CN106713191B - Multi-stage search SAGE method - Google Patents

Multi-stage search SAGE method Download PDF

Info

Publication number
CN106713191B
CN106713191B CN201710113842.1A CN201710113842A CN106713191B CN 106713191 B CN106713191 B CN 106713191B CN 201710113842 A CN201710113842 A CN 201710113842A CN 106713191 B CN106713191 B CN 106713191B
Authority
CN
China
Prior art keywords
estimation
search
wave
doppler frequency
azimuth angle
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.)
Active
Application number
CN201710113842.1A
Other languages
Chinese (zh)
Other versions
CN106713191A (en
Inventor
李兵兵
李育
李进
郭姣
钱鑫
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xidian University
Original Assignee
Xidian University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Xidian University filed Critical Xidian University
Priority to CN201710113842.1A priority Critical patent/CN106713191B/en
Publication of CN106713191A publication Critical patent/CN106713191A/en
Application granted granted Critical
Publication of CN106713191B publication Critical patent/CN106713191B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Power Engineering (AREA)
  • Quality & Reliability (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention belongs to the technical field of wireless communication and discloses a multistage SAGE searching method, which comprises the following steps: calculating an estimate of the full data corresponding to the ith wave; and when the parameters of the l-th wave are re-estimated, performing multi-stage depth search by adopting the idea of reducing the search step by step in the re-estimation of the incident azimuth angle and the Doppler frequency. Compared with the original SAGE algorithm, the method has the advantages that the calculation amount is greatly reduced while the channel parameters are effectively estimated, and the calculation complexity of the original SAGE is improved.

Description

Multi-stage search SAGE method
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a multi-stage SAGE searching method.
Background
The channel model is the basis for the design and study of communication systems. In order to establish an effective channel model, channel parameters for accurately describing channel characteristics need to be extracted from channel measurement data, so that the research on a channel parameter estimation method with low computational complexity has certain significance. The Space-selective Generalized Expectation-maximization algorithm SAGE (Space-Alternating Generalized Expectation-maximization) algorithm proposed by B.H. Floury et al is widely used, SAGE can jointly estimate parameters of relative delay, incident azimuth, Doppler frequency, complex amplitude of multipath, whereas the traditional SAGE algorithm has to reduce the computation amount of the party due to the constant search step size in M steps (B.H. Floury, M.Tstudy, R.Heddergogo.D. Dahlhaus, and K.L.Pedersen, Channel parameter estimation in mobile radio environment using the SAGE algorithm, IEEE journal selected Areas communication, vol.17, vol.3, pp.434, 434).
In summary, the traditional SAGE algorithm adopts a constant search step size in the step M, so that the computation amount is high and the operation is complex.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a multi-stage search SAGE method.
The invention is realized in this way, a multistage SAGE search method, when the multistage SAGE search method adopts the parameter of the l-th wave to be estimated again, the multistage depth search is carried out by adopting the step-by-step reduction of the search step length in the re-estimation of the incidence azimuth angle and the Doppler frequency, and the method specifically comprises the following steps:
(1) in the iteration of estimating the ith incident wave parameter again by the SAGE algorithm, in M steps, firstly estimating the time delay, namely:
Figure BDA0001235125770000021
(2) re-estimating the l-th incident wave azimuth angle philFirstly, the 1 st stage search step length delta phi is adopted1Searching the maximum value of | z | in the searching range of the incident azimuth angle to obtain philLevel 1 re-estimation of
Figure BDA0001235125770000022
Namely:
Figure BDA0001235125770000023
(3) in that
Figure BDA0001235125770000024
Nearby smaller level 2 search step delta phi2Searching the maximum value of | z |, to obtain philLevel 2 re-estimation of
Figure BDA0001235125770000025
Namely:
Figure BDA0001235125770000026
(4) re-estimating the first incident wave Doppler frequency vlFirstly, the 1 st level search step length delta v is adopted1Searching the maximum value of | z | in the Doppler frequency searching range to obtain vlLevel 1 re-estimation of
Figure BDA0001235125770000027
Namely:
Figure BDA0001235125770000028
(5) in that
Figure BDA0001235125770000029
Nearby smaller level 2 search step size Deltav2Searching the maximum value of | z |, obtaining vlLevel 2 re-estimation of
Figure BDA00012351257700000210
Namely:
Figure BDA00012351257700000211
note the book
Figure BDA00012351257700000212
(6) Calculating the re-estimation of the complex amplitude of the ith incident wave, namely:
Figure BDA00012351257700000213
wherein:
Figure BDA00012351257700000214
Dithe ith observation window is shown, I is the number of the observation windows, tau, phi and nu respectively show time delay, incident azimuth angle and Doppler frequency,
Figure BDA0001235125770000031
means theta ═ theta [ theta ]1,...,θL]The previous estimation of the time of the current time,
Figure BDA0001235125770000032
respectively representing the re-estimation of the time delay, the incident azimuth angle, the Doppler frequency and the complex amplitude of the l-th wave, U1Is included in the search range of the incident azimuth angle
Figure BDA0001235125770000033
A small range of (U)2Is included in the Doppler frequency search range
Figure BDA0001235125770000034
A small range of (A), PuIs the power of u (t), c (φ) is the steering vector of the array,
Figure BDA0001235125770000035
is the complete data X corresponding to the first wavel(t) estimation; by using
Figure BDA0001235125770000036
De-updating
Figure BDA0001235125770000037
And update accordingly
Figure BDA0001235125770000038
Further, when the parameter of the i-th wave is re-estimated, before performing the multi-level depth search by adopting the idea of reducing the search step size step by step in the re-estimation of the incident azimuth angle and the doppler frequency, the following steps are required: an estimate of the full data corresponding to the ith wave is calculated.
Further, in the iteration of the l-th wave parameter, the given received signal is in the observation window D0The above observation y (t) is first obtained through step E, and then the complete data X corresponding to the first wave is obtainedl(t) estimation:
Figure BDA0001235125770000039
wherein, βl＝1。
Another object of the present invention is to provide a wireless channel parameter extraction system using the multi-stage search SAGE method.
The invention has the advantages and positive effects that: compared with the traditional SAGE algorithm, the method has the advantages that the operation amount can be reduced by 1 magnitude while the channel parameters are effectively estimated, and the calculation complexity of the original SAGE is improved by the multi-stage search SAGE method.
Compared with the traditional method of constant search step size adopted in the M step of SAGE algorithm, the method has the following advantages:
in the constant search step method, the estimation accuracy of the algorithm is reduced as the selected constant search step is increased. Compared with a constant search step length method, when the operation amount is relative, the multi-stage search method adopts a multi-stage search step length strategy, so that the estimation accuracy of the multi-stage search method is higher, and the subsequent simulation experiment 1 compares the estimation accuracy of the two methods under different signal-to-noise ratios;
in the constant search step length method, the operation amount of the algorithm is greatly increased along with the reduction of the selected constant search step length. When the constant search step size is decreased by 1 order of magnitude, the operation amount of the algorithm is increased by 1 order of magnitude. Compared with the constant step size searching method, when the 2 nd level searching step size of the method is the same as the constant searching step size in the constant step size searching method, the operation amount of the method is reduced by 1 order of magnitude, and meanwhile, the estimation accuracy is ensured, and the following simulation experiment 2 compares the operation amount with the constant searching step size. In addition, if it is desired to improve the estimation accuracy, the above-mentioned idea can be used to perform a 3 rd, 4 th and … th level search of the incident azimuth angle and the doppler frequency in a two-level search method.
And thirdly, simulation results show that when the estimation accuracy of the two methods is relatively good, the operation amount is reduced by 1 order of magnitude when a multi-stage search method is adopted compared with a constant search step length method. The multi-stage search SAGE channel parameter estimation method provided by the invention reduces the computational complexity of the original SAGE algorithm.
Drawings
FIG. 1 is a flow chart of a method for multi-stage search SAGE provided by an embodiment of the present invention.
Fig. 2 is a schematic diagram of an average relative error versus snr curve of 2 multipath incident azimuth estimates for 100 monte carlo simulations provided by an embodiment of the present invention.
Fig. 3 is a diagram of the average relative error versus snr curve for 2 multipath doppler frequency estimates for 100 monte carlo simulations provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a multi-stage search SAGE method provided by an embodiment of the present invention includes the steps of:
s101: calculating an estimate of the full data corresponding to the ith wave;
s102: and when the parameters of the l-th wave are re-estimated, performing multi-stage depth search by adopting the idea of reducing the search step by step in the re-estimation of the incident azimuth angle and the Doppler frequency.
The application of the principles of the present invention will now be described in further detail with reference to specific embodiments.
Example 1:
the multi-stage search SAGE algorithm of the embodiment of the invention comprises the following steps:
step one, calculating the estimation of the complete data corresponding to the l wave;
the estimate of the full data corresponding to the ith wave is calculated as follows:
the detection signal may be expressed as:
Figure BDA0001235125770000051
wherein the content of the first and second substances,
Figure BDA0001235125770000052
is a burstSignal, [ a ]0,a1,...,aK-1]Is a probe sequence of length K, p (T) is duration TpOf the shaped pulse, duration T of the burst signala＝KTpPower of the probe signal is Pu。
Receiver configuration M bits in
Figure BDA0001235125770000053
Assuming that there are L plane waves reflected by the mirror at the receiver. The contribution of the ith wave to the M baseband signals output by the receive array can be expressed as a vector:
Figure BDA0001235125770000054
wherein, thetal＝[τl,φl,νl,αl]Is a vector containing the parameters of the l-th wave with its relative time delay taulAngle of incidence philDoppler frequency vlComplex amplitude αlCharacterizing;
c(φ)＝[c1(φ),...,cM(φ)]Tis the steering vector of the antenna array;
cm(φ)＝fm(φ)exp{j2πλ-1<e(φ),rm>},m＝1,...,M；
λ is the wavelength, e (φ) is the unit vector pointing in the direction determined by φ, fm(phi) is the complex electric field pattern of the mth array element,<·，·>a scalar product is represented. [. the]TIndicating transposition.
The received signals output by the antenna array are:
Figure BDA0001235125770000055
wherein N (t) ═ N1(t),...,NM(t)]TM-dimensional complex white Gaussian noise vector representing standard, definition
Figure BDA0001235125770000061
Wherein θ ═ θ1,...,θL]。
Observing the received signal over a window comprising I time segments D spaced apartiI1, I, each time segment having a length TaThe distance between the centers of 2 adjacent time periods is Tf≥Ta(ii) a Then the observation time is ITaThe observation range is (I-1) Tf+TaThe observation window may be expressed as:
Figure BDA0001235125770000062
the proposed multi-stage search SAGE algorithm only re-estimates the quantity to be estimated theta ═ theta in each iteration1,...,θL]While leaving the other components unchanged. The previous estimate of θ is
Figure BDA0001235125770000063
θlIs re-estimated as
Figure BDA0001235125770000064
The same holds true for the other two expressions; in the iteration of re-estimating the l-th wave parameter, the given received signal is in the observation window D0The above observation y (t) is first obtained through step E, and then the complete data X corresponding to the first wave is obtainedl(t) estimation:
Figure BDA0001235125770000065
wherein, βl＝1。
Step two, when the parameter of the l wave is re-estimated, adopting the idea of reducing the search step size step by step to carry out multi-stage depth search in the re-estimation of the incident azimuth angle and the Doppler frequency;
when the parameters of the l wave are re-estimated, the idea of reducing the search step length step by step is adopted to carry out multi-stage depth search in the re-estimation of the incident azimuth angle and the Doppler frequency according to the following steps:
when the parameters of the l-th wave are re-estimated, a thought of reducing the search step length step by step is adopted for carrying out multi-stage depth search in re-estimation of the incident azimuth angle and the Doppler frequency, and the specific method is as follows:
in the iteration of estimating the ith incident wave parameter again by the SAGE algorithm, in M steps, firstly estimating the time delay, namely:
Figure BDA0001235125770000066
then, the azimuth angle phi of the ith incident wave is estimated againlFirstly, the 1 st stage search step length delta phi is adopted1Searching the maximum value of | z | in the searching range of the incident azimuth angle to obtain philLevel 1 re-estimation of
Figure BDA0001235125770000067
Namely:
Figure BDA0001235125770000071
then again on
Figure BDA0001235125770000072
Nearby smaller level 2 search step delta phi2Searching the maximum value of | z |, to obtain philLevel 2 re-estimation of
Figure BDA0001235125770000073
Namely:
Figure BDA0001235125770000074
then, the Doppler frequency v of the first incident wave is estimated againlFirstly, the 1 st level search step length delta v is adopted1Searching the maximum value of | z | in the Doppler frequency searching range to obtain vlLevel 1 re-estimation of
Figure BDA0001235125770000075
Namely:
Figure BDA0001235125770000076
then again on
Figure BDA0001235125770000077
Nearby smaller level 2 search step size Deltav2Searching the maximum value of | z |, obtaining vlLevel 2 re-estimation of
Figure BDA0001235125770000078
Namely:
Figure BDA0001235125770000079
note the book
Figure BDA00012351257700000710
And finally, calculating the re-estimation of the complex amplitude of the ith incident wave, namely:
Figure BDA00012351257700000711
wherein:
Figure BDA00012351257700000712
Dithe ith observation window is shown, I is the number of the observation windows, tau, phi and nu respectively show time delay, incident azimuth angle and Doppler frequency,
Figure BDA00012351257700000713
means theta ═ theta [ theta ]1,...,θL]The previous estimation of the time of the current time,
Figure BDA00012351257700000714
respectively representing the time delay, incidence azimuth, Doppler frequency and complex amplitude of the first waveEstimate, U1Is included in the search range of the incident azimuth angle
Figure BDA00012351257700000715
A small range of (U)2Is included in the Doppler frequency search range
Figure BDA00012351257700000716
A small range of (A), PuIs the power of u (t), c (φ) is the steering vector of the array,
Figure BDA00012351257700000717
is the complete data X corresponding to the first wavel(t) estimation; by using
Figure BDA0001235125770000081
De-updating
Figure BDA0001235125770000082
And update accordingly
Figure BDA0001235125770000083
Given 2 multipaths, the parameters are shown in table 1. The carrier frequency is 2GHz, the receiving antenna array adopts a linear array with M-10 array elements, the spacing of the array elements is half of the wavelength, a (T) adopts 511 PN codes, the chip width is 3.69 mu s, each chip adopts 4 sampling points, and the sampling interval T iss＝0.9225μs。
TABLE 1 multipath parameters given
Figure BDA0001235125770000084
The application effect of the present invention will be described in detail with reference to the simulation.
Simulation experiment 1
1. Comparison of estimation accuracy
1.1 method with constant search step
The search ranges of the incidence azimuth angle and the Doppler frequency are respectively [0,180 DEG ]]、[-50Hz,50Hz]Constant searchThe cable step length is respectively 3 degrees and 2Hz, and the searching step number is respectively
Figure BDA0001235125770000085
Then, 100 Monte Carlo simulations are performed, and the curves of the average relative error of 2 multipath incident azimuth angles and Doppler frequency estimates with respect to the signal-to-noise ratio are shown in FIG. 2 and FIG. 3, and the relative error is formulated
Figure BDA0001235125770000086
And calculating, wherein b represents an estimated value, and a represents a true value.
1.2 method Using Multi-level search
The 1 st stage search step length of incidence azimuth angle and Doppler frequency is 3.6 deg. and 2.5Hz, and the search ranges are 0,180 deg. respectively]、[-50Hz,50Hz]With level 2 search step size using level 1 search step size
Figure BDA0001235125770000087
That is, 0.72 Hz and 0.5Hz, the search ranges are the neighborhoods centered on the 1 st re-estimation of the corresponding incident azimuth angle and Doppler frequency, respectively, and having radii of 3.6 Hz and 2.5Hz, respectively, and the corresponding search steps are
Figure BDA0001235125770000088
The average relative error versus signal-to-noise ratio for 2 multipath incident azimuth and doppler frequency estimates is plotted for 100 monte carlo simulations as shown in figures 2 and 3.
Simulation results show that the signal-to-noise ratio is [ -10dB,15dB]In the range, the search steps of the incident azimuth angle and the Doppler frequency in the M steps of the two methods are respectively
Figure BDA0001235125770000091
Compared with a constant search step length method, the multi-stage search method has higher estimation accuracy.
Simulation experiment 2
1 comparison of the quantities of operation
1.1 method with constant search step
Incident lightThe search step lengths of azimuth angle and Doppler frequency are respectively 0.1 degree and 0.1Hz, and the search ranges are respectively 0,180 degrees]、[-50Hz,50Hz]The search operand steps of the incidence azimuth angle and the Doppler frequency in the M steps are respectively
Figure BDA0001235125770000092
The signal-to-noise ratio was 15dB, the running time of the algorithm was about 2800 seconds using a 2G memory computer for simulation, and the estimated results are shown in table 2.
1.2 method Using Multi-level search
The 1 st stage search step length of the incidence azimuth angle and Doppler frequency is 1 degree and 1Hz, and the search ranges are 0,180 degrees respectively]、[-50Hz,50Hz]The 2 nd level search step length is 0.1 DEG and 0.1Hz, the search ranges are neighborhoods which take the 1 st level re-estimation of the corresponding incident azimuth angle and Doppler frequency as the center and take 1 DEG and 1Hz as the radius respectively. The search operand step number of the incident azimuth angle and the Doppler frequency in M steps is
Figure BDA0001235125770000093
Figure BDA0001235125770000094
The signal to noise ratio was 15db, the running time of the algorithm was about 260 seconds using a 2G memory computer for simulation, and the estimated results are shown in table 3.
TABLE 2 multipath parameters estimated by constant search step method
Figure BDA0001235125770000095
TABLE 3 multipath parameters estimated by the multilevel search method
Figure BDA0001235125770000096
Figure BDA0001235125770000101
Simulation results show that when the estimation accuracy of the two methods is relatively good, the calculation amount is greatly reduced when a multi-stage searching method is adopted compared with a constant searching step length method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (1)

1. The multistage search SAGE method is characterized in that when parameters of an l-th wave are re-estimated, multistage depth search is performed by adopting step-by-step reduction of search step length in re-estimation of an incident azimuth angle and Doppler frequency, and specifically comprises the following steps:
(1) in the iteration of estimating the ith incident wave parameter again by the SAGE algorithm, in M steps, firstly estimating the time delay, namely:
Figure FDA0002590731360000011
(2) re-estimating the l-th incident wave azimuth angle philFirstly, the 1 st stage search step length delta phi is adopted1Searching the maximum value of | z | in the searching range of the incident azimuth angle to obtain philLevel 1 re-estimation of
Figure FDA0002590731360000012
Namely:
Figure FDA0002590731360000013
(3) in that
Figure FDA0002590731360000014
Nearby smaller level 2 search step delta phi2Searching the maximum value of | z |, to obtain philLevel 2 re-estimation of
Figure FDA0002590731360000015
Namely:
Figure FDA0002590731360000016
(4) re-estimating the first incident wave Doppler frequency vlFirstly, the 1 st level search step length delta v is adopted1Searching the maximum value of | z | in the Doppler frequency searching range to obtain vlLevel 1 re-estimation of
Figure FDA0002590731360000017
Namely:
Figure FDA0002590731360000018
(5) in that
Figure FDA0002590731360000019
Nearby smaller level 2 search step size Deltav2Searching the maximum value of | z |, obtaining vlLevel 2 re-estimation of
Figure FDA00025907313600000110
Namely:
Figure FDA00025907313600000111
note the book
Figure FDA00025907313600000112
(6) Calculating the re-estimation of the complex amplitude of the ith incident wave, namely:
Figure FDA00025907313600000113
wherein:
Figure FDA0002590731360000021
Dithe ith observation window is shown, I is the number of the observation windows, tau, phi and nu respectively show time delay, incident azimuth angle and Doppler frequency,
Figure FDA0002590731360000022
means theta ═ theta [ theta ]1,...,θL]The previous estimation of the time of the current time,
Figure FDA0002590731360000023
respectively representing the re-estimation of the time delay, the incident azimuth angle, the Doppler frequency and the complex amplitude of the l-th wave, U1Is included in the search range of the incident azimuth angle
Figure FDA0002590731360000024
A small range of (U)2Is included in the Doppler frequency search range
Figure FDA0002590731360000025
A small range of (A), PuIs the power of u (t), c (φ) is the steering vector of the array,
Figure FDA0002590731360000026
is the complete data X corresponding to the first wavel(t) estimation; by using
Figure FDA0002590731360000027
De-updating
Figure FDA0002590731360000028
And update accordingly
Figure FDA0002590731360000029
When the parameter of the l-th wave is re-estimated, before performing multi-level depth search by adopting the idea of reducing the search step by step in re-estimation of the incident azimuth angle and the Doppler frequency, the following steps are required: calculating an estimate of the full data corresponding to the ith wave;
in the iteration of the l wave parameter, the given received signal is in the observation window D0The above observation y (t) is first obtained through step E, and then the complete data X corresponding to the first wave is obtainedl(t) estimation:
Figure FDA00025907313600000210
wherein, βl＝1。
CN201710113842.1A 2017-02-28 2017-02-28 Multi-stage search SAGE method Active CN106713191B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710113842.1A CN106713191B (en) 2017-02-28 2017-02-28 Multi-stage search SAGE method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710113842.1A CN106713191B (en) 2017-02-28 2017-02-28 Multi-stage search SAGE method

Publications (2)

Publication Number Publication Date
CN106713191A CN106713191A (en) 2017-05-24
CN106713191B true CN106713191B (en) 2020-09-08

Family

ID=58911969

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710113842.1A Active CN106713191B (en) 2017-02-28 2017-02-28 Multi-stage search SAGE method

Country Status (1)

Country Link
CN (1) CN106713191B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3900284B1 (en) * 2018-12-17 2023-11-08 U-blox AG Estimating one or more characteristics of a communications channel
CN114362852B (en) * 2021-12-03 2023-03-28 同济大学 Doppler parameter estimation method based on improved SAGE

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6944557B2 (en) * 2002-01-31 2005-09-13 Fujitsu Limited Ultrasonic length measuring apparatus and method for coordinate input
CN1674679A (en) * 2005-05-12 2005-09-28 北京中星微电子有限公司 Video frequency data compressing method and apparatus for optimizing search algorithm
CN101072066A (en) * 2006-05-08 2007-11-14 中兴通讯股份有限公司 Intelligent antenna realizing method for CDMA communication system
CN101109793A (en) * 2007-08-01 2008-01-23 上海华龙信息技术开发中心 Method for fast capturing satellite and implementing equipment thereof
CN101582864A (en) * 2009-06-18 2009-11-18 西安电子科技大学 SAGE channel estimation method based on partial interference cancellation
CN101588328A (en) * 2009-07-10 2009-11-25 中国科学院上海微系统与信息技术研究所 A kind of combined estimation method of high-precision wireless channel parameterized model
CN101566505B (en) * 2009-03-24 2011-10-12 中科院广州电子技术有限公司 Method for quickly calculating dominant wavelength of light-emitting diode
WO2015060888A1 (en) * 2013-10-24 2015-04-30 Interhealth Nutraceuticals, Inc. Method of reducing exercise-induced joint pain in non-arthritic mammals
CN104618725A (en) * 2015-01-15 2015-05-13 华侨大学 Multi-view video coding algorithm combining quick search and mode optimization
CN105939299A (en) * 2016-06-08 2016-09-14 西安电子科技大学 Channel parameter estimation method based on improved SAGE algorithm
US10365350B2 (en) * 2015-01-29 2019-07-30 Nidec Corporation Neural network-based radar system having independent multibeam antenna

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10149303A1 (en) * 2001-10-05 2003-07-10 Elektrobit Ag Bubikon Method for the device for examining a signal transmission system
DE10303475B3 (en) * 2003-01-29 2004-10-07 Infineon Technologies Ag Maximum likelihood estimation of the channel coefficients and the DC offset in a digital baseband signal of a radio receiver using the SAGE algorithm
CN100561149C (en) * 2008-09-17 2009-11-18 电子科技大学 The method of discrimination of invalid pixel among a kind of UFPA
CN101982953B (en) * 2010-11-04 2013-06-26 中国科学院上海微系统与信息技术研究所 Frequency domain multi-dimensional parameterized model of broadband wireless communication channel and modeling method
CN104407323B (en) * 2014-12-11 2018-01-26 中国工程物理研究院电子工程研究所 A kind of high dynamic low signal-to-noise ratio spread-spectrum signal pseudo-code time-delay measuring method
CN105976032A (en) * 2016-05-11 2016-09-28 河南理工大学 Social learning method facing function optimization
CN105978833A (en) * 2016-06-29 2016-09-28 西安电子科技大学 Improved SAGE channel parameter estimation method

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6944557B2 (en) * 2002-01-31 2005-09-13 Fujitsu Limited Ultrasonic length measuring apparatus and method for coordinate input
CN1674679A (en) * 2005-05-12 2005-09-28 北京中星微电子有限公司 Video frequency data compressing method and apparatus for optimizing search algorithm
CN101072066A (en) * 2006-05-08 2007-11-14 中兴通讯股份有限公司 Intelligent antenna realizing method for CDMA communication system
CN101109793A (en) * 2007-08-01 2008-01-23 上海华龙信息技术开发中心 Method for fast capturing satellite and implementing equipment thereof
CN101566505B (en) * 2009-03-24 2011-10-12 中科院广州电子技术有限公司 Method for quickly calculating dominant wavelength of light-emitting diode
CN101582864A (en) * 2009-06-18 2009-11-18 西安电子科技大学 SAGE channel estimation method based on partial interference cancellation
CN101588328A (en) * 2009-07-10 2009-11-25 中国科学院上海微系统与信息技术研究所 A kind of combined estimation method of high-precision wireless channel parameterized model
WO2015060888A1 (en) * 2013-10-24 2015-04-30 Interhealth Nutraceuticals, Inc. Method of reducing exercise-induced joint pain in non-arthritic mammals
CN104618725A (en) * 2015-01-15 2015-05-13 华侨大学 Multi-view video coding algorithm combining quick search and mode optimization
US10365350B2 (en) * 2015-01-29 2019-07-30 Nidec Corporation Neural network-based radar system having independent multibeam antenna
CN105939299A (en) * 2016-06-08 2016-09-14 西安电子科技大学 Channel parameter estimation method based on improved SAGE algorithm

Also Published As

Publication number Publication date
CN106713191A (en) 2017-05-24

Similar Documents

Publication Publication Date Title
CN110007148B (en) Single-frequency signal frequency estimation method based on comprehensive interpolation of discrete spectrum phase and amplitude
CN103308889A (en) Passive sound source two-dimensional DOA (direction of arrival) estimation method under complex environment
CN113595666B (en) Channel prediction method and system of large-scale MIMO system based on time-frequency joint correlation
CN106713191B (en) Multi-stage search SAGE method
CN104378787B (en) Flat fast fading long range channel prediction method based on Prony algorithm
CN102866383B (en) Direction-of-arrival estimation method based on self-adaption filtering in spatial domain
Luo et al. A space-frequency joint detection and tracking method for line-spectrum components of underwater acoustic signals
CN107966676A (en) Array antenna angle and information source number combined estimation method under complicated noise
CN109411890B (en) Single-station high-precision three-dimensional positioning method based on multiple antennas
CN111263321A (en) Method for improving indoor ultra-wideband positioning accuracy of TOA (time of arrival)
CN104459635A (en) Self-adaptive air filtering method based on iterative shrinkage weighted fusion
CN103701515A (en) Digital multi-beam forming method
CN108416105B (en) Steady adaptive beam-forming algorithm under pulse and Gaussian noise
CN104076324A (en) Method for estimating high-accuracy arrival direction without knowing information source number
RU2517365C2 (en) Method of detecting and finding direction of radio-frequency sources at one frequency
CN101499816B (en) Incoming wave arrival angle estimating method and apparatus
CN113406562B (en) TOA and DOA combined estimation dimension reduction method in Beidou and ultra-wideband system
CN109861770B (en) Broadband signal detection method based on beam forming output power combination
Ichige et al. An explicit high-resolution DOA estimation formula for two wave sources
Chahbi et al. A joint AOA, AOD and delays estimation of multipath signals based on beamforming techniques
Velickovic et al. The performance of the modified GCC technique for differential time delay estimation in the cooperative sensor network
CN111722178A (en) Far-field narrow-band signal incoming wave direction estimation method based on numerical solution of directivity model
CN111257827A (en) High-precision non-line-of-sight tracking and positioning method
Shinriki et al. Pulse compression for a simple pulse
CN116827725B (en) Iterative channel parameter estimation method based on genetic algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant