CN115718287A - Radar clutter suppression method based on alternative projection - Google Patents
Radar clutter suppression method based on alternative projection Download PDFInfo
- Publication number
- CN115718287A CN115718287A CN202211367932.0A CN202211367932A CN115718287A CN 115718287 A CN115718287 A CN 115718287A CN 202211367932 A CN202211367932 A CN 202211367932A CN 115718287 A CN115718287 A CN 115718287A
- Authority
- CN
- China
- Prior art keywords
- clutter
- template
- doppler
- radar
- time domain
- 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
- 230000001629 suppression Effects 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012545 processing Methods 0.000 claims abstract description 13
- 230000005855 radiation Effects 0.000 claims abstract description 6
- 239000011159 matrix material Substances 0.000 claims description 25
- 239000013598 vector Substances 0.000 claims description 25
- 238000001228 spectrum Methods 0.000 claims description 16
- 238000001514 detection method Methods 0.000 claims description 7
- 230000000295 complement effect Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 15
- 230000008878 coupling Effects 0.000 abstract description 2
- 238000010168 coupling process Methods 0.000 abstract description 2
- 238000005859 coupling reaction Methods 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 9
- 230000003044 adaptive effect Effects 0.000 description 8
- 238000005070 sampling Methods 0.000 description 7
- 238000006243 chemical reaction Methods 0.000 description 3
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention belongs to the technical field of external radiation source radar signal processing, and particularly relates to a radar clutter suppression method based on alternative projection, aiming at the problem of coupling clutter suppression performance and memory space requirement of the existing algorithm, an iterative optimization scheme is realized, and the method is different from the traditional iterative clutter suppression scheme (CLEAN algorithm and the like).
Description
Technical Field
The invention belongs to the technical field of external radiation source radar signal processing, and particularly relates to a radar clutter suppression method based on alternative projection.
Background
Clutter suppression techniques aim to reveal clutter masked targets from suppressing clutter in the echo signal. The classical clutter suppression technology adopts a weighted tap delay line to construct an adaptive filter, thereby realizing clutter suppression, and the method mainly comprises the following steps: least Mean Square (LMS) adaptive filters, normalized LMS (NLMS) adaptive filters, recursive Least Squares (RLS) adaptive filters, and many other adaptive filters. In 2012, james palm and Stephen Searle compare and analyze various classical adaptive filters by taking the suppression performance of zero-frequency clutter and the algorithm operation complexity as standards. However, the adaptive filter has problems of slow convergence rate, high filtering order, large calculation amount, and the like, and when the clutter distribution area is wide, it is difficult for the adaptive filter algorithm to effectively process actual data.
Therefore, clutter suppression algorithms based on the concept of "cancellation" begin to get wide attention, and a processing flow of clutter parameter estimation, clutter template construction, clutter amplitude estimation and cancellation is gradually formed. In a related study, f.colone et al first proposed the Extended Cancellation Algorithm (ECA). However, the clutter in the actual data is continuously distributed, and it is difficult to achieve effective suppression of the clutter through a "discrete" algorithm form. To address this problem, olivier Rabaste et al propose the concept of infinite interpolation and achieve "continuous suppression" of the Doppler dimension. Thereafter, brian Day et al extended the infinite interpolation theory to non-periodic continuous waves while achieving continuous suppression in the range and doppler dimensions.
However, as the clutter suppression algorithm breaks through the suppression performance, the computational complexity and the computational resource demand thereof are increasing, especially the memory demand of the computer. In order to solve the problem of excessive memory requirements, methods such as an extended cancellation algorithm-counters (ECA-B), a Sliding window cancellation algorithm (ECA-S), a sub-carrier domain clutter suppression method (ECA-C), a Matching pursuit algorithm (MP), and a Generalized sub-band suppression algorithm (GSC) are proposed. However, although the complexity of the algorithm can be reduced by the conventional method, the clutter suppression performance is lost. Therefore, the invention provides a radar clutter suppression method based on alternative projection from the viewpoint of reducing complexity and keeping clutter suppression performance. Through verification, the method can effectively reduce the memory requirement on the computer and can obtain the clutter suppression performance superior to the existing improved method.
