CN111948621B - LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio - Google Patents
LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio Download PDFInfo
- Publication number
- CN111948621B CN111948621B CN202010673993.4A CN202010673993A CN111948621B CN 111948621 B CN111948621 B CN 111948621B CN 202010673993 A CN202010673993 A CN 202010673993A CN 111948621 B CN111948621 B CN 111948621B
- Authority
- CN
- China
- Prior art keywords
- signal
- order
- opt
- domain
- sequence
- 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.)
- Active
Links
Images
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
-
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
- G01S13/9017—SAR image acquisition techniques with time domain processing of the SAR signals in azimuth
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses an LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio. The method aims at that the signal to noise ratio of a signal received by a receiver is extremely low, the energy focusing of the LFM signal is realized by fractional Fourier transform to a sparse transform domain, and the transformation matrix X is realized by utilizing a variation step length p [u]Fast fine search of the best transformed p-order, especially with a filter W [ n ]]For X corresponding to the optimal p-order p_opt [u]Filtering the sequence signal, and obtaining a time domain signal sequence with the signal to noise ratio improved through convolution calculation by inverse fractional Fourier transform. Aiming at the characteristics of extremely low signal-to-noise ratio and extremely large calculated amount of SAR system signals, the method of the invention realizes the rapid improvement of the signal-to-noise ratio of a receiver through the FrFT transformation and the rapid fine search of variation step length, and simultaneously reduces the requirement on the system power of a transmitter.
Description
Technical Field
The invention relates to the technical field of satellite-borne Synthetic Aperture Radar (SAR) imaging signal processing, relates to effective signal-to-noise ratio improvement and recovery technology of a radar receiver under low signal-to-noise ratio, and particularly relates to an LFM radar signal processing method of optimal sparse domain transformation under extremely low signal-to-noise ratio.
Background
In the current stage of rapid development of space remote sensing technology, the system sky right becomes a new target for competing for all military strong countries in the world, wherein the space reconnaissance capability is an important expression of national firmness and army fight. Compared with the traditional optical remote sensing and hyperspectral remote sensing methods, the spaceborne synthetic aperture radar has the advantages of no limitation of illumination, no influence of weather, good penetrating power and long-distance imaging, has the excellent characteristics of all-weather, high resolution, active radiation source and the like, and better meets the requirements of dynamic strategic reconnaissance, so the spaceborne synthetic aperture radar is favored by various countries. The SAR radar load on the satellite platform is higher than the earth surface, the satellite platform belongs to the field of micro-nano satellites, the total weight of the satellite platform is about tens of kilograms, the load power in the platform is more demanding, in a radar system, the power is inversely proportional to the square of a target acting distance R, the SAR system is an active system and needs to receive echoes reflected by a target to carry out imaging processing, and finally the receiving power of the SAR system is inversely proportional to the fourth power of the target acting distance R, so that the signal-to-noise ratio of target echo signals received by a receiver directly determines the imaging effect quality of the SAR system based on the analysis.
Factors affecting the imaging quality of the SAR system are relatively many, including factors such as the system transmitting power, the radar operating wavelength, the transmitting beam oblique/side view angle, the target scattering coefficient, etc., generally, noise is filled in the natural environment where we are located, and it is generally considered that the system is mixed with white gaussian noise in engineering, therefore, the receiving system of the SAR system receives the echo LFM time domain signal reflected by the target, which contains a great amount of noise, but for the microsatellite, because of limited loading capacity, the transmitting power is greatly limited, meanwhile, for the target scattering coefficient, the parameter size cannot be controlled, and under the conditions of extremely severe environment and long space-borne SAR operating distance, the received LFM time domain echo signal cannot distinguish the information and the characteristics of the effective signal, even the effective signal is buried in the noise, so if the received echo signal can be correspondingly processed in the transform domain, the signal-to-noise ratio of the effective signal can be greatly improved, and the imaging quality of the SAR system can be substantially improved, and meanwhile, the power of the transmitting system can be reduced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an LFM radar signal processing method of an optimal sparse transform domain under extremely low signal-to-noise ratio.
The aim of the invention is realized by the following technical scheme:
an LFM radar signal processing method of optimal sparse domain transformation under extremely low signal-to-noise ratio comprises the following steps:
And 4, performing convolution calculation by utilizing fractional Fourier inverse transform IFRFT to obtain a time domain signal sequence x' n after the signal to noise ratio is improved.
