CN110308418A - A kind of DOA estimation framework method - Google Patents
A kind of DOA estimation framework method Download PDFInfo
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- CN110308418A CN110308418A CN201910719938.1A CN201910719938A CN110308418A CN 110308418 A CN110308418 A CN 110308418A CN 201910719938 A CN201910719938 A CN 201910719938A CN 110308418 A CN110308418 A CN 110308418A
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- signal
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- 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
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
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- 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
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
- G01S3/46—Systems for determining direction or deviation from predetermined direction using antennas spaced apart and measuring phase or time difference between signals therefrom, i.e. path-difference systems
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- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The present invention provides a kind of DOA to estimate that framework method, in particular to a kind of system-oriented grade smart antenna wave reach the improved method of orientation estimation.The method are as follows: (1) be directed to different signal frequency models in advance, derive the DOA estimation evaluation criterion under different models, and determine evaluation criterion.(2) after the antenna array receiver to data, the feature as analysis object signal frequency is extracted from the data.(3) classify described as analysis object signal frequency.(4) trigger mechanism is used, selects the different predetermined DOA estimation evaluation criterions as analysis object signal frequency according to described.(5) evaluation criterion is estimated using the DOA of triggering selection, the wave for obtaining the signal is estimated up to orientation.
Description
Technical field
The present invention relates to a kind of DOA to estimate framework method, estimates more particularly to a kind of DOA of system-oriented grade smart antenna
Count framework method.
Background technique
Array signal processing is an important branch in field of signal processing, is obtaining rapid hair in the past 40 years
Exhibition, application are related to numerous military and states such as radar, communication, sonar, earthquake, exploration, radio astronomy and biomedical engineering
People economic field.
The time-domain spectral as known to everybody is a key concept in Time Domain Processing, spatial spectrum is array letter
Number processing in a key concept.Time-domain spectral indicates Energy distribution of the signal in each frequency, and spatial spectrum then indicates
Energy distribution of the signal in all directions of space.Therefore, if " spatial spectrum " of available signal, can obtain signal
Direction of arrival, so, spatial spectrum is commonly referred to as that wave is estimated up to orientation (DOA).
The DOA of early stage estimates using conventional beamformer method (CBF) beam-forming schemes as representative.But such algorithm is by battle array
The limitation of spacing between first number and array element, resolution ratio are lower.Then using Capon method and maximum entropy method (MEM) as representative
High resolution remote sensing data algorithm breaches above-mentioned limitation to a certain extent.
The 1970s multiple signal classification (Multiple is proposed with the Schmidt R O in the U.S. et al.
Signal Classification, MUSIC), subsequent invariable rotary subspace (Estimation of Signal
Parameters via Rotational Invariance Techniques, ESPRIT [6-7]) it is proposed, two seeds are empty
Between decompose class algorithm and substantially increase the precision of signal measurement.Later period maximum likelihood algorithm (ML), Weighted Sub-Space Fitting Direction
High resolution algorithms such as (Weighted Subspace Fitting, WSF) are also rapidly developed.Its precision is high, can be simultaneously
It detects the azimuth information of multiple signals and can handle coherent signal and be developed rapidly, but these algorithms are complicated, in real time
Property it is poor, be unfavorable for Practical Project practice in aspect real-time estimation.In currently practical direction finding instrument, do
Interferometer direction finding is the Direction Finding Algorithm being most widely used.
The essence of interferometer direction finding is exactly that electromagnetic wave signal is utilized to reach different antenna element in the antenna array of constant spacing
Between time difference caused by phase relation determine the orientation of radio signal.Its do not need carry out subspace classification or
The requirement of real-time that signal wave reaches orientation estimation may be implemented in the calculating of the complexity such as spatial fit, and its precision is also relatively
Height, can satisfy the demand of aspect estimation, therefore obtain extensive development in engineering practice.