Disclosure of Invention
The invention aims to solve the problem of coupling clutter suppression performance and memory space requirements of the existing algorithm, and realizes an iterative optimization scheme, which is different from the traditional iterative clutter suppression scheme (CLEAN algorithm and the like). In order to realize the purpose of the invention, the adopted technical scheme is as follows:
a radar clutter suppression method based on dynamic alternative projection is characterized by comprising the following steps:
step 1: respectively acquiring echo signals and reference signals from a radar, obtaining a range-Doppler spectrum through pulse-Doppler processing, identifying clutter according to threshold detection, extracting time delay-Doppler parameters of all the clutter, and further obtaining a clutter time delay-Doppler parameter set
Wherein N is c Is the number of clutters, i is the index of the clutters, τ i 、f i Respectively time delay and Doppler frequency of the ith clutter;
step 2: constructing a clutter template matrix phi based on the distance-Doppler parameter set;
wherein,
x(τ i ,f i ) Representing time delay of τ i Doppler frequency of f i The clutter template;
and step 3: randomly arranging and selecting each column of the clutter template matrix to form Q clutter template submatrixes with the same row number and column number and without repeated column vectors
Wherein H is the current dynamic adjustment times, H is the total dynamic adjustment times, and H is more than or equal to 1 and less than or equal to H; q is the index of the clutter template submatrix, and Q is the total number of the clutter template submatrix;
and 4, step 4: performing m on echo signals on the basis of step 3 h Alternately projecting to obtain the h-th time dynamically adjusted time domain vector of the digital echo signal
Wherein, define y 0 The time domain vector of the digital echo signal when h =0, namely the original time domain vector;
and 5: let h = h +1; when h is generated>H, ending the process, and outputting the echo signal time domain vector y after clutter suppression H (ii) a When H is less than or equal to H, continuing to execute;
step 6: based on the time domain vector y obtained in step 4 h And the division scheme of the clutter template matrix phi is dynamically adjusted in a self-adaptive manner, so that Q clutter subspaces corresponding to the dynamically adjusted Q clutter template submatrices further tend to be orthogonal.
Step 4 of the present invention specifically comprises the following steps:
step 4 (a): generating an orthogonal projection matrix from an echo space to an orthogonal complement of a corresponding clutter subspace based on a clutter template submatrix
Wherein,
is from echo space to clutter template sub-matrix phi h,q An orthogonal projection matrix of an orthogonal complement space of the corresponding clutter subspace;
step 4 (b): at y h-1 On the basis of (1), repeating m h Wheel alternate projection, i.e.
Step 6 of the present invention specifically comprises the following steps:
step 6 (a): for the time domain vector y obtained in step 4 h Performing pulse-Doppler processing to obtain corresponding range-Doppler spectrum, and performing pulse-Doppler processing according to clutter delay-Doppler parameterSet to complete peak extraction, estimate N c Residual energy of individual clutters, i.e. obtaining sets of residual energies of clutters
Step 6 (b): set clutter residual energy as E i Sorting in descending order, and re-dividing the clutter template matrix into Q clutter template sub-matrices according to the re-sorted order;
step 6 (c): and skipping to the step 4.
