CN104749562B - Passive bistatic radar signal processing method based on cyclic constant mould blind equalization - Google Patents
Passive bistatic radar signal processing method based on cyclic constant mould blind equalization Download PDFInfo
- Publication number
- CN104749562B CN104749562B CN201510127282.6A CN201510127282A CN104749562B CN 104749562 B CN104749562 B CN 104749562B CN 201510127282 A CN201510127282 A CN 201510127282A CN 104749562 B CN104749562 B CN 104749562B
- Authority
- CN
- China
- Prior art keywords
- signal
- bistatic radar
- direct wave
- constant modulus
- blind equalization
- 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.)
- Expired - Fee Related
Links
- 125000004122 cyclic group Chemical group 0.000 title claims abstract description 21
- 238000003672 processing method Methods 0.000 title claims abstract description 15
- 238000001914 filtration Methods 0.000 claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims abstract description 8
- 239000013598 vector Substances 0.000 claims description 10
- 230000003044 adaptive effect Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000021615 conjugation Effects 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 9
- 230000006978 adaptation Effects 0.000 abstract 1
- 230000006870 function Effects 0.000 description 10
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000001629 suppression Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035485 pulse pressure Effects 0.000 description 1
- 238000005070 sampling Methods 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 discloses a kind of passive bistatic radar signal processing method based on cyclic constant mould blind equalization, including step：S101：Recover direct-path signal in the signal received from the reference channel of passive bistatic radar using cyclic constant mould blind balance method；S102：According to the signal of the monitoring channel reception of passive bistatic radar, multipath adaptive-filtering process is carried out to the direct-path signal using Normalized least mean squares；S103：The signal obtained after the process of multipath adaptive-filtering and the direct-path signal are carried out the revised cross-correlation ambiguity function of Doppler to calculate, cross ambiguity function is obtained, and is detected target.The present invention can reduce the mean square error of constant mould blind equalization algorithm, improve self adaptation constringency performance, so as to improve the target detection capabilities of whole signal processing flow.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a passive bistatic radar signal processing method based on blind equalization of a cyclic constant modulus.
Background
The direct wave recovery is one of the key technologies of the target detection technology of the passive bistatic radar, and the purity of the direct wave is related to the convergence performance of a subsequent adaptive filtering algorithm and the false alarm condition of matched filtering. In the prior art, digital beam forming or blind equalization algorithm is generally adopted to realize direct wave signal recovery. The digital beam forming technology is not favorable for direct wave recovery in a complex multipath environment because the main lobe is wider. Due to the deconvolution characteristic, the constant modulus blind equalization algorithm can effectively suppress clutter of a main lobe and clutter of a side lobe, so that more attention is paid. But the convergence speed of the algorithm is slow. For a frame of data, the slow convergence causes difficulty in convergence of the adaptive filtering algorithm after the adaptive filtering algorithm is inputted thereafter.
Disclosure of Invention
The invention aims to solve the technical problem of providing a passive bistatic radar signal processing method based on the cyclic constant modulus blind equalization, which adopts the cyclic constant modulus blind equalization algorithm to recover direct waves, can reduce the mean square error of the constant modulus blind equalization algorithm, improves the self-adaptive convergence performance and further improves the target detection capability of the whole signal processing flow.
In order to solve the technical problem, the invention provides a passive bistatic radar signal processing method based on cyclic constant modulus blind equalization, which comprises the following steps:
s101: recovering a direct wave signal from a signal received by a reference channel of the passive bistatic radar by adopting a cyclic constant modulus blind equalization method;
s102: according to signals received by a monitoring channel of the passive bistatic radar, multipath self-adaptive filtering processing is carried out on the direct wave signals by adopting a normalized minimum mean square error algorithm;
s103: and performing Doppler correction on the signal obtained after multipath self-adaptive filtering and the direct wave signal to obtain a cross-correlation fuzzy function, and thus obtaining a cross-correlation fuzzy function, thereby detecting the target.