Although correlation interferometer algorithm is widely applied in practical wave is estimated up to orientation, the radius and signal wave of antenna
Long ratio will have a direct impact on the estimated accuracy of signal.When the distance between array element is greater than the half of signal wavelength, direction finding exists
Phase fuzzy problem, improvement before are the correlations that phase reflexive according to sine and cosine at maximum value further increases signal measurement
Property, the higher signal angle of precision is solved, but the measured value precision of signal is not still very high；When the distance between array element is small
There are direction finding angle baseline mirror symmetry fuzzy problems when the half of (or being equal to) signal wavelength.These two types of problems be all due to
In calculating caused by the uncertainty in orientation and the uncertainty of place quadrant.
Therefore, for this problem, the invention proposes improved correlation interferometer algorithms, it may be assumed that first converts a signal into
It between (- π~+π), is distinguish further according to quadrant difference where signal, and then solution baseline mirror symmetry obscures and phase mode
Paste problem.Finally, algorithm proposed by the invention improves the precision of aspect estimation by emulation experiment and analysis, have
Conducive to application in systems in practice.
Summary of the invention
To overcome above-mentioned image base AXIALLY SYMMETRIC PROBLEMS, phase fuzzy problem and the low disadvantage of precision, effectively solves these two types and ask
Topic and raising DOA estimated accuracy, it is an object of the invention to propose that a kind of adaptive DOA based on signal frequency estimates framework
Method.
Specifically, the application is achieved by the following technical solution:
In the smart antenna system for reaching orientation estimation module and these three parts of beamforming block including aerial array, wave
In system, the wave up to orientation estimation framework method the following steps are included:
(1) it is directed to different signal frequency models in advance, derives the DOA estimation evaluation criterion under different models, and really
Determine evaluation criterion；
(2) it after the antenna array receiver to data, is extracted from the data as analysis object signal frequency
Feature；
(3) classify described as analysis object signal frequency；
(4) use trigger mechanism, according to it is described as analysis object signal frequency select it is different it is predetermined described in
DOA estimates evaluation criterion；
(5) evaluation criterion is estimated using the DOA of triggering selection, the wave for obtaining the signal is estimated up to orientation.
Optionally, in (1) step, described derive using algorithm is correlation interferometer algorithm, quadrant classification method algorithm.
Optionally, the signal is divided into narrow band signal, broadband signal, cyclo-stationary signal.
Optionally, after (2) step,
(1) noise is classified, noise is divided into white Gaussian noise, coloured noise；
(2) it is directed to different noise models in advance, is gone out using correlation interferometer algorithm and quadrant classification algorithm algorithmic derivation
DOA under different models estimates evaluation criterion, and evaluation criterion is fixed up；
(3) different DOA is selected to estimate according to different noise models in different frequency using trigger mechanism
Evaluation criterion.
Optionally, signal source Dan Xinyuan.
Optionally, the aerial array is circle battle array, linear array.
It optionally, is narrow band signal, broadband signal, cyclo-stationary signal in detection signal, noise is white Gaussian noise, has
In the case where coloured noise, select for signal be narrow band signal, broadband signal, cyclo-stationary signal, noise be white Gaussian noise,
The DOA estimation evaluation criterion derived in the case where coloured noise.
The present invention has the following beneficial effects with respect to the prior art:
It is based on used rounding remainder quadrant classification method up to orientation estimation framework method according to the wave of invention, substantially not
It is successfully to solve mirror image base AXIALLY SYMMETRIC PROBLEMS and phase fuzzy problem, and improve DOA in the case where increasing computation complexity
The precision of estimation.
Detailed description of the invention
Fig. 1 is the figure that medelling indicates that intelligent antenna system is constituted.
Fig. 2 is the figure that medelling indicates the basic functional principle of adaptive antenna array in smart antenna.