Compared with the prior art, the radar clutter suppression method based on the alternative projection has the following advantages:
1. the compromise problem of clutter suppression performance and memory space requirement is converted into the compromise problem of computing time and memory space requirement, and the clutter suppression performance is guaranteed not to be limited by the size of a computer memory space;
2. compared with the traditional iterative clutter suppression method, the method provided by the invention can linearly converge on the overall least square solution by norm, and meanwhile, the dynamic adjustment mechanism provided by the invention can ensure that the convergence speed at a constant level is improved during linear convergence; in short, the radar clutter suppression method based on the alternative projection can obtain the least square solution, namely [ I-phi (phi) ] with high efficiency and accuracy H Φ) -1 Φ H ]y 0 。
Drawings
FIG. 1 is a schematic flow chart of a radar clutter suppression method based on alternative projection according to the present invention;
FIG. 2 is a range-Doppler spectrum generated by simulation data of an OFDM external radiation source radar provided by the invention;
FIG. 3 is a range-Doppler spectrum generated by clutter suppression of simulation data by the ECA-C method according to the present invention;
FIG. 4 is a range-Doppler spectrum generated after clutter suppression of simulation data by ECA-S method provided by the present invention;
FIG. 5 is a distance-Doppler spectrum generated by clutter suppression of simulation data provided by the present invention by a GSC method;
FIG. 6 is a distance-Doppler spectrum generated by clutter suppression of simulation data according to an alternative projection-based radar clutter suppression method provided by the present invention;
FIG. 7 is a Doppler slice plot of a target location in simulation data provided by the present invention, including results of the prior art method and the method of the present invention.
Detailed Description
The radar clutter suppression method based on the alternative projection according to the present invention will be further described with reference to the following specific examples and the accompanying drawings, but the embodiments of the present invention are not limited thereto.
In the embodiment, experimental verification is performed by using the simulation data of the OFDM external radiation source radar. As shown in fig. 1, the radar clutter suppression method based on the alternative projection provided by the present invention includes:
step 1: respectively acquiring echo signals and reference signals from an OFDM external radiation source radar, obtaining a time delay-Doppler spectrum through pulse-Doppler processing, identifying clutter according to threshold detection, extracting time delay-Doppler parameters of all the clutter, and further obtaining a clutter time delay-Doppler parameter set
The method specifically comprises the following steps:
step 1 (a): simulating a radar system according to a detection environment, constructing an echo model, and further generating a simulated digital echo signal y and a reference signal x;
the parameters of the radar system comprise a transmitting signal carrier frequency, an array element arrangement mode of a receiving array and the number of the array elements; the echo model comprises clutter, a target and noise, and the position distribution, the signal-to-clutter ratio and the signal-to-noise ratio of the clutter and the target need to be considered; the clutter distributed continuously in the detection environment can be equivalent to the superposition of a plurality of discrete clutter scattering points, and the noise adopts additive white Gaussian noise.
Step 1 (b): obtaining time delay-Doppler spectrum chi (tau, f) through pulse-Doppler processing, identifying clutter according to threshold detection, extracting time delay-Doppler parameter of all clutterTo a number of
Wherein epsilon 0 Is a threshold.
Step 2: constructing a clutter template matrix phi based on the time delay-Doppler parameter set, which specifically comprises the following steps:
step 2 (a): let the reference signal at the sampling instant t be
Wherein f is s Is the sampling frequency. Assuming that T is the coherent processing time duration, a reference signal vector x = [ x (0), x (1), …, x (N-1) can be constructed] T (ii) a Wherein N = Tf s 。
Step 2 (b): knowing the time delay τ i The reference signal vector x is time-shifted. To ensure when
The time-shift transformation can obtain the true value without error, and the invention utilizes the OFDM signal cyclic prefix to carry out the time-shift transformation. Is not provided with tau i f s ＝n a +n b (ii) a Wherein,
is an integer shift of n b E (0,1) is the fractional shift.
Step 2 (b). 1: time-shifting the integer sampling point for x, and recording the temporary variable as x (a) Then, there is,
x (a) ＝[x(-n a ),x(-n a +1),…,x(N-n a -1)] T
meanwhile, x (-n) is ensured through symbol synchronization and proper interception a ) Sample points corresponding to the start time of OFDM symbols, and x (a) Entirely containing L samples of the OFDM symbol. Thus, x (a) And may also be expressed as (i) a,
wherein x is l Represents the time domain vector of the l-th OFDM symbol, and satisfies,
x l ＝[x l (0),x l (1),…x l (N u +N g -1)] T
x l,g (n)＝x l,u (N u -N g +n),0≤n≤N g -1
wherein N is g Is the sampling length of the cyclic prefix, N u Is the sampling length of the effective symbol part, K is the number of subcarriers, s l,k Is the complex amplitude of the kth subcarrier of the ith OFDM symbol.
