CN103095357B - Smart antenna self-adapting Beamforming Method - Google Patents
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Abstract
The invention discloses a kind of smart antenna self-adapting Beamforming Method, it comprises set-membership filtering algorithm steps, part update algorithm step, affine projection algorithm step and constraint adaptive filter algorithm step: set-membership filtering algorithm steps: obtain SM-CAP algorithm by set-membership filtering algorithm; Part update algorithm step: obtain SM-PU-NLMS or SM-SPU-CAP algorithm by part update algorithm; Affine projection algorithm step: obtain SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAP by affine projection algorithm? vdr algorithm; Constraint adaptive filter algorithm step: the beamforming algorithm based on set-membership filtering and linear restriction becoming error boundary when being obtained by constraint adaptive filter algorithm.The present invention is by set-membership filtering, affine projection algorithm and the triplicity of part renewal technology, and convergence rate is rapid, complexity is low and steady output rate is less; Under the framework of set-membership filtering, the constraint affine projection algorithm of Weight variable number being applied to the Wave beam forming of smart antenna, reduce further complexity when not losing 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 made up of multiple antenna element, it changes the antenna pattern of array by the weighted amplitude and phase place regulating each array element signals, automatically measure user side to, and main beam is aimed at subscriber signal direction, and form the sunken or lower antenna pattern gain of antenna pattern zero in interference signal direction, thus reach the object suppressing interference 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 the capacity of mobile communication and quality, and improve the availability of frequency spectrum very urgent, the introducing of smart antenna brings new life to whole wireless communication field.At present, intelligent antenna technology one of key technology being confirmed as 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 research at present.In a communications system, training sequence is limited in certain sample range possibly, therefore, wishes the convergence rate of adaptive algorithm as quickly as possible, finds the key that computing is simple, convergence rate is faster, performance is more excellent algorithm becomes Wave beam forming.
Linear constraint minimal variance beam-forming technology is ensureing, under the condition to desired signal directive gain certain value, to calculate optimum weight vector and make array output power minimum, be used widely in Array Signal Processing.Linear restriction in linear restriction adaptive beam former reflects the arrival direction (DOA) of subscriber signal in aerial array process, although LCMV algorithm is widely used, but apply in practice and still there is many problems: under low snap said conditions, LCMV algorithm cannot be restrained, there is distortion and poor stability in adaptive direction figure, makes the performance degradation of LCMV algorithm; And owing to adding the inversion process to the covariance matrix receiving data, the amount of calculation of LCMV algorithm is larger.These are under such as wireless telecommunications and the dynamic situation of radar application equal altitudes, and when the array element quantity in filter is very large, convergence of algorithm slows, and needs a large amount of snapshots just can reach stable state.By increasing some constraintss, the susceptibility of algorithm pair array error and Beam steering error can be reduced.Such as derivative constraints can make adaptive beam main lobe flatten to broaden, thus improves the robustness of algorithm; Zeros constrained can be used for tackling nonstationary interference.But owing to adding many constraints in the algorithm, not only occupy the degree of freedom of system, and add system complexity.
Reduce the replacement scheme of adaptive-filtering computational complexity as one, set-membership filtering technology is subject to extensive concern.SMF algorithm is derived under filter output error bounded, and it mainly more to be newly arrived reduction computation complexity by the data selection that the time is sparse.The sparse renewal of SMF more effectively can utilize the computing capability of dsp processor, reduces power consumption.Concerning mobile terminal, smart antenna can strengthen subscriber signal when not losing bandwidth, suppresses interference, and the low-power consumption of set-membership filtering can energy savings, prolongs standby time.Therefore, the smart antenna studied based on set-membership filtering and linear restriction contributes to keeping high s/n ratio and the low-power consumption of mobile terminal or equipment, has stronger realistic meaning.
Smart antenna is also adaptive antenna, is made up of multiple antenna element, connects a complex weighted device after each antenna, finally carries out merging with adder and exports.The smart antenna of this structure can only complete spatial processing, and have spatial domain simultaneously, structurally relative complex is a little for the smart antenna of temporal processing ability, what connect after each antenna is a time delay tap weighted network (identical with time domain FIR equalizer in structure).The main meaning of self adaptation or intelligence refers to that these weight coefficients can carry out adaptive updates adjustment according to certain adaptive algorithm.
The basic thought of smart antenna is: antenna dynamically follows the tracks of multiple desired user with multiple high-gain narrow beam, under receiving mode, suppressed from the signal outside narrow beam, under emission mode, the signal power that desired user can be made to receive is maximum, and the interference simultaneously making the undesired user beyond narrow beam range of exposures be subject to is minimum.Smart antenna utilizes 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 beamformer.A practical sef-adapting filter needs to possess Fast Convergent, lower processing delay and steady output rate, also to consider the demand of sef-adapting filter to hardware performance simultaneously, therefore the algorithm that structure is simple, be easy to realization should be selected when practical application, and fully combined with hardware characteristic is optimized work, reduce algorithm operation quantity.
Reduce the replacement scheme of adaptive-filtering computational complexity as one, set-membership filtering technology is subject to extensive concern.SMF algorithm is derived under filter output error bounded, and it mainly more to be newly arrived reduction computation complexity by the data selection that the time is sparse.The filter of SMF only has the amplitude when output estimation error to be greater than setting, just carries out the renewal of coefficient.The sparse renewal of SMF more effectively can utilize the computing capability of dsp processor, reduces power consumption.Concerning mobile terminal, smart antenna can strengthen subscriber signal when not losing bandwidth, suppresses interference, and the low-power consumption of set-membership filtering can energy savings, prolongs standby time.
The research of smart antenna mainly contains the content of four aspects: the first, the core algorithm that intelligent antenna beam is formed, and mainly finds the algorithm that amount of calculation is little, performance is more excellent; The second, set up more reasonably vector space model, the research of push model; 3rd, the lift-off technology of research down link; 4th, develop corresponding test platform, intelligent antenna performance is tested and verifies.This project core technology is the core algorithm to smart antenna---the research of Adaptive beamformer technology.
Since nineteen fifty-nine VanAtta proposes the concept of adaptive antenna array, the development of smart antenna experienced by substantially with the next stage: 1) previous decade concentrates in the control of adaptive beam, its research object is radar antenna battle array, object improves 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 control on, such as adaptive-filtering, adaptive nulling, Adaptive Sidelobe Canceling, self-adapting clutter control etc.; 3) the 3rd 10 years, mainly concentrate in Estimation of Spatial Spectrum, such as maximum likelihood Power estimation, Maximum Entropy Spectral Estimation, feature space quadrature spectrum estimation etc.; 4) last decade, engineers is just being devoted to intelligent antenna technology to be applied in mobile communication.
Following intelligent movable antenna all adopts digital method to realize Wave beam forming, i.e. digital beam froming (DBF) antenna, thus Software for Design can be used to complete adaptive algorithm renewal, increases the flexibility of system under the prerequisite not changing system hardware configuration.Adaptive beamformer technology has obtained the very big concern of 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, going deep into of the aspect researchs such as antenna theory, adaptive antenna array starts the mobile communication for having complicated radio propagation environment.The main research project of smart antenna has: the TSUNAMI project cooperated with Spain by Germany, Britain, Denmark that European Communications Committee implements in the works at RAKE; Mitsubishi electrically, ATR photoelectric communication Research Institute satellite communication ground moving DBF experiment of antenna system; The GSMDCSBS antenna system of Ericsson-mannesman company; The antenna system of WEST-VectorGroup company of the U.S.; 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, achieve each function of smart antenna, complete the indoor test of system, outfield experiments, the wave beam achieving multiple user is followed the tracks of.The TD-SCDMA system of Xin Wei company of the Datang Telecom research and development of China, by smart antenna application in TDD mode system, is the synchronization CDMA wireless communication system of first set applying intelligent antenna in the world.
In recent years, the domestic achievement in research about adaptive beam-forming algorithm is also many: the people such as University of Electronic Science and Technology Song paean propose a kind of beamforming algorithm reducing secondary lobe; The people such as University of Science and Technology for National Defence Dai Lingyan propose the robust adaptive beamforming algorithm under a kind of Coherent Environment for even linear array; The people such as an Airforce Radar institute height book man of virtue and ability propose one and are called MODE-TOEP (MODE-TOEP) beamforming algorithm; The people such as He Jie, Feng great Zheng propose a kind of two-dimensional adaptive beamforming algorithm based on the domain of dependence; The people such as Northeastern University Song Xin, Wang Jinkuan propose a kind of I'm well adaptive beam-forming algorithm based on variable diagonal loading.
Smart antenna research based on set-membership filtering and adaptive beam-forming algorithm just just starts abroad, many problem values are also had to further investigate, and it is domestic more to LCMV algorithm research, but it is also fewer at present in conjunction with the research of set-membership filtering theoretical research Adaptive beamformer, therefore the selected topic of this project has certain frontier nature and exploration, and the research and development of this project simultaneously will promote country further 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, algorithm computing is simple, fast convergence rate, the smart antenna self-adapting Beamforming Method based on set-membership filtering and linear restriction of good performance.
The object of the invention is to be achieved through the following technical solutions: smart antenna self-adapting Beamforming Method, it comprises a set-membership filtering algorithm steps, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Set-membership filtering algorithm steps comprises following sub-step:
S101: obtain SM-NLMS algorithm by set-membership filtering algorithm;
S102: obtain SM-AP algorithm by SM-NLMS algorithm;
S103: obtain SM-CAP algorithm by SM-AP algorithm;
Part update algorithm step comprises following sub-step:
S201: obtain SPU-NLMS algorithm by part update algorithm;
S202: obtain SM-PU-NLMS algorithm by SPU-NLMS algorithm;
S203: obtain SM-PU-CNLMS algorithm by SM-PU-NLMS algorithm, or obtain SM-SPU-AP algorithm by SM-PU-NLMS algorithm, then obtain SM-SPU-CAP algorithm by SM-SPU-AP algorithm;
Affine projection algorithm step comprises following sub-step:
S301: the affine projection algorithm being obtained Weight variable number by affine projection algorithm;
S302: be combined with set-membership filtering algorithm, linear restriction adaptive algorithm by the affine projection algorithm of Weight variable number, the collection person obtaining three kinds of Weight variable numbers retrains affine projection algorithm: SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAPvdr algorithm;
Constraint adaptive filter algorithm step comprises following sub-step:
S401: obtain CLMS algorithm by constraint adaptive filter algorithm;
S402: obtain SM-CLMS algorithm by CLMS algorithm;
S403: obtain CAP algorithm by CLMS algorithm, then obtain SM-CAP algorithm and SM-REDCAP algorithm respectively by CAP algorithm;
S404: the beamforming algorithm based on set-membership filtering and linear restriction becoming 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 is by set-membership filtering, affine projection algorithm and the triplicity of part renewal technology, and in linear restriction Beam-former, convergence rate is rapid, complexity is low and steady output rate is less;
(2) under the framework of set-membership filtering, the constraint affine projection algorithm of Weight variable number being applied to the Wave beam forming of smart antenna, reduce further complexity when not losing algorithm performance;
(3) under multi-user wireless communication environment, by estimate interference and noise power automatically adjust set-membership filtering time become error boundary, the directional diagram of desired signal can be made to obtain good coupling, raising parameter Estimation precision;
(4) this Beamforming Method has the features such as complexity is low, computing simple, fast convergence rate, functional, steady output rate is less.
Accompanying drawing explanation
Fig. 1 is the flow chart of Adaptive beamformer 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) affine projection algorithm and Wave beam forming application is retrained
(1) based on the constraint affine projection algorithm of set-membership filtering
Linear restriction adaptive-filtering (LCAF) is applied to some fields of the signal transacting comprising Wave beam forming.Adaptive algorithm for Linearly constrained problem can be divided into: LMS type or RLS type, and two which represent computation complexity and convergence rate extreme.Adopt the framework identical with other normalization bounding algorithms, as normalization constrained-LMS algorithm, propose constraint affine projection (CAP) algorithm.And the thinking of normalization bounding algorithm is expanded to SMF framework, thus derive the constraint affine projection algorithm based on set-membership filtering, the Fast Convergent that it preserves CAP algorithm and the low imbalance brought due to data selection.
(2) constraint set person's affine projection algorithm and the Wave beam forming application thereof of complexity is reduced
In discontinuous updated time, adopt a permutation matrix, act on input data, make the nonzero value in input vector transfer to the left side, reduce the dimension of matrix inversion, thus obtain an alternate algorithm, reduce the peak value computation complexity of algorithm.This algorithm is used for the Wave beam forming in smart antenna, verifies its validity.
(2) piecemeal part upgrades set-membership filtering algorithm and Wave beam forming
(1) part upgrades affine projection algorithm
By sparse for sef-adapting filter grouping, per moment update section sub-module reduces complexity.This application of policies in affine projection algorithm, concrete grammar is as follows: L filter coefficient is divided into M block, and the length of every block is N, selects the B block in M block to carry out part renewal; By adopting certain Optimality Criteria, M block being sorted, selecting the individual maximum block of B to be used for upgrading.
(2) piecemeal part upgrades collection person's affine projection algorithm
Set-membership filtering is combined with above-mentioned algorithm, just obtains collection person's affine projection algorithm that piecemeal part upgrades.
(3) application of above-mentioned algorithm in Wave beam forming
Experimentally result and document, determines to affect the principal element that directional diagram estimates mean-squared departure.Take mean-squared departure as evaluation index, adopt Monte Carlo method to calculate mean-squared departure and data updating rate.
(3) collection person's affine projection algorithm of Weight variable number and Wave beam forming application
The computation complexity of affine projection algorithm and convergence rate and its data reusing degree of adopting in close relations.When using multiple input vector, its convergence rapidly, but complexity is high, and have larger imbalance.On the other hand, although its convergence rate is slow when the vector used is little, steady output rate is less, and complexity is low.For this reason, the affine projection algorithm adopting Weight variable number can be considered, adopt in initial convergence phase and larger reuse number, and when algorithm enters stable state, use and less reuse number.
(1) fixed ratio reuse several affine projection algorithm
According to minimal disturbances criterion in the input vector of reusing, the input vector of fixing ratio is selected to be used for upgrading.
(2) Dynamic Selection reuses several affine projection algorithms
When each iteration, according to the number of certain criterion Dynamic Selection input vector.For the ease of hardware implementing, a kind of typical strategy is that the number of reusing selected switches between maximum and 2.
(3) combination of above-mentioned algorithm and collection person's affine projection algorithm
The sparse renewal of set-membership filtering and time change step length are applied to above-mentioned algorithm, reduce to reach more attractive complexity.
(4) Wave beam forming of above-mentioned algorithm application in smart antenna, and compare with other classic algorithm
(4) collection person's affine projection algorithm that error span is adjustable and Wave beam forming thereof
SMF filtering technique depends on the appointment of error boundary, in actual applications owing to lacking the dynamic knowledge of environment, is difficult to accurate estimation to the border of error.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 take the automatic alignment error border of certain mechanism to ensure good performance.
(1) based on time become collection person's affine projection algorithm of error boundary
Under mobile communication environment, when there is multi-access inference and intersymbol interference in regular the entering and logging off of user, 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 good convergence efficiency and lower computation complexity, but in many practical problems, due to very difficult predicated error border, thus is difficult to the complexity of control algolithm, limits the application of collection person's algorithm.By adopting a kind of adaptive error border, avoid the priori about error boundary, and retain data selection characteristic and the convergence property of collection person's algorithm.
(3) intelligent antenna beam of above-mentioned algorithm under mobile communication environment formed in application, and with the Performance comparision of conventional method.
As shown in Figure 1, smart antenna self-adapting Beamforming Method, it comprises a set-membership filtering algorithm steps, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Set-membership filtering algorithm steps comprises following sub-step:
S101: obtain SM-NLMS algorithm by set-membership filtering algorithm;
S102: obtain SM-AP algorithm by SM-NLMS algorithm;
S103: obtain SM-CAP algorithm by SM-AP algorithm;
Part update algorithm step comprises following sub-step:
S201: obtain SPU-NLMS algorithm by part update algorithm;
S202: obtain SM-PU-NLMS algorithm by SPU-NLMS algorithm;
S203: obtain SM-PU-CNLMS algorithm by SM-PU-NLMS algorithm, obtains upgrading constraint normalization minimum mean-square calculation based on the part of set-membership filtering; Or obtain SM-SPU-AP algorithm by SM-PU-NLMS algorithm, then obtain SM-SPU-CAP algorithm by SM-SPU-AP algorithm, obtain upgrading constraint affine projection algorithm based on the selectivity part of set-membership filtering;
Affine projection algorithm step comprises following sub-step:
S301: the affine projection algorithm being obtained Weight variable number by affine projection algorithm;
S302: be combined with set-membership filtering algorithm, linear restriction adaptive algorithm by the affine projection algorithm of Weight variable number, the collection person obtaining three kinds of Weight variable numbers retrains affine projection algorithm: SM-DS-CAP algorithm, SM-SR-CAP algorithm and SM-CAPvdr algorithm;
Constraint adaptive filter algorithm step comprises following sub-step:
S401: obtain CLMS algorithm by constraint adaptive filter algorithm;
S402: obtain SM-CLMS algorithm by CLMS algorithm;
S403: obtain CAP algorithm by CLMS algorithm, then obtain SM-CAP algorithm and SM-REDCAP algorithm respectively by CAP algorithm;
S404: the beamforming algorithm based on set-membership filtering and linear restriction becoming 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 a set-membership filtering algorithm steps, a part update algorithm step, an affine projection algorithm step and a constraint adaptive filter algorithm step:
Set-membership filtering algorithm steps comprises following sub-step:
S101: obtain the normalization minimum mean-square calculation SM-NLMS algorithm based on set-membership filtering by set-membership filtering algorithm;
S102: obtain the affine projection algorithm SM-AP algorithm based on set-membership filtering by the normalization minimum mean-square calculation SM-NLMS algorithm based on set-membership filtering;
S103: obtain the constraint affine projection algorithm SM-CAP algorithm based on set-membership filtering by the affine projection algorithm SM-AP algorithm based on set-membership filtering;
Part update algorithm step comprises following sub-step:
S201: the Normalized least mean squares SPU-NLMS algorithm being obtained the renewal of selectivity part by part update algorithm;
S202: the Normalized least mean squares SPU-NLMS algorithm upgraded by selectivity part obtains upgrading normalization minimum mean-square calculation SM-PU-NLMS algorithm based on the part of set-membership filtering;
S203: upgrade normalization minimum mean-square calculation SM-PU-NLMS algorithm by the part based on set-membership filtering and obtain upgrading constraint normalization minimum mean-square calculation SM-PU-CNLMS algorithm based on the part of set-membership filtering, or upgrade normalization minimum mean-square calculation SM-PU-NLMS algorithm by the part based on set-membership filtering to obtain upgrading affine projection algorithm SM-SPU-AP algorithm based on the selectivity part of set-membership filtering, upgrade affine projection algorithm SM-SPU-AP algorithm by the selectivity part based on set-membership filtering again to obtain upgrading constraint affine projection algorithm SM-SPU-CAP algorithm based on the selectivity part of set-membership filtering,
Affine projection algorithm step comprises following sub-step:
S301: the affine projection algorithm being obtained Weight variable number by affine projection algorithm;
S302: be combined with set-membership filtering algorithm, linear restriction adaptive algorithm by the affine projection algorithm of Weight variable number, the collection person obtaining three kinds of Weight variable numbers retrains affine projection algorithm: the dynamic input signal exponent number based on set-membership filtering retrains affine projection algorithm SM-DS-CAP algorithm, retrains affine projection algorithm SM-CAPvdr algorithm based on select by a certain percentage the constraint affine projection algorithm SM-SR-CAP algorithm of regressor and the variable data reuse factor collection person of set-membership filtering;
Constraint adaptive filter algorithm step comprises following sub-step:
S401: obtain constraint least mean square algorithm CLMS algorithm by constraint adaptive filter algorithm;
S402: obtain the constraint least mean square algorithm SM-CLMS algorithm based on set-membership filtering by constraint least mean square algorithm CLMS algorithm;
S403: obtain constraint affine projection algorithm CAP algorithm by constraint least mean square algorithm CLMS algorithm, then obtain the constraint affine projection algorithm SM-CAP algorithm based on set-membership filtering and the constraint of the random early check based on set-membership filtering affine projection algorithm SM-REDCAP algorithm respectively by constraint affine projection algorithm CAP algorithm;
S404: by retraining the beamforming algorithm based on set-membership filtering and linear restriction becoming error boundary when affine projection algorithm SM-REDCAP algorithm obtains based on the constraint least mean square algorithm SM-CLMS algorithm of set-membership filtering, the constraint affine projection algorithm SM-CAP algorithm based on set-membership filtering and the random early check based on set-membership filtering.
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| 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 |
| 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 |
| CN106155724B (en) * | 2015-04-14 | 2020-03-13 | 阿里巴巴集团控股有限公司 | Upgrading method and device |
| EP3516783B1 (en) * | 2016-09-23 | 2020-06-24 | Telefonaktiebolaget LM Ericsson (PUBL) | Beam finding procedure |
| 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 |
| 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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