Fig. 3 is the traditional process figure and flow chart of the invention that medelling indicates DOA estimation in smart antenna of the invention.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
Traditional DOA estimation framework is: after antenna array receiver to data, time delay being converted into phase
Delay, then handled by correlation interferometer algorithm, and then obtain the DOA estimation of signal source.
Based on this, it can effectively solve mirror image base AXIALLY SYMMETRIC PROBLEMS, phase fuzzy problem the present invention provides a kind of new and have
Effect improves the framework (especially under conditions of high frequency and low signal-to-noise ratio) for the precision that wave is estimated up to orientation, flow chart such as Fig. 3
It is shown.
Orientation (DOA) estimation module and Wave beam forming are being reached including aerial array (Antenna Array), wave
In the intelligent antenna system of (Adaptive Algorithm) the module three parts, wave of the invention estimates framework method up to orientation
Include the following steps.The step of presetting frequency threshold；From the antenna array receiver data, obtained from the signal data
The step of actual frequency；The actual frequency is compared with the size of the signal frequency threshold value and is divided into two kinds of different situations
The step of；When the actual frequency is less than the frequency threshold, signal is solved using traditional correlation interferometer algorithm
DOA result；When the actual frequency is more than or equal to the signal frequency threshold value, firstly, being reached not on the same day by the signal
The time difference of the linear array period of the day from 11 p.m. to 1 a.m is converted into phase difference, then resulting phase difference and (2*pi) are made ratio, finds out integer part and remainder
Part carries out dissection process followed by improved correlation interferometer algorithm by resulting complementing part using classifying
Step；
Wherein, improved correlation interferometer algorithm is recycled to carry out solving the phase difference of signal and as initial phase difference；
Finally, initial phase difference+(2*pi) * integer part is acquired final phase difference, then turn according between phase difference and signal
Change relationship solves the DOA estimation of signal.
Frequency threshold generally can flexibly be modified according to bay radius.The present invention is by taking 300MHz as an example.
As shown in figure 3, wave of the present invention estimates that framework method is the biography estimated towards DOA in smart antenna up to orientation
System flow chart and flow chart of the invention, mainly comprise the steps that
(1) after from antenna array receiver to data, the feature of signal frequency is extracted first.
(2) signal and noise are classified.For example signal is divided into: narrow band signal, broadband signal, cyclo-stationary signal etc.
Deng.Noise is divided into white Gaussian noise, coloured noise etc..
(3) it is directed to different signal and noise model in advance, is calculated using correlation interferometer algorithm and quadrant classification algorithm etc.
Method derives that the DOA under different models estimates evaluation criterion, and evaluation criterion is fixed up.
(4) it is selected according to different signal and noise model different using trigger mechanism in different frequency
DOA estimates evaluation criterion.For example if detection signal is broadband, cyclo-stationary signal, noise is coloured noise, then selection is directed to
Signal is broadband, cyclo-stationary signal, and noise estimates evaluation criterion by the DOA derived under coloured noise model.
(5) evaluation criterion for utilizing triggering selection, handles the data received, and the wave for obtaining signal reaches orientation
Estimation.
It is wave according to the present invention up to orientation estimation framework method, is based on used quadrant classification method, is not increasing substantially
The precision for estimating DOA in the case where computation complexity is improved.
Embodiments of the present invention are illustrated above, but the embodiment is only used as an example, is not to have
Limit the intention of invention scope.The present invention can be implemented by other various forms, can be in the range without departing from inventive concept
It is interior to carry out various changes.
Claims (7)
1. a kind of DOA estimates framework method, which is characterized in that
In the intelligent antenna system for reaching orientation estimation module and these three parts of beamforming block including aerial array, wave
In, the wave up to orientation estimation framework method the following steps are included:
(1) it is directed to different signal frequency models in advance, derives the DOA estimation evaluation criterion under different models, and determination is commented
Price card is quasi-；
(2) after the antenna array receiver to data, the spy as analysis object signal frequency is extracted from the data
Sign；
(3) classify described as analysis object signal frequency；
(4) use trigger mechanism, according to it is described as analyze object signal frequency select the different predetermined DOA to estimate
Count evaluation criterion；
(5) evaluation criterion is estimated using the DOA of triggering selection, the wave for obtaining the signal is estimated up to orientation.
2. DOA according to claim 1 estimates framework method, which is characterized in that (1) in step, the derivation uses calculation
Method is correlation interferometer algorithm, quadrant classification method algorithm.
3. DOA according to claim 1 estimates framework method, which is characterized in that the signal is divided into narrow band signal, broadband
Signal, cyclo-stationary signal.
4. DOA according to claim 1 estimates framework method, which is characterized in that
(2) after step,
(1) noise is classified, noise is divided into white Gaussian noise, coloured noise；
(2) it is directed to different noise models in advance, goes out difference using correlation interferometer algorithm and quadrant classification algorithm algorithmic derivation
DOA under model estimates evaluation criterion, and evaluation criterion is fixed up；
(3) trigger mechanism is used, in different frequency, selects different DOA to estimate evaluation according to different noise models
Standard.
5. DOA according to claim 1 estimates framework method, which is characterized in that signal source Dan Xinyuan.
6. DOA according to claim 1 estimates framework method, which is characterized in that the aerial array is circle battle array, linear array.
7. DOA according to claim 4 estimates framework method, which is characterized in that detecting signal as narrow band signal, broadband
Signal, cyclo-stationary signal, noise are white Gaussian noise, in the case where coloured noise, select to be directed to signal be narrow band signal, width
Band signal, cyclo-stationary signal, noise are evaluated by the DOA estimation derived in the case where white Gaussian noise, coloured noise
Standard.
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Citations (5)
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|---|---|---|---|---|
| US7477192B1 (en) * | 2007-02-22 | 2009-01-13 | L-3 Communications Titan Corporation | Direction finding system and method |
| WO2015109869A1 (en) * | 2014-01-24 | 2015-07-30 | 深圳大学 | High resolution doa estimation method and system |
| CN105301557A (en) * | 2015-11-06 | 2016-02-03 | 中国石油大学(华东) | Direction-of-arrival estimate configuration method |
| CN105425204A (en) * | 2015-11-03 | 2016-03-23 | 中国石油大学(华东) | DOA (Direction of Arrival) estimation configuration method |
| CN107356921A (en) * | 2017-08-11 | 2017-11-17 | 桂林电子科技大学 | A kind of method that frequency diversity array radar is positioned based on a frequency deviation target |
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2019
- 2019-08-06 CN CN201910719938.1A patent/CN110308418A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7477192B1 (en) * | 2007-02-22 | 2009-01-13 | L-3 Communications Titan Corporation | Direction finding system and method |
| WO2015109869A1 (en) * | 2014-01-24 | 2015-07-30 | 深圳大学 | High resolution doa estimation method and system |
| CN105425204A (en) * | 2015-11-03 | 2016-03-23 | 中国石油大学(华东) | DOA (Direction of Arrival) estimation configuration method |
| CN105301557A (en) * | 2015-11-06 | 2016-02-03 | 中国石油大学(华东) | Direction-of-arrival estimate configuration method |
| CN107356921A (en) * | 2017-08-11 | 2017-11-17 | 桂林电子科技大学 | A kind of method that frequency diversity array radar is positioned based on a frequency deviation target |
Non-Patent Citations (1)
| Title |
|---|
| 蔡丽萍 等: "改进相关干涉仪算法在DOA估计中的应用", 《计算机系统应用》 * |
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| CB03 | Change of inventor or designer information |
Inventor after: Chen Haihua Inventor after: Hu Jialiang Inventor after: Zhao Yu Inventor after: Yang Mingyang Inventor before: Chen Haihua Inventor before: Hu Jialiang Inventor before: Zhao Yu |
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Application publication date: 20191008 |
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