Step 2 (b). 2: for x (a) Time-shifting the non-integer sampling points, with the temporary variable denoted x (b) Then, there is,
wherein,
x l,u ＝[x l,u (0),…,x l,u (N u -1)] T
x l,u (-n b )＝[x l,u (-n b ),…,x l,u (N u -n b -1)] T
step 2 (c): knowing the Doppler frequency f i For x (b) Frequency conversion is carried out, and the final result after time delay-Doppler conversion is the frequency conversion
Wherein,
step 2 (d): for all i =1, …, N c And repeating steps 2 (b) -2 (c), if any,
and step 3: dividing the clutter template matrix into Q clutter template submatrices, and recording the submatrices as
Wherein H =1,2, …, H is the current dynamic adjustment times, and H is the total times of dynamic adjustment. The method specifically comprises the following steps: define r (i) → { q, j } as the sub-matrix Φ from the column index i to the qth clutter template of the clutter template matrix Φ h,q Bijective of column index j; and define r -1 (q, j) → i is the inverse of r (i); in addition, define
Then there is a list of the number of,
wherein the bijective r (i) may have any form.
And 4, step 4: performing m on echo signals on the basis of step 3 h Alternate projection is performed to obtain the h-th time dynamically adjusted digital echo signal time domain vector y h The method specifically comprises the following steps:
step 4 (a): sub-matrix phi of clutter template h,q The corresponding clutter subspace is denoted S h,q The space of orthogonal complement is noted
Will be provided with
Is recorded as from Hilbert space
To
A projection matrix of (i), i.e.
Then there is a change in the number of,
step 4 (b): definition of
An echo signal sequence; wherein, y 0 = y time domain vector of input echo signal, y H Is the output echo signal time domain vector. After the h-th dynamic adjustment, at y h-1 On the basis of (1), repeating m h Wheel alternate projection, i.e.
And 5: let h = h +1; when h is generated>H, ending the process, and outputting the echo signal time domain vector y after clutter suppression H (ii) a When H is less than or equal to H, continuing to execute;
step 6: based on the time domain vector y obtained in step 4 h The division scheme of the clutter template matrix phi is dynamically adjusted in a self-adaptive manner to ensure that the division scheme is as far as possible
Orthogonal to each other, specifically:
step 6 (a): for the time domain vector y obtained in step 4 h Performing pulse-Doppler processing to obtain corresponding delay-Doppler spectrum, performing peak extraction according to clutter delay-Doppler parameter set, and estimating N c Residual energy of individual clutters, i.e. obtaining sets of residual energies of clutters
Step 6 (b): similar to step 3, define e (q, j) → i as the sub-matrix Φ from the qth clutter template h,q Bijective from the column index j to the column index i of the clutter template matrix Φ; for the q-th 1 J th of sub-matrix of individual clutter templates 1 Column sum for q 2 J th of sub-matrix of individual clutter templates 2 Column, when q is 1 ≤q 2 And j is 1 ≤j 2 When the pressure is satisfied,
constructing a clutter template submatrix with bijections e (q, j) as mapping functions, i.e.
x q,j ＝x(τ e(q,j) ,f e(q,j) )
Step 6 (c): and skipping to the step 4.
The simulation signal used in this example is an OFDM signal with a bandwidth of 10KHz, which is sampled at a sampling frequency of 48 KHz. Assuming that the reference signal x has no interference or noise, the echo signal y includes ground clutter, sea clutter and ionospheric clutter, the target is a moving target, and the specific parameters are shown in table 1.
TABLE 1 simulation target and clutter parameters
As shown in fig. 2, the echo signal y is subjected to pulse-doppler processing, and the obtained delay-doppler spectrum has a uniform and random basis in the whole detection plane; the base is generated by clutter, is far higher than the noise base, and is a main factor for covering the target. Fig. 6a is a range-doppler spectrum obtained after 12 rounds of alternate projection are performed by the radar clutter suppression method based on alternate projection provided by the present invention, and fig. 6b is a range-doppler spectrum obtained after 24 rounds of alternate projection are performed; obviously, after 12 rounds of alternate projection, ionospheric clutter covered by ground sea clutter and the target are exposed (fig. 6 a), and after 12 rounds of alternate projection, ionospheric clutter is further suppressed, and the signal-to-noise ratio of the target is further increased (fig. 6 b). In addition, comparing the final output range-doppler spectrum of the algorithm shown in fig. 3 to fig. 6, it can be found that the radar clutter suppression method based on the alternative projection provided by the present invention can obtain the maximum output signal-to-clutter ratio.
In fig. 7, the longitudinal gray dashed line marks the position of the target in the doppler slice, and the remaining 5 transverse curves are the doppler slices obtained according to the original echo signal vector and the output vectors of different clutter suppression methods, respectively. It is apparent from fig. 7 that the radar clutter suppression method based on the alternative projection has the lowest basis, and the target energy loss is the smallest, with the largest output signal-to-noise ratio.
This description presents an exemplary embodiment for the purpose of illustrating the context and method of operation of the invention. The introduction of details in the examples is not intended to limit the scope of the claims but rather to aid in the understanding of the methods described herein. It will be apparent to those skilled in the art that various modifications, changes, derivations or substitutions of the steps of the exemplary embodiments can be made without departing from the inventive concept, and should be considered to be within the scope of the invention.
Claims (5)
1. A radar clutter suppression method based on dynamic alternative projection is characterized by comprising the following steps:
step 1: respectively acquiring digitized echo signals and reference signals from an external radiation source radar, obtaining a range-Doppler spectrum through pulse-Doppler processing, identifying clutter according to threshold detection, extracting time delay-Doppler parameters of all the clutter, namely obtaining a clutter time delay-Doppler parameter set
Wherein N is c Is the number of clutters, i is the index of the clutters, τ i 、f i Respectively time delay and Doppler frequency of the ith clutter;
step 2: constructing a clutter template matrix phi based on the distance-Doppler parameter set;
wherein,
x(τ i ,f i ) Representing time delay of τ i Doppler frequency of f i The clutter template;
and 3, step 3: randomly arranging and selecting each column of the clutter template matrix to form Q clutter template submatrixes with the same row number and column number and without repeated column vectors
Wherein H is the current dynamic adjustment times, H is the total dynamic adjustment times, and H is more than or equal to 1 and less than or equal to H; q is the index of the clutter template submatrix, and Q is the total number of the clutter template submatrix;
and 4, step 4: on the basis of step 3, namely after h dynamic adjustment, performing m on the echo signal h Alternately projecting to obtain the h-th time dynamically adjusted time domain vector of the digital echo signal
Wherein y is defined specifically 0 Is the digital echo signal time domain vector when h =0,i.e. the original time domain vector;
and 5: let h = h +1; when h is>When H is needed, the process is ended, and the echo signal time domain vector y after clutter suppression is output H (ii) a When H is less than or equal to H, continuing to execute;
step 6: based on the time domain vector y obtained in step 4 h The division scheme of the clutter template matrix phi is dynamically adjusted in a self-adaptive manner, so that Q clutter sub-spaces corresponding to the dynamically adjusted Q clutter template sub-matrices further tend to be orthogonal; and jumping to the step 4.
2. The method of claim 1, wherein the echo signal and the reference signal are related to the OFDM signal.
3. The method for radar clutter suppression based on dynamic alternative projection according to claim 1, wherein the step 4 specifically comprises:
step 4 (a): generating an orthogonal projection matrix from an echo space to an orthogonal complement of a corresponding clutter subspace based on a clutter template submatrix
Wherein,
is from echo space to clutter template sub-matrix phi h,q An orthogonal projection matrix of an orthogonal complement space of the corresponding clutter subspace;
step 4 (b): at y h-1 On the basis of (1), repeatedly carrying out
Wheel alternating projections, i.e.
4. The method for radar clutter suppression based on dynamic alternative projection according to claim 1, wherein the step 6 specifically comprises:
step 6 (a): for the time domain vector y obtained in step 4 h Performing pulse-Doppler processing to obtain corresponding range-Doppler spectrum, performing peak extraction according to clutter delay-Doppler parameter set, and estimating N c Residual energy of individual clutters, i.e. obtaining sets of residual energies of clutters
Step 6 (b): integrating clutter residual energy by E i Sorting the clutter templates in a descending order, and subdividing the clutter template matrix into Q clutter template sub-matrices according to the sorted order;
step 6 (c): and jumping to the step 4.
5. A method for radar clutter suppression based on dynamic alternative projection according to any of the claims 3 and 4, characterized in that it can converge on [ I- Φ (Φ) with norm in convergence H Φ) -1 Φ H ]And y, at least linear convergence is achieved in the convergence rate, and the linear convergence rate can be ensured to be continuously improved in a constant level.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211367932.0A CN115718287A (en) | 2022-11-03 | 2022-11-03 | Radar clutter suppression method based on alternative projection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211367932.0A CN115718287A (en) | 2022-11-03 | 2022-11-03 | Radar clutter suppression method based on alternative projection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115718287A true CN115718287A (en) | 2023-02-28 |
Family
ID=85254665
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211367932.0A Pending CN115718287A (en) | 2022-11-03 | 2022-11-03 | Radar clutter suppression method based on alternative projection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115718287A (en) |
-
2022
- 2022-11-03 CN CN202211367932.0A patent/CN115718287A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103716262B (en) | Channel estimation by time-domain parameter extraction | |
| CN110095765A (en) | High-resolution Doppler's reconstructing method of frequency-agile radar | |
| CN109061633B (en) | Signal design method of OFDM radar communication integrated airborne platform system | |
| CN110096956B (en) | Signal denoising method and device based on EEMD and permutation entropy second-order difference | |
| CN111147407B (en) | TMSBL underwater acoustic OFDM time-varying channel estimation method based on channel prediction | |
| CN109688074B (en) | Channel estimation method of OFDM system based on compressed sensing | |
| CN108268837B (en) | Radiation source fingerprint feature extraction method based on wavelet entropy and chaotic characteristics | |
| CN103176946A (en) | Sparse decomposition and denoising method facing block sparse signals | |
| CN107037409A (en) | MIMO radar waveform separation method based on compressed sensing | |
| CN114422303B (en) | Distance-speed joint fast estimation method for radar communication shared signal | |
| CN114785379A (en) | Underwater sound JANUS signal parameter estimation method and system | |
| CN114895251B (en) | OFDM radar communication integrated signal design method based on multivariable optimization | |
| Jia et al. | Convolutive blind source separation for communication signals based on the sliding Z-transform | |
| Delamou et al. | Deep learning-based estimation for multitarget radar detection | |
| CN108566347B (en) | Pilot frequency design method for double-selection sparse channel of multi-user OFDM system | |
| CN115718287A (en) | Radar clutter suppression method based on alternative projection | |
| CN116047516A (en) | CP-OFDM SAR imaging method combined with interpolation network | |
| CN110059401B (en) | OFDM system underwater sound channel impulse response reconstruction method | |
| CN114330441B (en) | Water sound JANUS signal identification method and system based on time spectrum and transfer learning | |
| CN115932808A (en) | Passive sonar intelligent detection method based on multi-feature fusion | |
| CN116612735A (en) | Underwater sound audio denoising method | |
| CN103176947A (en) | Multi-channel signal denoising method based on signal correlation | |
| CN113055318B (en) | Channel estimation method | |
| EP3712626B1 (en) | High-rate dft-based data manipulator and data manipulation method for high performance and robust signal processing | |
| CN111398912B (en) | Synthetic aperture radar interference suppression method based on tensor low-rank approximation |
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 |