In the above technical solution, further, the step 3 specifically includes:
step 3.1 setting s fixed step lengths delta 1 ，δ 2 ，…δ s And satisfy delta 1 ＞δ 2 ＞…δ s > 0, the transformed (u, p) coordinate system (0, 0) point is used as the initial searching starting point, and the larger stepping length delta is used for the first time 1 As a step search unit δ.
Step 3.2 according to the FrFT transformed signal sequence X p [u]For a two-dimensional matrix X p [u]The maximum value of the maximum value component in the u domain corresponding to the medium-p-order variable is quickly searched: first find X in u domain corresponding to each p-order variable p [u]And store it as a one-dimensional vector P u [p]Starting from the (0, 0) point, each step is searched for delta by comparing two adjacent points P u [p]And P u [p-δ]Obtain larger P u_max [p]And recording the corresponding P-order, repeating the above operation until the whole P u [p]Complete the whole search and record the largest P u_max [p]Value and corresponding p-order p opt_temp ；
Step 3.3 at maximum value P u_max [p]The center, ε, is the radius, and typically the value of ε is 3-5 times the value of δ, at [ p ] opt_temp -ε,p opt_temp +ε]Selecting delta with smaller stride in range 2 As step search unit delta, repeating step 3.2 until all s fixed step lengths are traversed;
step 3.4 recording the optimal p-order number p opt And corresponding u-domain FrFT transformed sequence signal X p_opt [u]。
Further, after the step 3 is completed, a filtering process is performed to design a filter W [ n ]]For the sequence signal X corresponding to the optimal p-order p_opt [u]Filtering is carried out, effective information is reserved, and meanwhile, related noise signals are filtered:
the filtered sequence signal is X p_opt_filter [u]；
X p_opt_filter [u]＝X p_opt [u]*W[n]；
And then step 4 is carried out.
Further, step 2 is to search the p-order transformation operator parameters of the received signal x [ n ] which can be focused in the specific rotation transformation domain by using the fractional Fourier transformation FrFT method, and the transformation kernel of the FrFT is adopted as follows:
wherein p is a transformation order, alpha=p.pi/2 is a rotation angle, u is a transverse axis in a coordinate base of a transformation domain after rotation, cot (·) represents a complementary cut, csc (·) represents a complementary cut, t is a time sampling sequence, and delta is a unit impulse function;
the expression of the sequence signal for the optimal p-order transform is:
wherein K is p [u,t]And x [ n ]]And performing dot multiplication operation.
Further, in the step 4, the expression of the time domain signal sequence x' [ n ] obtained by convolution calculation after the signal-to-noise ratio is improved by using the inverse fractional fourier transform IFRFT is:
wherein X is p_opt_filter [u]Representing the filtered optimal p-order sequence signal;
K -p [u,t]representing the ifraft inverse fractional fourier transform.
The beneficial effects of the invention are as follows:
in a dense and complex electromagnetic environment with extremely low signal-to-noise ratio, a receiver receives an LFM (linear frequency modulation) radar signal with extremely low signal-to-noise ratio, and two paths of I, Q orthogonal signals are formed after Hilbert transformation. The method is characterized in that the time domain characteristics of LFM signals under extremely low signal-to-noise ratio are quite unobvious, the received effective signals cannot be identified rapidly, in consideration of the fact that the received time domain signals are sparse in effective information and are LFM signals with specific modulation slope k, based on the fact that the received time domain signals are subjected to FrFT operation to form a peak matrix in a three-dimensional coordinate system, unknown signals are traversed by using twiddle factors in the FrFT conversion, when the twiddle factors in the FrFT conversion are matched with observed signals exactly, energy focusing effects are formed, pulse impulse is formed in the transformed u domain by the effective signals, rapid fine search of the whole transformation domain is achieved by using variation step length when the optimal peak value is searched, and the transformation domain time domain signals of the u domain corresponding to the energy focusing position are found by the method, so that the extremely low signal-to-noise ratio of the LFM signals is improved rapidly, meanwhile requirements on system power of a transmitter are reduced, the method can be effectively applied in engineering, and the improvement of the whole system performance is greatly facilitated.
Drawings
Fig. 1 is a schematic diagram of LFM echo signal time domain.
Fig. 2 is a schematic diagram of LFM echo signal time-frequency analysis.
Fig. 3 is a schematic diagram of a two-dimensional matrix of the (u, p) coordinate system after fractional fourier transform.