Further, the S101 specifically includes:
s1011: calculating a signal x received by a reference channel of a passive bistatic radarR(k)，
Wherein,in the formula,which represents a convolution operation, is a function of,as multipath clutter response of the reference channel, LξA (k) is the transmitted signal of the transmitting station, n2(k) Noise for the reference channel;
s1012: according to a signal x received by a reference channel of the passive bistatic radarR(k) Performing N by using constant modulus blind equalization iterative formulaxThe second iteration obtains the direct wave signal p (k) ═ fT(k)XR(k)，
Wherein, the constant modulus blind equalization iterative formula is f (k +1) ═ f (k) + η. XR *(k) e (k), wherein η is a step-size factor, and f (k) is a weight vector of a constant modulus blind equalizer, specificallyXR(k) For recursive vectors, XR(k)＝[xR(k) xR(k-1) … xR(k-Nf+1)]T，[·]*For conjugation, e (k) ═ p (k) [ gamma-p ]2(k)]Obtained by nonlinear transformation of direct wave signal p (k) output by a constant modulus blind equalizer,e {. represents solving a mathematical expectation;
s1013: intercepting the length of the direct wave signalDegree of NsAnd calculating a mean square error of the sample points,
wherein,
s1014: setting an empirical threshold as j, and setting the maximum loop iteration number as NmGet itIf ρ0>And the sum of j,<Nmand returning to execute the step S1012 after the iteration of the frame is completed, otherwise, executing the step S102.
Further, the S102 specifically includes:
s1021: calculating signal x received by monitoring channel of passive bistatic radarS(k)，
Wherein,in the formula,for monitoring the multipath clutter response of a channel, LλIs the number of multipath clutter, n1(k) To monitor the noise of the channel, xT(k) In order to be the target signal,τmfor bistatic time delay, fdmBistatic doppler, M is the target number;
s1022: according to the signal x received by the monitoring channel of the passive bistatic radarS(k) And said direct wave signal p (k) adopts normalized minimum mean square error algorithm to make multipath self-adaptive filtering treatment on said direct wave signal, the signal obtained after multipath self-adaptive filtering treatment is y (k),
wherein y (k) xS(k)-PH(k) F (k) in the formula (I),f (k) is NL× 1 filter coefficients, in particularα, β are small positive numbers, p (k) ═ p (k) … p (k-i) … p (k-N)L+1)]T。
Further, the S103 specifically includes:
performing cross-correlation fuzzy function calculation after Doppler correction on a signal y (k) obtained after multipath self-adaptive filtering and the direct wave signal p (k), specifically, dividing data into sections with the length of L and performing cross-correlation operation to obtain a cross-correlation fuzzy function zeta (kappa, L),
wherein,where κ is the discrete time delay and l is the discrete doppler frequency.
The implementation of the invention has the following beneficial effects: the invention uses the output of the blind equalization algorithm as the input of the blind equalization algorithm to carry out cycle processing, and determines whether the cycle is stopped or not by judging the mean square error, thereby effectively improving the convergence capability of the algorithm and reducing the mean square error of the constant modulus blind equalization algorithm. The signals processed by the cyclic blind equalization algorithm are sent to the adaptive filtering direct wave and clutter suppression, the adaptive convergence performance is obviously improved, and therefore the target detection capability of the whole signal processing flow is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart illustrating steps of a passive bistatic radar signal processing method based on cyclic constant modulus blind equalization according to an embodiment of the present invention;
fig. 2 is a signal processing flow chart of an embodiment of a passive bistatic radar signal processing method based on cyclic constant modulus blind equalization provided by the invention;
FIG. 3 is a signal processing flow diagram of a radar received signal processing method based on constant modulus blind equalization in the prior art;
FIG. 4 is a prior art restored direct wave signal modulus of FIG. 3;
FIG. 5 is a diagram of the modulus change of the output signal of FIG. 3 after the data obtained from the constant modulus blind equalization process is processed by the normalized least mean square algorithm;
FIG. 6 is a target detection plane of the prior art data processing of FIG. 3;
FIG. 7 is the recovered direct wave signal modulus of the present invention of FIG. 2;
FIG. 8 is a diagram of the modulus change of the output signal of FIG. 2 after the data obtained by the blind equalization with a cyclic constant modulus is processed by the normalized least mean square algorithm;
FIG. 9 is a target detection plane of the data processing of the present invention of FIG. 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flowchart illustrating steps of an embodiment of a passive bistatic radar signal processing method based on cyclic constant modulus blind equalization provided by the present invention, and fig. 2 is a flowchart illustrating signal processing of an embodiment of a passive bistatic radar signal processing method based on cyclic constant modulus blind equalization provided by the present invention, as shown in fig. 1 and fig. 2, the present invention includes steps of:
s101: and recovering the direct wave signal from the signal received by the reference channel of the passive bistatic radar by adopting a cyclic constant modulus blind equalization method.
