CN103095357A - Intelligence antenna self-adaptive beam forming method - Google Patents
Intelligence antenna self-adaptive beam forming method Download PDFInfo
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Abstract
The invention discloses an intelligence antenna self-adaptive beam forming method which includes the steps of a set membership filtering algorithm, a partial updating algorithm, an affine projection algorithm and a constraint self-adaptive filtering algorithm, wherein the set membership filtering algorithm is used for achieving an SM-CAP algorithm, the partial updating algorithm is used for achieving an SM-PU-NLMS algorithm or an SM-SPU-CA algorithm, the affine projection algorithm is used for achieving an SM-DS-CAP algorithm, an SM-SR-CAP algorithm and an SM-CAPvdr algorithm, and the constraint self-adaptive filtering algorithm is used for achieving a beam forming algorithm which is of time varying error boundary and based on set membership filtering and linear constraint. The intelligence antenna self-adaptive beam forming method enables the set membership filtering algorithm, the affine projection algorithm and the partial updating algorithm to be combined, a rate of convergence is high, complexity is low and maladjustment of steady state is small. Under the frame of the set membership filtering, a constraint affine projection algorithm with changeable reuse numbers is applied in the beam forming of an intelligence antenna, so that the complexity is further reduced without damaging algorithm performance.
Description
Technical field
The present invention relates to a kind of smart antenna self-adapting Beamforming Method.
Background technology
Smart antenna original name adaptive antenna array is widely used in radar, sonar and military aspect at first, and Main Function is space filtering and location.Along with the development of Digital Signal Processing, smart antenna is applied in the communications field more and more.The array antenna that smart antenna is comprised of a plurality of antenna elements, the antenna pattern that its weighted amplitude by regulating each array element signals and phase place change array, automatically measure the user side to, and main beam is aimed at the subscriber signal direction, antenna pattern zero falls into or lower antenna pattern gain and form in the interference signal direction, thereby reaches the purpose that suppresses to disturb and improve signal to noise ratio.Along with the fast development of global mobile communication cause, people have had more and more higher requirement to capacity and the quality of mobile communication, improve the availability of frequency spectrum very urgent, and the introducing of smart antenna has brought new life to whole wireless communication field.At present, intelligent antenna technology has been confirmed as one of key technology of 3-G (Generation Three mobile communication system).
The beamforming algorithm of smart antenna is the core content of smart antenna research, is also the focus of studying at present.In communication system, training sequence is limited in certain sample range possibly, therefore, wishes the convergence rate of adaptive algorithm as quickly as possible, and seeking the algorithm that computing is simple, convergence rate is faster, performance is more excellent becomes the key that wave beam forms.
Linear restriction minimum variance beam-forming technology calculates optimum weight vector and makes array output power minimum under the condition that guarantees desired signal directive gain certain value, is used widely in Array Signal Processing.Linear restriction in the linear restriction adaptive beam former has reflected the arrival direction (DOA) of subscriber signal in aerial array is processed, although the LCMV algorithm is widely used, yet use in practice and still have many problems: under low snap said conditions, the LCMV algorithm can't be restrained, adaptive direction figure distorted and poor stability make LCMV Algorithm Performance degradation; And due to the inversion process that has increased the covariance matrix of receive data, the amount of calculation of LCMV algorithm is larger.This is in such as wireless telecommunications and the dynamic situation of radar application equal altitudes, and when the array element quantity in filter was very large, convergence of algorithm speed was slack-off, needs a large amount of snapshots just can reach stable state.By increasing some constraintss, can reduce the susceptibility of algorithm pair array error and beam position error.Such as can making the adaptive beam main lobe flatten, derivative constraints broadens, thus the robustness of raising algorithm; Retrain zero point and can be used for tackling nonstationary interference.But owing to having increased many constraints in algorithm, not only taken the degree of freedom of system, and increased system complexity.
As a replacement scheme that reduces the adaptive-filtering computational complexity, collection person's filtering technique is subject to extensive concern.The SMF algorithm is derived under filter output error bounded, and it is mainly by sparse data selection of the time reduction computation complexity of more newly arriving.The sparse renewal of SMF can more effectively utilize the computing capability of dsp processor, reduces power consumption.Concerning mobile terminal, smart antenna can strengthen subscriber signal under the situation of not losing bandwidth, suppress to disturb, and the low-power consumption of collection person's filtering can energy savings, prolongs standby time.Therefore, research has stronger realistic meaning based on high s/n ratio and low-power consumption that the smart antenna of collection person's filtering and linear restriction helps to keep mobile terminal or equipment.
Smart antenna also is adaptive antenna, is comprised of a plurality of antenna elements, connects a complex weighted device after each antenna, merges output with adder at last.The smart antenna of this structure can only be completed spatial processing, and structurally relative complex is a little to have simultaneously the smart antenna of spatial domain, time domain disposal ability, and what connect after each antenna is a time-delay tap weighted network (identical with time domain FIR equalizer on structure).The main meaning of self adaptation or intelligence refers to that these weight coefficients can carry out the adaptive updates adjustment according to certain adaptive algorithm.
The basic thought of smart antenna is: antenna is dynamically followed the tracks of a plurality of desired users with a plurality of high-gain narrow beams, under receiving mode, suppressed from the signal outside narrow beam, under emission mode, the signal power that desired user is received is maximum, and the interference that narrow beam range of exposures unexpected user in addition is subject to is minimum.Smart antenna is to utilize the difference of user's space position to distinguish different user, auto-adaptive filtering technique be widely used in the fields such as System Discrimination, echo cancellation, adaptive equalization, noise cancellation and adaptive beam formation.The sef-adapting filter of a practicality need to possess Fast Convergent, lower processing delay and stable state lacked of proper care, to consider also that simultaneously sef-adapting filter is to the demand of hardware performance, therefore should select algorithm simple in structure, that be easy to realize when practical application, and fully the combined with hardware characteristic is optimized work, reduces algorithm operation quantity.
As a replacement scheme that reduces the adaptive-filtering computational complexity, collection person's filtering technique is subject to extensive concern.The SMF algorithm is derived under filter output error bounded, and it is mainly by sparse data selection of the time reduction computation complexity of more newly arriving.The filter of SMF only has amplitude when the output estimation error greater than setting, just carries out the renewal of coefficient.The sparse renewal of SMF can more effectively utilize the computing capability of dsp processor, reduces power consumption.Concerning mobile terminal, smart antenna can strengthen subscriber signal under the situation of not losing bandwidth, suppress to disturb, and the low-power consumption of collection person's filtering can energy savings, prolongs standby time.
The research of smart antenna mainly contains the content of four aspects: the first, and the core algorithm that intelligent antenna beam forms is mainly to seek the algorithm that amount of calculation is little, performance is more excellent; The second, set up more reasonably vector space model, advance the research of algorithm; The 3rd, the lift-off technology of research down link; The 4th, develop corresponding test platform, intelligent antenna performance is tested and verified.This project core technology is the core algorithm to smart antenna---the research of adaptive beam formation technology.
Propose the concept of adaptive antenna array since nineteen fifty-nine Van Atta since, the development of smart antenna was experienced substantially with the next stage: 1) previous decade concentrates in the control of adaptive beam, its research object is the radar antenna battle array, purpose is to improve performance and the electronic warfare capability of radar, is applied in Adaptive Controlled Phased Array array antenna and adaptive beam steering antenna etc.; 2) second 10 years, mainly concentrate on adaptive nulling and control, such as adaptive-filtering, adaptive nulling, Adaptive Sidelobe Canceling, self-adapting clutter control etc.; 3) the 3rd 10 years, mainly concentrate on Estimation of Spatial Spectrum, such as maximum likelihood spectrum is estimated, Maximum Entropy Spectral Estimation, feature space quadrature spectrum estimation etc.; 4) last decade, engineers just are being devoted to intelligent antenna technology is applied in mobile communication.
Following intelligent movable antenna all adopts digital method to realize that wave beam forms, and namely digital beam forms (DBF) antenna, upgrades thereby can use Software for Design to complete adaptive algorithm, increases the flexibility of system under the prerequisite that does not change system hardware configuration.Adaptive beam forms the very big concern that technology has obtained decades, is widely used in radar, sonar and field of wireless communication.
In recent years, along with the development of mobile communication technology and to wireless channel, networking technology, the going deep into of the aspect researchs such as antenna theory, adaptive antenna array begins be used to the mobile communication with complicated radio propagation environment.The main research project of smart antenna has: European Communications Committee cooperates by Germany, Britain, Denmark the TSUNAMI project completed what RAKE implemented in the works with Spain; Mitsubishi is electric, ATR photoelectric communication Research Institute satellite communication ground moving DBF experiment of antenna system; The GSMDCS BS antenna system of Ericsson-mannesman company; The antenna system of U.S. WEST-Vector Group company; The experiment porch of a smart antenna of smart antenna seminar of domestic Tsing-Hua University exploitation, this platform adopts damascene structures and bus structures, and carried out great many of experiments, realized each function of smart antenna, completed the indoor test of system, outfield experiments has realized that a plurality of users' wave beam is followed the tracks of.The TD-SCDMA system of the Datang Telecom letter prestige company research and development of China in TDD mode system, is the synchronization CDMA wireless communication system of first set applying intelligent antenna in the world with smart antenna application.
In recent years, domestic achievement in research about adaptive beam-forming algorithm is also many: the people such as Song of University of Electronic Science and Technology paean have proposed a kind of beamforming algorithm that reduces secondary lobe; University of Science and Technology for National Defence wears the people such as Ling Yan and has proposed robust adaptive beamforming algorithm under a kind of Coherent Environment for even linear array; The people such as the high book man of virtue and ability of radar institute of air force have proposed a kind of MODE-TOEP (MODE-TOEP) beamforming algorithm that is called; The people such as He Jie, Feng Dazheng proposes a kind of two-dimensional adaptive beamforming algorithm based on the domain of dependence; The people such as the Song Xin of Northeastern University, Wang Jinkuan have proposed a kind of I'm well adaptive beam-forming algorithm based on variable diagonal loading.
Smart antenna research based on collection person's filtering and adaptive beam-forming algorithm just just begins abroad, also have many problem values to further investigate, and it is domestic more to the LCMV algorithm research, but at present also fewer in conjunction with the research that collection person's filtering theory research adaptive beam forms, therefore the selected topic of this project has certain frontier nature and exploration, and the research and development of this project simultaneously will further promote country in smart antenna field autonomous innovation level.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, provide that a kind of amount of calculation is little, the algorithm computing is simple, fast convergence rate, well behaved smart antenna self-adapting Beamforming Method based on collection person's filtering and linear restriction.
The objective of the invention is to be achieved through the following technical solutions: the smart antenna self-adapting Beamforming Method, it comprises collection person's filtering algorithm step, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Collection person's filtering algorithm step comprises following substep:
S101: obtain the SM-NLMS algorithm by collection person's filtering algorithm;
S102: obtain the SM-AP algorithm by the SM-NLMS algorithm;
S103: obtain the SM-CAP algorithm by the SM-AP algorithm;
Part update algorithm step comprises following substep:
S201: obtain the SPU-NLMS algorithm by the part update algorithm;
S202: obtain the SM-PU-NLMS algorithm by the SPU-NLMS algorithm;
S203: obtain the SM-PU-CNLMS algorithm by the SM-PU-NLMS algorithm, perhaps obtain the SM-SPU-AP algorithm by the SM-PU-NLMS algorithm, then obtain the SM-SPU-CAP algorithm by the SM-SPU-AP algorithm;
The affine projection algorithm step comprises following substep:
S301: obtain the affine projection algorithm that Weight variable is used number by affine projection algorithm;
S302: Weight variable is combined with collection person's filtering algorithm, linear restriction adaptive algorithm with the affine projection algorithm of number, obtains three kinds of Weight variables and retrain affine projection algorithm with the collection person of number: SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAP vdr algorithm;
Constraint adaptive filter algorithm step comprises following substep:
S401: obtain the CLMS algorithm by the constraint adaptive filter algorithm;
S402: obtain the SM-CLMS algorithm by the CLMS algorithm;
S403: obtain the CAP algorithm by the CLMS algorithm, then obtain respectively SM-CAP algorithm and SM-REDCAP algorithm by the CAP algorithm;
S404: the beamforming algorithm based on collection person's filtering and linear restriction that becomes error boundary when being obtained by SM-CLMS algorithm, SM-CAP algorithm and SM-REDCAP algorithm.
The invention has the beneficial effects as follows:
(1) adaptive algorithm with collection person's filtering, affine projection algorithm and the triplicity of part renewal technology, is used for the linear restriction Beam-former, and convergence rate is rapid, complexity is low and the stable state imbalance is less;
(2) under the framework of collection person's filtering, Weight variable is applied to the wave beam formation of smart antenna with the constraint affine projection algorithm of number, further reduced complexity in the situation that do not lose algorithm performance;
(3) under the multi-user wireless communication environment, disturb and noise power is adjusted the time change error boundary of collection person's filtering automatically by estimation, can make the directional diagram of desired signal obtain good coupling, the precision of raising parameter Estimation;
(4) this Beamforming Method has the characteristics such as complexity is low, computing simple, fast convergence rate, functional, the stable state imbalance is less.
Description of drawings
Fig. 1 is the flow chart of adaptive beam formation method of the present invention.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail, but protection scope of the present invention is not limited to the following stated.
(1) constraint affine projection algorithm and wave beam form and use
(1) based on the constraint affine projection algorithm of collection person's filtering
Linear restriction adaptive-filtering (LCAF) is applied to comprise that wave beam is formed on some fields that interior signal is processed.The adaptive algorithm that is used for Linearly constrained problem can be divided into: LMS type or RLS type, they have represented that computation complexity and convergence rate two are extreme.Adopt the framework identical with other normalization bounding algorithms, as normalization constraint LMS algorithm, propose constraint affine projection (CAP) algorithm.And the thinking of normalization bounding algorithm is expanded to the SMF framework, thereby derive the constraint affine projection algorithm based on collection person's filtering, the low imbalance that it has kept the Fast Convergent of CAP algorithm and has brought due to data selection.
(2) the constraint set person's affine projection algorithm and the wave beam formation thereof that reduce complexity are used
In discontinuous updated time, adopt a permutation matrix, act on the input data, make the nonzero value in input vector transfer to the left side, reduce the dimension of matrix inversion, thereby obtain an alternate algorithm, reduce the peak value computation complexity of algorithm.The wave beam that this algorithm is used for smart antenna forms, and verifies its validity.
(2) piecemeal partly upgrades collection person's filtering algorithm and wave beam formation
(1) part is upgraded affine projection algorithm
By the sparse grouping of sef-adapting filter, per moment upgrades part of module and reduces complexity.In affine projection algorithm, concrete grammar is as follows this application of policies: L filter coefficient is divided into the M piece, and the length of every is N, selects the B piece in the M piece to carry out the part renewal; By adopting certain Optimality Criteria, M piece sorted, select B maximum piece to be used for upgrading.
(2) piecemeal partly upgrades collection person's affine projection algorithm
Collection person's filtering is combined with above-mentioned algorithm, just obtain collection person's affine projection algorithm that piecemeal partly upgrades.
(3) application of above-mentioned algorithm in wave beam forms
According to experimental result and document, determine to affect the principal element that directional diagram is estimated mean-squared departure.Take mean-squared departure as evaluation index, adopt Monte Carlo method to calculate mean-squared departure and data updating rate.
(3) Weight variable forms application with collection person's affine projection algorithm and the wave beam of number
The data reusing degree of the computation complexity of affine projection algorithm and convergence rate and its employing is in close relations.Its is restrained rapidly when using a plurality of input vector, but complexity is high, and has larger imbalance.On the other hand, although when the vector that uses seldom the time its convergence rate slow, the stable state imbalance is less, complexity is low.For this reason, can consider to adopt Weight variable with the affine projection algorithm of number, adopt the larger number of reusing in initial convergence phase, and when algorithm enters stable state, use the less number of reusing.
(1) fixed ratio reuses several affine projection algorithms
According to the minimal disturbances criterion, select the input vector of fixing ratio to be used for upgrading in the input vector of reusing.
(2) Dynamic Selection is reused several affine projection algorithms
When each iteration, according to the number of certain criterion Dynamic Selection input vector.Realize for the ease of hardware, a kind of typical strategy is that the number of reusing of selecting switches between maximum and 2.
(3) combination of above-mentioned algorithm and collection person's affine projection algorithm
Sparse renewal and the time change step length of collection person's filtering are applied to above-mentioned algorithm, to reach more attractive reduced complexity.
(4) wave beam of above-mentioned algorithm application in smart antenna forms, and compares with other classic algorithm
(4) adjustable collection person's affine projection algorithm and the wave beam thereof of error span forms
The SMF filtering technique depends on the appointment of error boundary, in actual applications due to the dynamic knowledge that lacks environment, the border of error is difficult to accurate estimation.Under dynamic environment, select a fixing border can cause the risk of break bounds (error boundary is greater than actual value) and lower bound (error boundary is less than actual value), cause the decline of performance.This means and take certain mechanism automatically to guarantee performance preferably in the alignment error border.
(1) based on the time become collection person's affine projection algorithm of error boundary
Under mobile communication environment, what the user was regular entering and logging off, and exists under the situation of multiple access interference and intersymbol interference, suppose that real error boundary is constant, by following the tracks of and estimating interference power, obtain a time dependent error boundary, reduce the risk of break bounds or lower bound.
(2) based on collection person's affine projection algorithm on adaptive error border
Collection person's algorithm has convergence efficiency and lower computation complexity preferably, yet in many practical problems, due to very difficult predicated error border, thereby is difficult to the complexity of control algolithm, has limited the application of collection person's algorithm.By adopting a kind of adaptive error border, avoid the priori about error boundary, and keep data selection characteristic and the convergence property of collection person's algorithm.
(3) application during the intelligent antenna beam of above-mentioned algorithm under mobile communication environment forms, and with the Performance Ratio of conventional method.
As shown in Figure 1, the smart antenna self-adapting Beamforming Method, it comprises collection person's filtering algorithm step, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Collection person's filtering algorithm step comprises following substep:
S101: obtain the SM-NLMS algorithm by collection person's filtering algorithm;
S102: obtain the SM-AP algorithm by the SM-NLMS algorithm;
S103: obtain the SM-CAP algorithm by the SM-AP algorithm;
Part update algorithm step comprises following substep:
S201: obtain the SPU-NLMS algorithm by the part update algorithm;
S202: obtain the SM-PU-NLMS algorithm by the SPU-NLMS algorithm;
S203: obtain the SM-PU-CNLMS algorithm by the SM-PU-NLMS algorithm, obtain upgrading the constraint normalization minimum mean-square calculation based on the part of collection person's filtering; Perhaps obtain the SM-SPU-AP algorithm by the SM-PU-NLMS algorithm, then obtain the SM-SPU-CAP algorithm by the SM-SPU-AP algorithm, obtain partly upgrading the constraint affine projection algorithm based on the selectivity of collection person's filtering;
The affine projection algorithm step comprises following substep:
S301: obtain the affine projection algorithm that Weight variable is used number by affine projection algorithm;
S302: Weight variable is combined with collection person's filtering algorithm, linear restriction adaptive algorithm with the affine projection algorithm of number, obtains three kinds of Weight variables and retrain affine projection algorithm with the collection person of number: SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAP vdr algorithm;
Constraint adaptive filter algorithm step comprises following substep:
S401: obtain the CLMS algorithm by the constraint adaptive filter algorithm;
S402: obtain the SM-CLMS algorithm by the CLMS algorithm;
S403: obtain the CAP algorithm by the CLMS algorithm, then obtain respectively SM-CAP algorithm and SM-REDCAP algorithm by the CAP algorithm;
S404: the beamforming algorithm based on collection person's filtering and linear restriction that becomes error boundary when being obtained by SM-CLMS algorithm, SM-CAP algorithm and SM-REDCAP algorithm.
Claims (1)
1. smart antenna self-adapting Beamforming Method is characterized in that: it comprises collection person's filtering algorithm step, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Collection person's filtering algorithm step comprises following substep:
S101: obtain the SM-NLMS algorithm by collection person's filtering algorithm;
S102: obtain the SM-AP algorithm by the SM-NLMS algorithm;
S103: obtain the SM-CAP algorithm by the SM-AP algorithm;
Part update algorithm step comprises following substep:
S201: obtain the SPU-NLMS algorithm by the part update algorithm;
S202: obtain the SM-PU-NLMS algorithm by the SPU-NLMS algorithm;
S203: obtain the SM-PU-CNLMS algorithm by the SM-PU-NLMS algorithm, perhaps obtain the SM-SPU-AP algorithm by the SM-PU-NLMS algorithm, then obtain the SM-SPU-CAP algorithm by the SM-SPU-AP algorithm;
The affine projection algorithm step comprises following substep:
S301: obtain the affine projection algorithm that Weight variable is used number by affine projection algorithm;
S302: Weight variable is combined with collection person's filtering algorithm, linear restriction adaptive algorithm with the affine projection algorithm of number, obtains three kinds of Weight variables and retrain affine projection algorithm with the collection person of number: SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAP vdr algorithm;
Constraint adaptive filter algorithm step comprises following substep:
S401: obtain the CLMS algorithm by the constraint adaptive filter algorithm;
S402: obtain the SM-CLMS algorithm by the CLMS algorithm;
S403: obtain the CAP algorithm by the CLMS algorithm, then obtain respectively SM-CAP algorithm and SM-REDCAP algorithm by the CAP algorithm;
S404: the beamforming algorithm based on collection person's filtering and linear restriction that becomes error boundary when being obtained by SM-CLMS algorithm, SM-CAP algorithm and SM-REDCAP algorithm.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103364771A (en) * | 2013-07-14 | 2013-10-23 | 西安电子科技大学 | Radar beam forming device based on airspace compression projection and random clock sampling |