Fig. 4 is a transformation diagram of the FrFT rotation transform domain.
Fig. 5 is a schematic diagram of a u-domain sampling signal sequence of an optimal p-order FrFT transform.
FIG. 6 shows the filtered sequence signal X corresponding to the optimal p-order p_opt_filter [u]Schematic representation of the time domain signal.
Fig. 7 is a block diagram of very low signal-to-noise ratio LFM echo signal processing.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention utilizes the Chrip characteristic of LFM signals and searches the received signal x [ n ] by a fractional Fourier transform (FrFT) method]The p-order of effective energy focusing can be performed in a specific rotational transform domain. Received signal x [ n ]]After fractional Fourier transform, a corresponding p-order and u-domain two-dimensional transformation matrix X is formed p [u]In the new transformation coordinate system (u, p), each p-order corresponds to a corresponding FrFT transformed output sequence; gradually realizing transformation matrix X by using variable step length multiple times p [u]Fast fine search and finding of the best transform p-order: by setting the step search units delta= [ delta ] with different precision 1 ，δ 2 ，…δ s ]And satisfy delta 1 ＞δ 2 ＞…δ s > 0, in turn respectively by delta 1 ，δ 2 ，…δ s Is a sequential pair of transform matrices X p (u) searching, after each search is completed, finding X p Maximum value P of (u) u_max [p]And recording the corresponding P-order to the maximum value P u_max [p]The center, ε, is the radius, and typically the value of ε is 3-5 times the value of δ, at [ p ] opt_temp -ε,p opt_temp +ε]Selecting steps with smaller steps in rangeThe searching unit searches again until all the stepping searching units delta with different precision are completely traversed, and finally records the optimal p-order number p opt And corresponding u-domain FrFT transformed sequence signal X p_opt [u]. According to a fractional inverse Fourier transform (IFRFT) formula, a convolution calculation is performed to obtain a time domain signal sequence x' [ n ] after the signal to noise ratio is improved]. In addition, in obtaining the optimal p-order number p opt Corresponding u-domain FrFT transformed sequence signal X p_opt [u]After that, it can be combined with a filter W [ n ]]Multiplying to obtain a filtered sequence signal X p_opt_filter [u]So as to filter out relevant noise signals except the effective information while retaining the effective information, and further improve the signal to noise ratio.
In the embodiment, a computer simulation method is mainly adopted for verification, and all steps and conclusions are verified to be correct on MATLAB-R2014 a; the specific implementation steps are as follows:
step 1: LFM echo signal generation based on very low signal-to-noise ratio.
The parameters for simulation verification of the method are set as follows: light speed c=3×10 8 m/s; sampling rate F of signal s =400 MHz, the carrier frequency of the radar transmit pulse is F c =50 MHz, the bandwidth b=10 MHz of the radar transmission pulse, and the pulse width pw=5.12 μs of the radar transmission pulse. After the radar signal is reflected by the target, the signal-to-noise ratio of the received signal is-40 dB, and the time domain diagram is shown as the figure. The general expression for LFM radar signals is as follows:
x(t)＝Aexp(j2πf 0 t+jπμt 2 )+n(t)
wherein n (t) is 0 as the mean and sigma as the variance 2 Is a gaussian white noise of (c).
The received signal x (t) is subjected to time domain sampling to obtain a time domain digital signal sequence x [ N ] with the length of N, the time domain of the LFM received signal is shown in figure 1, and the time-frequency joint distribution is shown in figure 2.
step 2.1, searching a p-order transformation operator parameter of a received signal x [ n ] capable of focusing energy in a specific rotation transformation domain by using a fractional Fourier transform (FrFT) method, wherein a fractional Fourier transform calculation formula is shown as follows:
wherein the transformation core of the FrFT is:
where α=p·pi/2 is the rotation angle, p is the transform order, u is the horizontal axis in the transformed domain coordinate base after rotation, cot (·) represents the cose, and csc (·) represents the cose.
Step 2.2, receiving signal x [ n ]]After fractional Fourier transform, a corresponding p-order and u-domain two-dimensional transformation matrix X is formed p (u) each p-order corresponds to the corresponding FrFT transformed output sequence, FIG. 3 is a diagram of the transformed output corresponding to each p-order of the FrFT transformed (u, p) coordinate system, X p [u]The expression of (2) is shown in the following formula.
Fig. 4 is a transformation schematic diagram of an α -angle FrFT rotation transform domain, the transformation law of which obeys the following formula:
step 3.1, setting 3 fixed step lengths delta for each step search unit delta 1 ，δ 2 ，δ 3 And satisfy delta 1 >δ 2 >δ 3 > 0, typically we choose delta 1 ＝2δ 2 ＝4δ 3 And the transformed (u, p) coordinate system (0, 0) point is used as an initial searching starting point, and a larger step length delta is used for the first time 3 As a step search unit δ.
Step 3.2, according to the signal sequence X after the FrFT transformation p [u]For a two-dimensional matrix X p [u]And (5) carrying out rapid searching of the maximum value component in the u domain corresponding to the medium-p-order variable. First find the maximum value in the u-domain of each P-order variable pair and store it as a one-dimensional vector P u [p]Starting from the (0, 0) point, each step is searched for delta by comparing two adjacent points P u [p]And P u [p-δ]Obtain larger P u_max [p]And recording the corresponding P-order, repeating the above operation until the whole P u [p]Complete the whole search and record the largest P u_max [p]Value and corresponding p-order p opt_temp 。
Step 3.3, at maximum value P u_max [p]Is centered, ε is the radius, at [ p ] opt_temp -ε,p opt_temp +ε]Selecting delta with smaller stride in range 2 As a step search unit δ, step 3.2 is repeated until all steps δ are traversed.
Step 3.4, recording the optimal p-order number p opt And corresponding u-domain FrFT transformed sequence signal X p_opt [u]。
Through the rapid search method, the optimal p-order number is determined to be 1.016, and the corresponding sequence signal after u-domain FrFT transformation is shown in FIG. 5.
Mean value u, variance delta 2 Is distributed to follow N Gauss (t)～N(0，σ 2 ) After linear transformation, the power spectrum is still Gaussian white noise, and the power spectrum is uniform in the whole frequency bandDistribution.
The filtered sequence signal is denoted as X p_opt_filter (u)。
X p_opt_filter [u]＝X p_opt [u]*W[n]
FIG. 6 shows the filtered sequence signal X corresponding to the optimal p-order p_opt_filter [u]Calculating the signal sequence x' n after conversion]Improved signal-to-noise ratio capability:
the signal-to-noise ratio of the received signal x [ n ] is-40 dB, the signal power is 0.01dBW, and the signal-to-noise ratio calculation formula is adopted:
the noise power is available at 100dBW. Since the noise is Gaussian white noise whose power is uniformly distributed over the entire frequency band, the optimal p-order FrFT-transformed sequence signal X p_opt [u]After a window function with the width of 5 units is adopted, gaussian white noise outside a filter is filtered, the power of the filtered signal noise is changed into 0.24%, the signal-to-noise ratio of the whole system is improved to-13.867 dB, and compared with an original received echo signal, the signal-to-noise ratio of an LFM signal is improved by 26.124dB; the SAR radar LFM signal processing method (the processing flow is shown in figure 7) can greatly improve the signal-to-noise ratio of the received system signal and correspondingly reduce the requirement on the power of a transmitter system. The previous description of the disclosed examples is provided to enable any person skilled in the art to make or use the present invention. The non-detailed description of the invention is within the knowledge of a person skilled in the art.