Specifically, step S101 specifically includes:
s1011: calculating a signal x received by a reference channel of a passive bistatic radarR(k)，
Wherein,in the formula,which represents a convolution operation, is a function of,as multipath clutter response of the reference channel, LξA (k) is the transmitted signal of the transmitting station, n2(k) Noise for the reference channel;
s1012: according to a signal x received by a reference channel of the passive bistatic radarR(k) Performing N by using constant modulus blind equalization iterative formulaxThe second iteration obtains the direct wave signal p (k) ═ fT(k)XR(k)，
Wherein, the constant modulus blind equalization iterative formula is f (k +1) ═ f (k) + η. XR *(k) e (k), wherein η is a step-size factor, and f (k) is a weight vector of a constant modulus blind equalizer, specificallyXR(k) In order to be a recursive vector, the vector,XR(k)＝[xR(k) xR(k-1) … xR(k-Nf+1)]T，[·]*for conjugation, e (k) ═ p (k) [ gamma-p ]2(k)]Obtained by nonlinear transformation of direct wave signal p (k) output by a constant modulus blind equalizer,e {. represents solving a mathematical expectation;
s1013: intercepting the length of the direct wave signal as NsAnd calculating a mean square error of the sample points,
wherein,
s1014: setting an empirical threshold as j, and setting the maximum loop iteration number as NmGet itIf ρ0>And the sum of j,<Nmand returning to execute the step S1012 after the iteration of the frame is completed, otherwise, executing the step S102.
S102: and according to the signals received by the monitoring channel of the passive bistatic radar, performing multipath self-adaptive filtering processing on the direct wave signals by adopting a normalized minimum mean square error algorithm.
Specifically, step S102 specifically includes:
s1021: calculating signal x received by monitoring channel of passive bistatic radarS(k)，
Wherein,in the formula,for monitoring the multipath clutter response of a channel, LλIs the number of multipath clutter, n1(k) To monitor the noise of the channel, xT(k) In order to be the target signal,τmfor bistatic time delay, fdmBistatic doppler, M being the target number, a (k) being the signal transmitted by the transmitting station;
s1022: according to the signal x received by the monitoring channel of the passive bistatic radarS(k) And said direct wave signal p (k) adopts normalized minimum mean square error algorithm to make multipath self-adaptive filtering treatment on said direct wave signal, the signal obtained after multipath self-adaptive filtering treatment is y (k),
wherein y (k) xS(k)-PH(k) F (k) in the formula (I),f (k) is NL× 1 filter coefficients, in particularα, β are small positive numbers, p (k) ═ p (k) … p (k-i) … p (k-N)L+1)]T。
S103: and performing Doppler correction on the signal obtained after multipath self-adaptive filtering and the direct wave signal to obtain a cross-correlation fuzzy function, and thus obtaining a cross-correlation fuzzy function, thereby detecting the target.
Specifically, step S103 specifically includes:
performing cross-correlation fuzzy function calculation after Doppler correction on a signal y (k) obtained after multipath self-adaptive filtering and the direct wave signal p (k), specifically, dividing data into sections with the length of L and performing cross-correlation operation to obtain a cross-correlation fuzzy function zeta (kappa, L),
wherein,wherein κ is offThe dispersion delay, l, is the discrete doppler frequency.
A radar signal processing method based on constant modulus blind equalization generally adopted in the prior art is shown in fig. 3, and the differences from the present invention are as follows: the direct wave is restored by directly adopting a constant modulus blind equalization algorithm. In order to verify the performance of the invention, the performance of the algorithm is explained by taking a passive bistatic radar based on frequency modulation broadcasting as an example, the bandwidth of a frequency modulation broadcasting system is set to be 50kHz, and the sampling rate is set to be 200 kHz. The signal integration time was taken to be 2 s. The weight vector length equalizer is set to 31. Assume that the 3 targets have bistatic delay and doppler parameters of (48 point, -153Hz), (154 point, -103Hz), (230 point, 60Hz), respectively. The signal-to-noise ratio (SCNR) was set to-48 dB and the signal-to-noise ratio was-2 dB (before pulse pressure). The cluttered channel from the transmission to the reference channel is as follows
λ0＝1 λ7＝0.17ej0.24λ9＝0.15e-j2.8
The clutter channel from the transmit to the monitor channel is as follows
Taking the number of iterations NxFor 10000 points, the mean square error ρ is calculated. The length of the weight vector of the minimum mean square error algorithm is set to be 40, and the iteration step length is set to be 0.001. Selecting a threshold of 45, and calculating a sample N of a mean square errors＝1000。
The results of the algorithm process flow based on constant modulus blind equalization are shown in fig. 4-6. Where fig. 4 is the modulus of the signal output during convergence of the constant modulus blind equalization algorithm, and is also the modulus of the signal output to the LMS algorithm. The output signal modulus of the least mean square algorithm of fig. 5 shows that the algorithm convergence is not stable. As can be seen from fig. 6, the detected signal-to-noise ratios of the three targets are not high.
After the method is adopted, the constant modulus blind equalization algorithm undergoes secondary loop iteration, and the secondary loop output result is shown in figure 7. It can be seen that the module value fluctuation of the algorithm convergence is obviously reduced. The modulus of the output signal after the input of the minimum mean square error algorithm is shown in fig. 8. As can be seen from fig. 8, the output mean square error is significantly less than that of fig. 4 of the original algorithm. The target detection effect of the invention is shown in figure 9, and compared with the original algorithm, the signal-to-noise ratio is improved by 7 dB. The detection of the three targets is clearly visible.
Therefore, the implementation of the invention has the following beneficial effects: the invention uses the output of the blind equalization algorithm as the input of the blind equalization algorithm to carry out cycle processing, and determines whether the cycle is stopped or not by judging the mean square error, thereby effectively improving the convergence capability of the algorithm and reducing the mean square error of the constant modulus blind equalization algorithm. The signals processed by the cyclic blind equalization algorithm are sent to the adaptive filtering direct wave and clutter suppression, the adaptive convergence performance is obviously improved, and therefore the target detection capability of the whole signal processing flow is improved.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A passive bistatic radar signal processing method based on cyclic constant modulus blind equalization is characterized by comprising the following steps:
s101: recovering a direct wave signal from a signal received by a reference channel of the passive bistatic radar by adopting a cyclic constant modulus blind equalization method;
s102: according to signals received by a monitoring channel of the passive bistatic radar, multipath self-adaptive filtering processing is carried out on the direct wave signals by adopting a normalized minimum mean square error algorithm;
s103: performing Doppler correction on the signal obtained after multipath adaptive filtering and the direct wave signal to obtain a cross-correlation fuzzy function, and detecting a target;
the S101 specifically includes:
s1011: calculating a signal x received by a reference channel of a passive bistatic radarR(k)，
Wherein,in the formula,which represents a convolution operation, is a function of,as multipath clutter response of the reference channel, LξA (k) is the transmitted signal of the transmitting station, n2(k) K is the noise of the reference channel and is a discrete time variable;
s1012: according to a signal x received by a reference channel of the passive bistatic radarR(k) Performing N by using constant modulus blind equalization iterative formulaxThe second iteration obtains the direct wave signal p (k) ═ fT(k)XR(k)，
Wherein, the constant modulus blind equalization iterative formula is f (k +1) ═ f (k) + η. XR *(k) e (k), wherein η is a step-size factor, and f (k) is a weight vector of a constant modulus blind equalizer, specificallyXR(k) For recursive vectors, XR(k)＝[xR(k) xR(k-1) … xR(k-Nf+1)]T，[·]*For conjugation, e (k) ═ p (k) [ gamma-p ]2(k)]Obtained by nonlinear transformation of direct wave signal p (k) output by a constant modulus blind equalizer,e {. tableThe expression takes the mathematical expectation, NfIs the length of the weight vector f (k) of the constant modulus blind equalizer;
s1013: intercepting the length of the direct wave signal as NsAnd calculating a mean square error of the sample points,
wherein,
s1014: setting an empirical threshold as j, and setting the maximum loop iteration number as NmGet itIf ρ0Is greater than and j < NmIf so, the process returns to the step S1012 after the iteration of the frame is completed, otherwise, the step S102 is performed.
2. The passive bistatic radar signal processing method based on cyclic constant modulus blind equalization as claimed in claim 1, wherein said S102 specifically comprises:
s1021: calculating signal x received by monitoring channel of passive bistatic radarS(k)，
Wherein,in the formula,for monitoring the multipath clutter response of a channel, LλIs the number of multipath clutter, n1(k) To monitor the noise of the channel, xT(k) In order to be the target signal,τmfor bistatic time delay, fdmBistatic doppler, M being the target number, k being the discrete time variable;
s1022: according to the signal x received by the monitoring channel of the passive bistatic radarS(k) And said direct wave signal p (k) adopts normalized minimum mean square error algorithm to make multipath self-adaptive filtering treatment on said direct wave signal, the signal obtained after multipath self-adaptive filtering treatment is y (k),
wherein y (k) xS(k)-PH(k) F (k) in the formula (I),f (k) is NL× 1 filter coefficients, in particularα, β are small positive numbers, p (k) ═ p (k) … p (k-i) … p (k-N)L+1)]T。
3. The passive bistatic radar signal processing method based on cyclic constant modulus blind equalization as claimed in claim 2, wherein said S103 specifically comprises:
performing cross-correlation fuzzy function calculation after Doppler correction on a signal y (k) obtained after multipath self-adaptive filtering and the direct wave signal p (k), specifically, dividing data into sections with the length of L and performing cross-correlation operation to obtain a cross-correlation fuzzy function zeta (kappa, L),
wherein,where κ is the discrete time delay, l is the discrete doppler frequency, and k is the discrete time variable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510127282.6A CN104749562B (en) | 2015-03-23 | 2015-03-23 | Passive bistatic radar signal processing method based on cyclic constant mould blind equalization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510127282.6A CN104749562B (en) | 2015-03-23 | 2015-03-23 | Passive bistatic radar signal processing method based on cyclic constant mould blind equalization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104749562A CN104749562A (en) | 2015-07-01 |
| CN104749562B true CN104749562B (en) | 2017-03-15 |
Family
ID=53589538
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510127282.6A Expired - Fee Related CN104749562B (en) | 2015-03-23 | 2015-03-23 | Passive bistatic radar signal processing method based on cyclic constant mould blind equalization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104749562B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153178A (en) * | 2017-05-18 | 2017-09-12 | 西安电子科技大学 | External illuminators-based radar reference signal contains object detection method during multi-path jamming |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2645297C1 (en) * | 2017-03-22 | 2018-02-20 | Дмитрий Сергеевич Войнов | Method for determining location of a quasi-continuous broadband source of radio-emission by mobile complex of radio-technical supervision |
| CN108508413B (en) * | 2017-09-25 | 2020-03-13 | 中国人民解放军国防科技大学 | Target detection method based on probability statistics under low signal-to-noise ratio condition |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2397431A1 (en) * | 2002-08-09 | 2004-02-09 | Andrew Lohbihler | Method and apparatus for a wireless position sensing interface device employing spread spectrum technology of one or more radio transmitting devices |
| FR2909773B1 (en) * | 2006-12-12 | 2009-01-30 | Thales Sa | MULTIVOYAL PASSIVE RADAR PROCESSING METHOD OF FM OPPORTUNITY SIGNAL. |
| CN102707271B (en) * | 2012-05-31 | 2014-04-16 | 武汉大学 | System utilizing CMMB (China Mobile Multimedia Broadcasting) signal to detect target and method thereof |
| CN104237859B (en) * | 2014-08-27 | 2017-01-25 | 武汉大学 | Method for achieving external illuminator radar multi-channel time domain clutter suppression by means of GPU |
-
2015
- 2015-03-23 CN CN201510127282.6A patent/CN104749562B/en not_active Expired - Fee Related
Non-Patent Citations (1)
| Title |
|---|
| Space–time constant modulus algorithm for multipath removal on the reference signal exploited by passive bistatic radar;F. Colone et al.;《IET Radar, Sonar & Navigation》;20090610;第3卷(第3期);第253-264页 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153178A (en) * | 2017-05-18 | 2017-09-12 | 西安电子科技大学 | External illuminators-based radar reference signal contains object detection method during multi-path jamming |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104749562A (en) | 2015-07-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104777466B (en) | Passive bistatic radar signal processing method based on dimension reduction constant modulus blind equalization | |
| CN107666361B (en) | The adaptive cancellation method and device of multicarrier passive intermodulation interference | |
| CN108599809B (en) | Full duplex self-interference signal number removing method and device | |
| CN104749562B (en) | Passive bistatic radar signal processing method based on cyclic constant mould blind equalization | |
| WO2014190572A1 (en) | Blind spectrum sensing method and device based on fast fourier transform | |
| CN101729461A (en) | System and method for eliminating single-frequency interference and multi-frequency interference | |
| CN102521503A (en) | LMS (least mean square) based helicopter rotor wing gap time predicting method and device utilizing same | |
| CN103346756A (en) | Space-time self-adaptive filtering method and device | |
| WO2011153727A1 (en) | Blind self-adaptive filtering method, apparatus and broadband transmission system for eliminating narrowband interference | |
| US7173991B2 (en) | Methods and apparatus for spectral filtering channel estimates | |
| CN110677796A (en) | Audio signal processing method and hearing aid | |
| WO2002078312A1 (en) | System for convolutional echo cancellation by iterative autocorrelation | |
| WO2011010107A2 (en) | Receiver for wireless transmission | |
| CN101808058B (en) | Single carrier/multi-carrier share receiver and signal processing method thereof | |
| JP4809395B2 (en) | Wireless communication apparatus and interference power reduction method | |
| JP4772462B2 (en) | Receiving machine | |
| Tafreshi et al. | Software defined radio implementation of adaptive nonlinear digital self-interference cancellation for mobile inband full-duplex radio | |
| CN114611542A (en) | Signal noise reduction processing method and communication device | |
| US9973174B2 (en) | Method and apparatus for determining stability factor of adaptive filter | |
| WO2002078313A1 (en) | System for convolutional echo cancellation by iterative autocorrelation | |
| EP3712626B1 (en) | High-rate dft-based data manipulator and data manipulation method for high performance and robust signal processing | |
| CN105093191A (en) | Direct wave recovery method for external radiation source radar | |
| CN103929150B (en) | Weight vector updating method for sub-band adaptive filter | |
| US20110014887A1 (en) | Interference cancellation | |
| EP3148101B1 (en) | Receiving device and receiving method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Xikang Road Gulou District of Nanjing City, Jiangsu Province, No. 1 Applicant after: Hohai University Address before: Xikang Road Jiangning District of Nanjing City, Jiangsu Province, No. 1 210098 Applicant before: Hohai University |
|
| COR | Change of bibliographic data | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170315 Termination date: 20200323 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |