents the slow time,indicating a fast time. Need to satisfyTdmax>τmaxfsWherein tau ismaxTime delay f corresponding to the maximum detection distance of the systemsFor the sampling rate, B is the signal bandwidth, c is the speed of light, vdmaxThe target maximum radial motion velocity.
(III) by calculatingAnd transforming the fast time dimension of the echo signal and the direct wave signal into a frequency domain, wherein F {. cndot.) represents fast Fourier transform, and F represents the frequency domain of the fast time dimension.
(IV) by calculatingAnd obtaining a correlation matrix of the echo signals and the direct wave signals, and referring to the steps (two), (three) and (four) as frequency domain pulse compression, wherein the steps are shown in figure 3.
(V) carrying out Keystone transformation:
to obtainAnd correcting the range migration of the transformed target echo.
(VI) when the Doppler frequency is blurred, correcting according to the blurring degree
Wherein F is the blur number.
(VII) performing inverse fast Fourier transform along the fast time dimension, and performing fast Fourier transform along the slow time dimension, namely calculating:
(eight) pairs of Ψ (f)dτ) to perform CFAR detection if Ψ (i, j)>If not, determining that the target does not exist in the position, and determining that mu is the CFAR threshold corresponding to the position.
The technical scheme of the invention is as follows:
and (I) assuming that the flying target is positioned at the space position O, and flying at a constant speed according to the speed v in the horizontal direction. At time t, the flight target position is O'. The distance between the transmitting party and the flying target is set as RT(t) the distance between the flight target and the receiving party is RR(t)。
R (t) ═ R for target echo signal propagation pathT(t)+RR(t), the time delay and doppler frequency can be expressed as:
where λ represents the wavelength of the transmitted signal and c is the speed of light.
For the time delay term taurTaylor series expansion is performed at t ═ 0, and neglecting the second order terms and the higher order terms yields:
τr(t)≈τr0+aτt
wherein, taur0For initial delay, aτIs the rate of change of the delay.
(II) the flying target is approximated to a point source target, the transmitting signal is reflected by the point source target and then reaches the receiving antenna, and then the echo signal can be expressed as:
se(t)＝sref(t-τr(t))
＝sr(t-τr0-aτt)exp(j2πfc(t-τr0-aτt))
wherein s isrefAnd (t) is a reference signal.
And (III) performing frequency mixing separation on the received echo signals and direct wave signals to respectively obtain baseband echo signals and direct wave signals, wherein the baseband echo signals can be expressed as:
(III) the direct wave signal and the echo signal after the frequency mixing are processed in most sections and are divided into MsegSegments, each segment having a length LsegAnd extending the length by Tdmax(generally, T is taken to bedmax＝Lseg) I.e. each segment has a length Lseg+Td. The time within a segment is called the fast time tfThe inter-period time is called the slow time tm. Wherein, the direct wave signal expands TdmaxA long 0 vector; echo signal expansion TdmaxA long data vector. Wherein the echo signal can be expressed as:
wherein,is the segmented echo signal.
Let fd＝-fcaτFor the Doppler shift of the echo signal to the target, it is assumed that there is an ambiguity in the Doppler frequency, the ambiguity coefficient is F, and the apparent frequency is F'dThe relationship between the three can be expressed as:
fd＝f'd+F·PRF
the echo signal may be rewritten as:
neglecting the apparent frequency f'dThe phase effect in the fast time, the above equation is simplified as:
and (IV) realizing pulse frequency domain compression by an FDPC method:
and (3) performing Fourier transform on the fast time dimension of the echo signal:
taking Fourier transform of the direct wave fast time dimension:
due to, echo signalsF & PRF frequency shift exists in fast time frequency domain becauseThis frequency shifts the direct wave to obtain Sref(f-F·PRF,tm) To realize the intra-pulse Doppler frequency shift fuzzy compensation and the echo signalConjugate multiplication is carried out to realize pulse frequency domain compression:
(V) utilizing Keystone transformation:
wherein f is the intra-pulse (fast time dimension) frequency t'mFor new variables introduced, the slow time dimension is virtualized. The data matrix S is then transformed by the Keystone transformi(f,tm) Can be expressed as:
the calculation can be quickly realized through Chirp-Z transformation (shown in figure 3), and the specific steps are as follows:
the method comprises the following specific implementation steps:
(1) m represents the pulse number of the radar receiving echo, the minimum integer satisfying the condition that L is more than or equal to 2M-1 and L is the integral power of 2 is selected, and theta is made0＝0，A0＝W0＝1，Then there is
(2) Generating L point sequences G (n) and H (n), and performing FFT to obtain G (k) and H (k), namely:
(3)and the first M points of v (n) are taken as the weight value to obtain
(4) The signal frequency spectrum after the distance migration compensation is Z (f, t'm)＝IFFT[X(zn)]。
Due to fd＝fcaτ＝f'd+F·PRF
Then the above formula is substituted:
order toA slow time compensation term for doppler frequency ambiguity, after compensation:
then, inverse Fourier transform is carried out on the formula in a fast time dimension to obtain:
Sc(tf,t'm)＝sI_im(tf-τ0)exp(-j2πfcτ0)
×exp(-j2πfcaτt'm)
wherein,the above formula indicates that range migration has been eliminated.
And (VI) performing fast Fourier transform along a slow time dimension, namely calculating:
T(τ,fd)＝F{Sc(tf,t'm)}|。
(seven) pairs of T (tau, f)d) Performing CFAR detection if T (tau, f)d)>μ(1≤i≤Mseg,1≤j≤Lseg) Judging that the target exists at the position, and estimating the Doppler frequency corresponding to the positionAnd time delayAccording to fd＝f'dCalculating and estimating a real Doppler frequency by + F-PRF; otherwise, determining that the target does not exist in the position, and μ is the CFAR threshold corresponding to the position.
The invention has the advantages that the gain under Doppler ambiguity is improved by compensating the intra-pulse Doppler ambiguity, the system gain under the condition of Doppler frequency ambiguity is effectively improved, and the influence of Doppler ambiguity on the system output gain is reduced.
Drawings
FIG. 1 is an external radiation source radar double-base model;
FIG. 2 is a schematic diagram of the Kestone algorithm;
FIG. 3 is a CZT schematic;
FIG. 4 is a flow chart of a conventional algorithm and a flow chart of the present invention;
FIG. 5 is a spectrum of a signal RD in the case of Doppler ambiguity;
FIG. 6 is a signal RD spectrum compensated for only the slow time dimension Doppler ambiguity based Doppler ambiguity case;
fig. 7 is a signal RD spectrum after doppler blur compensation for intra-pulse and full time based on doppler blur.
Detailed Description
The invention is further illustrated in the following with reference to the figures and examples
Examples
This example illustrates the detection of a target when the received target signal SNR is-35 dB.
The method of this example is shown in fig. 4, and the radar system is composed of a main antenna and a monitoring antenna as shown in fig. 1, wherein the monitoring antenna receives direct waves of a signal source, and the main antenna receives target echoes.
Considering the signal source using a satellite television signal (DVB-S signal), the symbol rate Rs27.5MHz, QPSK signal carrier frequency fc11.9GHz, receiver sampling rate fs55MHz, and 50ms accumulation time.
Assuming a target echo time delay of aboutThe target flies to the receiving station horizontally at a constant speed, and the time delay change rate a corresponding to the target motionτAt an actual doppler frequency of about f, 1e-6d＝-aτfc11900Hz, reference Signal-to-noise ratio SNRt＝20dB。
The detection method of an embodiment includes the steps of:
suppose that the direct wave signal received by the monitoring antenna has st(t)：
Wherein s isr(t) is a baseband signal, carrier fcT is the observation time, 11.9 GHz.
(II) the flying target is approximated to a point source target, the transmitting signal is reflected by the point source target and then reaches the receiving antenna, and then the echo signal can be expressed as:
wherein, aτ＝-1e-6，
Let fd＝-fcaτFor the Doppler shift of the echo signal to the target, it is assumed that there is an ambiguity in the Doppler frequency, the ambiguity coefficient is F, and the apparent frequency is F'dThe relationship between the three can be expressed as:
fd＝f'd+F·PRF
the incoming echo signal may be expressed as:
(III) the direct wave signal and the echo signal after the frequency mixing are processed most sectionally and written as the time of speedM form, divided intoseg687 segments, each segment length is Lseg4000 and extend the length by Tdmax3000 (generally, T is taken)dmax＝Lseg) I.e. each segment has a length Lseg+Td. The time within a segment is called the fast time tfThe inter-period time is called the slow time tm. Wherein, the direct wave signal expands TdmaxA long 0 vector; echo signal expansion TdmaxA long data vector. Wherein the echo signal can be expressed as:
wherein,is the segmented echo signal.
Let fd＝-fcaτFor the Doppler shift of the echo signal to the target, it is assumed that there is an ambiguity in the Doppler frequency, the ambiguity coefficient is F, and the apparent frequency is F'dThe relationship between the three can be expressed as:
fd＝f'd+F·PRF
wherein f isd＝11900Hz，F＝1，f'd＝-1851Hz，
The echo signal may be rewritten as:
neglecting the apparent frequency f'dThe phase effect in the fast time, the above equation is simplified as:
and (IV) realizing pulse frequency domain compression by an FDPC method:
and (3) performing Fourier transform on the fast time dimension of the echo signal:
taking Fourier transform of the direct wave fast time dimension:
due to, echo signalsF & PRF frequency shift exists in fast time frequency domain, so that S is obtained by frequency shifting direct waveref(f-F·PRF,tm) To realize the intra-pulse Doppler frequency shift fuzzy compensation and the echo signalConjugate multiplication is carried out to realize pulse frequency domain compression:
(V) utilizing Keystone transformation:
wherein f is the intra-pulse (fast time dimension) frequency t'mFor new variables introduced, the slow time dimension is virtualized. The data matrix S is then transformed by the Keystone transformi(f,tm) Can be expressed as:
the calculation can be quickly realized through Chirp-Z transformation (shown in figure 3), and the specific steps are as follows:
(1) m represents the pulse number of the radar receiving echo, the minimum integer satisfying the condition that L is more than or equal to 2M-1 and L is the integer power of 2 is selected, and theta is made0＝0，A0＝W0＝1，Then there is
(2) Generating L point sequences G (n) and H (n), and performing FFT to obtain G (k) and H (k), namely:
(3)and the first M points of v (n) are taken as the weight value to obtain
(4) The signal frequency spectrum after the distance migration compensation is Z (f, t'm)＝IFFT[X(zn)]。
Due to fd＝fcaτ＝f'd+F·PRF
Then the above formula is substituted:
order toA slow time compensation term for doppler frequency ambiguity, after compensation:
then, inverse Fourier transform is carried out on the formula in a fast time dimension to obtain:
Sc(tf,t'm)＝sI_im(tf-τ0)exp(-j2πfcτ0)
×exp(-j2πfcaτt'm)
wherein,the above formula indicates that range migration has been eliminated.
And (VI) performing fast Fourier transform along a slow time dimension, namely calculating:
T(τ,fd)＝F{Sc(tf,t'm)}|。
(seven) pairs of T (tau, f)d) Performing CFAR detection if T (tau, f)d)>μ(1≤i≤Mseg,1≤j≤Lseg) Judging that the target exists at the position, and estimating the Doppler frequency corresponding to the positionAnd time delayAccording to fd＝f'dCalculating and estimating a real Doppler frequency by + F-PRF; otherwise, determining that the target does not exist in the position, and μ is the CFAR threshold corresponding to the position.
Fig. 5 shows a simulation result in which doppler blur compensation is not performed in the embodiment, and the target accumulation gain at this time is 45.6073dB, and fig. 6 shows a target accumulation gain 49.7082dB, which can be obtained based on the spectrum of the doppler blur compensated signal RD in the slow time dimension only in the case of doppler blur. Fig. 7 shows a signal RD spectrum obtained by simultaneously performing doppler blur compensation on intra-pulse and full-time signals based on doppler blur, and the target accumulation gain at this time is 61.9178 dB. The invention realizes moving target accumulation based on intra-pulse Doppler fuzzy compensation and improves accumulation gain.
Claims (1)
1. The external radiation source moving target detection method based on intra-pulse Doppler frequency fuzzy compensation is characterized by comprising the following steps of:
s1, setting the flying target at a spatial position O, flying at a constant speed according to the speed v in the horizontal direction, and setting the flying target position to be O' at the time t; the distance between the transmitting party and the flying target is set as RT(t) the distance between the flight target and the receiving party is RR(t); the propagation path of the target echo signal is R (t) ═ RT(t)+RR(t), the time delay and doppler frequency can be expressed as:
wherein λ represents the wavelength of the transmitted signal and c is the speed of light;
for the time delay term taurTaylor series expansion is performed at t ═ 0, and neglecting the second order terms and the higher order terms yields:
τr(t)≈τr0+aτt
wherein, taur0For initial delay, aτIs the delay variation rate;
s2, the flying target is approximated to a point source target, the transmitting signal is reflected by the point source target and then reaches the receiving antenna, and the echo signal is expressed as:
se(t)＝sref(t-τr(t))
＝sr(t-τr0-aτt)exp(j2πfc(t-τr0-aτt))
wherein s isref(t) is a reference signal, fcIs the signal carrier frequency;
s3, performing mixing separation on the received echo signal and the received direct wave signal to respectively obtain a baseband echo signal and a direct wave signal, wherein the baseband echo signal is expressed as:
s4, for long-time coherent accumulation, calculating a fuzzy function by adopting a frequency domain pulse compression method, which specifically comprises the following steps:
s41, carrying out segmentation processing on the mixed direct wave signal and echo signal, and dividing into MsegSegments, each segment having a length LsegAnd extending the length by TdmaxI.e. each segment has a length Lseg+Td(ii) a The time within a segment is called the fast time tfTime of dayInterzone is called slow time tm(ii) a Wherein, the direct wave signal expands TdmaxA long 0 vector; echo signal expansion TdmaxA long data vector;
s42, the echo signal is expressed as follows according to the conditions set in step S41:
wherein,for the segmented echo signal, the fuzzy coefficient is F, and the apparent frequency is Fd'；
S43, let fd＝-fcaτSetting Doppler frequency and fuzzy coefficient F for Doppler frequency shift of echo signal to targetd' the relationship between the three is:
fd＝f′d+F·PRF
the echo signal is rewritten as:
neglecting the apparent frequency f'dThe phase effect in the fast time, the above equation is simplified as:
s44, respectively solving the Fourier transform of fast time for the two signals to obtain:
and (3) performing Fourier transform on the fast time dimension of the echo signal:
taking Fourier transform of the direct wave fast time dimension:
s45 echo signalF & PRF frequency shift exists in fast time frequency domain, so that S is obtained by frequency shifting direct waveref(f-F·PRF,tm) To realize the intra-pulse Doppler frequency shift fuzzy compensation and the echo signalConjugate multiplication is carried out to realize pulse frequency domain compression:
s5, utilizing Keystone transformation:
wherein f is the pulse internal frequency t'mFor the introduced variables, the slow time dimension is virtualized; the data matrix S is then transformed by the Keystone transformi(f,tm) Expressed as:
the calculation can be quickly realized through Chirp-Z transformation, and the specific steps are as follows:
s51, representing the pulse number of the radar received echo by M, selecting the minimum integer satisfying the condition that L is more than or equal to 2M-1 and L is the power of the integer of 2, and enabling theta to be theta0＝0，A0＝W0＝1，Then there is
S52, generating L point sequences G (n) and H (n), and performing FFT to obtain G (k) and H (k), namely:
S53、and the first M points of v (n) are taken as the weight value to obtain0≤n≤M-1；
S54, the signal frequency spectrum after the distance migration compensation is Z (f, t'm)＝IFFT[X(zn)]；
S6, the step S5 obtains the data matrix Si(f,tm) Data matrix after Keystone transformation:
due to fd＝fcaτ＝f′d+F·PRF
Then the above formula is substituted:
order toA slow time compensation term for doppler frequency ambiguity, after compensation:
then, inverse Fourier transform is carried out on the formula in a fast time dimension to obtain:
Sc(tf,t'm)＝sI_im(tf-τ0)exp(-j2πfcτ0)
×exp(-j2πfcaτt'm)
wherein u isI_im(tf)＝IFFT{|Sr(f-FPRF)|2Indicates that range migration has been eliminated;
s7, fast Fourier transform is carried out along the slow time dimension, namely calculation:
T(τ,fd)＝F{Sc(tf,t'm)}|
s8, for T (tau, f)d) Performing CFAR detection if T (tau, f)d)>μ(1≤i≤Nseg,1≤j≤Lseg) Judging that the target exists at the position, and estimating the Doppler frequency corresponding to the positionAnd time delayAccording to fd＝fd' + F PRF calculates the estimated true Doppler frequency; otherwise, determining that the target does not exist in the position, and μ is the CFAR threshold corresponding to the position.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810820543.6A CN108549064A (en) | 2018-07-24 | 2018-07-24 | External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810820543.6A CN108549064A (en) | 2018-07-24 | 2018-07-24 | External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108549064A true CN108549064A (en) | 2018-09-18 |
Family
ID=63492355
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810820543.6A Pending CN108549064A (en) | 2018-07-24 | 2018-07-24 | External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108549064A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111796267A (en) * | 2020-07-14 | 2020-10-20 | 哈尔滨工业大学 | Maneuvering turning target tracking-before-detection method based on pseudo-spectrum matched filtering |
| CN111948619A (en) * | 2020-06-30 | 2020-11-17 | 西安电子科技大学 | Passive cooperative detection method and system for target under irradiation of multi-type external radiation sources |
| CN112180342A (en) * | 2020-09-29 | 2021-01-05 | 中国船舶重工集团公司第七二四研究所 | Long-term accumulation observation parameter compensation method for offshore maneuvering target |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102628937A (en) * | 2012-04-20 | 2012-08-08 | 西安电子科技大学 | Radar detection method based on generalized keystone transformation and non-coherent accumulation |
| EP2524239A1 (en) * | 2010-01-11 | 2012-11-21 | BAE Systems PLC | Pulse radar range profile motion compensation |
| CN104062640A (en) * | 2014-06-30 | 2014-09-24 | 北京理工大学 | Quick implementation method for passive radar range migration compensation |
| CN104076351B (en) * | 2014-06-30 | 2017-02-08 | 电子科技大学 | Phase-coherent accumulation detection method for high-speed high maneuvering target |
| CN106597403A (en) * | 2016-11-29 | 2017-04-26 | 西安电子工程研究所 | High-velocity target coherent accumulation detection method based on piecewise compensation |
| CN108279403A (en) * | 2018-01-04 | 2018-07-13 | 电子科技大学 | Parallel Implementation method is converted based on the Keystone apart from framing |
-
2018
- 2018-07-24 CN CN201810820543.6A patent/CN108549064A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2524239A1 (en) * | 2010-01-11 | 2012-11-21 | BAE Systems PLC | Pulse radar range profile motion compensation |
| CN102628937A (en) * | 2012-04-20 | 2012-08-08 | 西安电子科技大学 | Radar detection method based on generalized keystone transformation and non-coherent accumulation |
| CN104062640A (en) * | 2014-06-30 | 2014-09-24 | 北京理工大学 | Quick implementation method for passive radar range migration compensation |
| CN104076351B (en) * | 2014-06-30 | 2017-02-08 | 电子科技大学 | Phase-coherent accumulation detection method for high-speed high maneuvering target |
| CN106597403A (en) * | 2016-11-29 | 2017-04-26 | 西安电子工程研究所 | High-velocity target coherent accumulation detection method based on piecewise compensation |
| CN108279403A (en) * | 2018-01-04 | 2018-07-13 | 电子科技大学 | Parallel Implementation method is converted based on the Keystone apart from framing |
Non-Patent Citations (3)
| Title |
|---|
| 冯斌: "DVB-S信号的无源雷达接收处理技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 张志鹏: "数字电视信号外辐射源雷达相参积累算法研究与GPU实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 杨鹏程 等: "机载外辐射源雷达空时处理中距离徙动校正算法研究", 《电子与信息学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111948619A (en) * | 2020-06-30 | 2020-11-17 | 西安电子科技大学 | Passive cooperative detection method and system for target under irradiation of multi-type external radiation sources |
| CN111948619B (en) * | 2020-06-30 | 2024-01-30 | 西安电子科技大学 | Passive cooperative detection method and system for targets under irradiation of multiple types of external radiation sources |
| CN111796267A (en) * | 2020-07-14 | 2020-10-20 | 哈尔滨工业大学 | Maneuvering turning target tracking-before-detection method based on pseudo-spectrum matched filtering |
| CN111796267B (en) * | 2020-07-14 | 2022-05-06 | 哈尔滨工业大学 | Maneuvering turning target tracking-before-detection method based on pseudo-spectrum matched filtering |
| CN112180342A (en) * | 2020-09-29 | 2021-01-05 | 中国船舶重工集团公司第七二四研究所 | Long-term accumulation observation parameter compensation method for offshore maneuvering target |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108931768B (en) | External radiation source weak target positioning method based on signal fusion | |
| CN108279403B (en) | Keystone transformation parallel implementation method based on distance framing | |
| CN108398676B (en) | External radiation source radar weak moving target detection method | |
| CN111736128B (en) | Phase-coherent accumulation method based on SKT-SIAF-MSCFT | |
| CN107819709B (en) | Method and device for detecting moving target | |
| CN103454632B (en) | One-station fixed frequency-modulated continuous wave double-base SAR imaging method | |
| CN108896976A (en) | A kind of coherent processing method for multichannel external illuminators-based radar | |
| Baczyk et al. | Reconstruction of the reference signal in DVB-T-based passive radar | |
| CN110187347B (en) | Large-width imaging method of geosynchronous orbit satellite-machine bistatic synthetic aperture radar | |
| CN108549064A (en) | External sort algorithm moving-target detection method based on Doppler frequency fuzzy compensation in arteries and veins | |
| CN110824438B (en) | Method and device for processing OFDM radar signals | |
| CN109100762B (en) | Bistatic radar sea surface moving target detection method based on Beidou satellite radiation source | |
| CN113391284A (en) | Temporary high-speed target detection method based on long-time accumulation | |
| CN116106900A (en) | Filter bank multi-carrier-based integrated signal design and processing method | |
| CN113093141B (en) | Multi-carrier frequency LFMCW radar signal synthesis processing method | |
| Hussain et al. | Segmented radon Fourier transform for long-time coherent radars | |
| Lang et al. | Reducing hardware requirements and computational effort for automotive OFDM radar systems | |
| del Arroyo et al. | Collecting and processing WiMAX ground returns for SAR imaging | |
| Wan et al. | Ground moving target imaging based on MSOKT and KT for synthetic aperture radar | |
| CN110658502A (en) | Amplitude-phase error correction method | |
| Hashempour et al. | Cyclic prefix-based OFDM ISAR imaging | |
| CN113884999A (en) | Cross-distance unit compensation method of external radiation source radar | |
| Galati et al. | Optimal Processing in Noise Radar: Implementation Problems | |
| Berthillot et al. | DVB-T Airborne Passive Radar: clutter block rejection | |
| Sun et al. | High-vivid uniformly-moving target deception jamming method against SAR-GMTI based on inverse omega-k algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180918 |
|
| WD01 | Invention patent application deemed withdrawn after publication |