Claims (4)
1. The LFM radar signal processing method for the optimal sparse domain transformation under the extremely low signal-to-noise ratio is characterized by comprising the following steps of:
step 1, the SAR system performs time domain sampling on the echo returned after the transmitted LFM radar pulse signal contacts an observed target, and obtains a time domain digital signal sequence with the length of N, namely a received signal x [ N ], n=1, 2 … N;
step 2. For the received signal x [ n ]]Performing fractional Fourier transform (FrFT) to form a corresponding p-order and u-domain two-dimensional transformation matrix X p [u]Each p-order corresponds to the corresponding FrFT transformed output sequence;
step 3, gradually realizing the transformation matrix X by using the variation step length p [u]Fast fine search and finding of the best transformed p-order to obtain the best p-order p opt And the corresponding u-domain FrFT transformed sequence signal X p_opt [u]The method specifically comprises the following steps:
step 3.1 setting s fixed step lengths delta 1 ，δ 2 ，…δ s And satisfy delta 1 ＞δ 2 ＞…δ s > 0, the transformed (u, p) coordinate system (0, 0) point is used as the initial searching starting point, and the larger stepping length delta is used for the first time 1 As a step search unit δ;
step 3.2 according to the FrFT transformed signal sequence X p [u]For a two-dimensional matrix X p [u]The maximum value of the maximum value component in the u domain corresponding to the medium-p-order variable is quickly searched: first find X in u domain corresponding to each p-order variable p [u]And store it as a one-dimensional vector P u [p]Starting from the (0, 0) point, each step is searched for delta by comparing two adjacent points P u [p]And P u [p-δ]Obtain larger P u_max [p]And recording the corresponding P-order, repeating the above operation until the whole P u [p]Complete the whole search and record the largest P u_max [p]Value and corresponding p-order p opt_temp ；
Step 3.3 at maximum value P u_max [p]The center, ε, is the radius, and typically the value of ε is 3-5 times the value of δ, at [ p ] opt_temp -ε,p opt_temp +ε]Selecting delta with smaller stride in range 2 As step search unit delta, repeating step 3.2 until all s fixed step lengths are traversed;
step 3.4 recording the optimal p-order number p opt And corresponding u-domain FrFT transformed sequence signal X p_opt [u]；
And 4, performing convolution calculation by utilizing fractional Fourier inverse transform IFRFT to obtain a time domain signal sequence x' n after the signal to noise ratio is improved.
2. The LFM radar signal processing method according to claim 1, wherein the filtering process is performed after the completion of step 3, and a filter W [ n ] is designed]For the sequence signal X corresponding to the optimal p-order p_opt [u]Filtering is carried out, effective information is reserved, and meanwhile, related noise signals are filtered:
the filtered sequence signal is X p_opt_filter [u]；
X p_opt_filter [u]＝X p_opt [u]*W[n]；
And then step 4 is carried out.
3. The LFM radar signal processing method according to claim 1, wherein the step 2 is characterized in that a fractional fourier transform FrFT method is used to find a p-order transform operator parameter of a received signal x [ n ] capable of focusing energy in a specific rotation transform domain, and a transform kernel of FrFT is:
wherein p is a transformation order, alpha=p.pi/2 is a rotation angle, u is a transverse axis in a coordinate base of a transformation domain after rotation, cot (·) represents a complementary cut, csc (·) represents a complementary cut, t is a time sampling sequence, and delta is a unit impulse function;
the expression of the sequence signal for the optimal p-order transform is:
wherein K is p [u,t]And x [ n ]]And performing dot multiplication operation.
4. The LFM radar signal processing method according to claim 2, wherein in the step 4, the expression of the time domain signal sequence x '[ n ], x' [ n ] after signal-to-noise ratio improvement obtained by convolution calculation using inverse fractional fourier transform IFRFT is:
wherein X is p_opt_filter [u]Representing the filtered optimal p-order sequence signal;
K -p [u,t]representing the ifraft inverse fractional fourier transform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010673993.4A CN111948621B (en) | 2020-07-14 | 2020-07-14 | LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010673993.4A CN111948621B (en) | 2020-07-14 | 2020-07-14 | LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111948621A CN111948621A (en) | 2020-11-17 |
| CN111948621B true CN111948621B (en) | 2023-06-27 |
Family
ID=73341091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010673993.4A Active CN111948621B (en) | 2020-07-14 | 2020-07-14 | LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111948621B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114866165B (en) * | 2021-06-29 | 2024-04-26 | 哈尔滨工业大学 | Rapid acquisition method for multi-band indoor signal distribution field |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1503293A2 (en) * | 2002-12-24 | 2005-02-02 | STMicroelectronics Belgium N.V. | Method and system for performing a Fast Fractional Fourier Transform. |