| CN103762958A (en) * | 2014-01-07 | 2014-04-30 | 南京信息工程大学 | Improved affine combination adaptive filtering method |
| CN106155724A (en) * | 2015-04-14 | 2016-11-23 | 阿里巴巴集团控股有限公司 | A kind of upgrade method and device |
| CN109257068A (en) * | 2018-09-11 | 2019-01-22 | 广东石油化工学院 | A kind of electric-power wire communication signal adaptive filter method |
| CN109716671A (en) * | 2016-09-23 | 2019-05-03 | 瑞典爱立信有限公司 | Wave beam finds process |
| CN111929646A (en) * | 2020-08-14 | 2020-11-13 | 中国地质大学（北京） | Beam scanning ground penetrating radar system and intelligent beam scanning detection method |
| CN112904094A (en) * | 2021-02-04 | 2021-06-04 | 中国人民解放军国防科技大学 | Orofacial antenna external field test method based on unmanned aerial vehicle |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101572574A (en) * | 2009-06-01 | 2009-11-04 | 中国民航大学 | Smart antenna self-adapting interference suppression method based on least square-lowest mean square |
-
2013
- 2013-01-18 CN CN201310018878.3A patent/CN103095357B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101572574A (en) * | 2009-06-01 | 2009-11-04 | 中国民航大学 | Smart antenna self-adapting interference suppression method based on least square-lowest mean square |
Non-Patent Citations (2)
| Title |
|---|
| 毛瑞石、李忠、邓新文: "基于集员滤波的鱼雷声纳子带自适应滤波算法", 《四川兵工学报》 * |
| 马娟娟、金永、程擂: "基于仿射投影算法的时延估计", 《传感器世界》 * |
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| CN103364771A (en) * | 2013-07-14 | 2013-10-23 | 西安电子科技大学 | Radar beam forming device based on airspace compression projection and random clock sampling |
| CN103364771B (en) * | 2013-07-14 | 2015-05-27 | 西安电子科技大学 | Radar beam forming device based on airspace compression projection and random clock sampling |
| CN103762958A (en) * | 2014-01-07 | 2014-04-30 | 南京信息工程大学 | Improved affine combination adaptive filtering method |
| CN103762958B (en) * | 2014-01-07 | 2016-09-28 | 南京信息工程大学 | A kind of affine combination adaptive filter method of improvement |
| CN106155724A (en) * | 2015-04-14 | 2016-11-23 | 阿里巴巴集团控股有限公司 | A kind of upgrade method and device |
| CN106155724B (en) * | 2015-04-14 | 2020-03-13 | 阿里巴巴集团控股有限公司 | Upgrading method and device |
| CN109716671A (en) * | 2016-09-23 | 2019-05-03 | 瑞典爱立信有限公司 | Wave beam finds process |
| CN109257068A (en) * | 2018-09-11 | 2019-01-22 | 广东石油化工学院 | A kind of electric-power wire communication signal adaptive filter method |
| CN109257068B (en) * | 2018-09-11 | 2021-09-17 | 广东石油化工学院 | Adaptive filtering method for power line communication signals |
| CN111929646A (en) * | 2020-08-14 | 2020-11-13 | 中国地质大学（北京） | Beam scanning ground penetrating radar system and intelligent beam scanning detection method |
| CN112904094A (en) * | 2021-02-04 | 2021-06-04 | 中国人民解放军国防科技大学 | Orofacial antenna external field test method based on unmanned aerial vehicle |
| CN112904094B (en) * | 2021-02-04 | 2022-08-23 | 中国人民解放军国防科技大学 | Orofacial antenna external field test method based on unmanned aerial vehicle |
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Inventor after: Ning Tao Inventor after: Zhang Xiaorong Inventor after: Wu Xiaobo Inventor after: Kong Xiangrui Inventor before: Ning Tao Inventor before: Ma Jianqing Inventor before: Song Jiabin Inventor before: Wu Weidong |
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Denomination of invention: Intelligence antenna self-adaptive beam forming method Effective date of registration: 20160728 Granted publication date: 20151118 Pledgee: Agricultural Bank of China Limited by Share Ltd Chengdu West Branch Pledgor: Jiuhua Yuantong Science and Technology Development Co., Ltd., Chengdu Registration number: 2016510000036 |
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Date of cancellation: 20201224 Granted publication date: 20151118 Pledgee: Agricultural Bank of China Limited by Share Ltd. Chengdu West Branch Pledgor: CHENGDU JIUHUA YUANTONG TECHNOLOGY DEVELOPMENT Co.,Ltd. Registration number: 2016510000036 |