| CN101963662A (en) * | 2010-09-20 | 2011-02-02 | 北京理工大学 | Self-focusing preprocessing method based on short-time fractional order Fourier domain filter |
| CN102546499A (en) * | 2011-12-23 | 2012-07-04 | 北京理工大学 | Fractional-order channelized receiving method of real linear frequency modulation (LFM) signal |
| CN102879784A (en) * | 2012-10-25 | 2013-01-16 | 西安电子科技大学 | Unified imaging method for synthetic aperture radar (SAR) in four modes |
| CN103116155A (en) * | 2012-09-28 | 2013-05-22 | 北京理工大学 | Homotype radar same frequency interference suppression method used for ship formation condition |
| CN110559014A (en) * | 2019-08-28 | 2019-12-13 | 华南理工大学 | fractional order Fourier transform echo imaging method and system based on probe compensation |
| CN111239697A (en) * | 2020-02-11 | 2020-06-05 | 西北工业大学 | Multidimensional domain combined SAR broadband interference suppression method based on low-rank matrix decomposition |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10180495B2 (en) * | 2016-07-05 | 2019-01-15 | The Aerospace Corporation | Separating weak and strong moving targets using the fractional fourier transform |
-
2020
- 2020-07-14 CN CN202010673993.4A patent/CN111948621B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1503293A2 (en) * | 2002-12-24 | 2005-02-02 | STMicroelectronics Belgium N.V. | Method and system for performing a Fast Fractional Fourier Transform. |
| CN101963662A (en) * | 2010-09-20 | 2011-02-02 | 北京理工大学 | Self-focusing preprocessing method based on short-time fractional order Fourier domain filter |
| CN102546499A (en) * | 2011-12-23 | 2012-07-04 | 北京理工大学 | Fractional-order channelized receiving method of real linear frequency modulation (LFM) signal |
| CN103116155A (en) * | 2012-09-28 | 2013-05-22 | 北京理工大学 | Homotype radar same frequency interference suppression method used for ship formation condition |
| CN102879784A (en) * | 2012-10-25 | 2013-01-16 | 西安电子科技大学 | Unified imaging method for synthetic aperture radar (SAR) in four modes |
| CN110559014A (en) * | 2019-08-28 | 2019-12-13 | 华南理工大学 | fractional order Fourier transform echo imaging method and system based on probe compensation |
| CN111239697A (en) * | 2020-02-11 | 2020-06-05 | 西北工业大学 | Multidimensional domain combined SAR broadband interference suppression method based on low-rank matrix decomposition |
Non-Patent Citations (1)
| Title |
|---|
| FRFT与时域联合处理的欺骗干扰抑制方法;刘鑫 等;《现代防御技术》;20170831;第45卷(第04期);85-90 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111948621A (en) | 2020-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104111458B (en) | Compressed sensing synthetic aperture radar image-forming method based on dual sparse constraint | |
| US8665132B2 (en) | System and method for iterative fourier side lobe reduction | |
| CN109212526B (en) | Distributed array target angle measurement method for high-frequency ground wave radar | |
| CN110632594B (en) | Long-wavelength spaceborne SAR imaging method | |
| CN108983234B (en) | Terahertz ISAR imaging system and image reconstruction method based on back projection | |
| CN200944136Y (en) | Active external scaler of broadband synthetic aperture radar | |
| CN111142105A (en) | ISAR imaging method for complex moving target | |
| CN109507668B (en) | Double-base imaging method based on navigation satellite signals | |
| CN109917361B (en) | Three-dimensional unknown scene imaging method based on bistatic radar | |
| CN109856636B (en) | Curve synthetic aperture radar self-adaptive three-dimensional imaging method | |
| CN110487241B (en) | Method for extracting elevation control points of building area by satellite laser height measurement | |
| CN110208796B (en) | Scanning radar super-resolution imaging method based on singular value inverse filtering | |
| CN109507666A (en) | The sparse frequency band imaging method of ISAR based on off-network variation bayesian algorithm | |
| CN105738897A (en) | Signal distance image reconstruction method based on combined sparse characteristics | |
| CN110879391B (en) | Radar image data set manufacturing method based on electromagnetic simulation and missile-borne echo simulation | |
| CN111948621B (en) | LFM radar signal processing method for optimal sparse domain transformation under extremely low signal-to-noise ratio | |
| CN111190151A (en) | System parameter design and emission power optimization method of multi-mode small satellite SAR in scanning mode | |
| CN110361733B (en) | Medium orbit SAR (synthetic aperture radar) large squint imaging method based on time-frequency joint resampling | |
| CN107271995A (en) | The system sensitivity Optimization Design adjusted based on beam position | |
| CN111665501B (en) | MIMO radar two-dimensional imaging method based on improved CBP | |
| Cui et al. | DNN with similarity constraint for GEO SA-BSAR moving target imaging | |
| CN110133656B (en) | Three-dimensional SAR sparse imaging method based on decomposition and fusion of co-prime array | |
| CN109884621B (en) | Radar altimeter echo coherent accumulation method | |
| CN116719027A (en) | Star-machine double-base SAR imaging method in bidirectional sliding beam focusing mode | |
| CN110658502A (en) | Amplitude-phase error correction method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |