CN109239654A - Positioning using TDOA result method for correcting error neural network based - Google Patents
Positioning using TDOA result method for correcting error neural network based Download PDFInfo
- Publication number
- CN109239654A CN109239654A CN201810952670.1A CN201810952670A CN109239654A CN 109239654 A CN109239654 A CN 109239654A CN 201810952670 A CN201810952670 A CN 201810952670A CN 109239654 A CN109239654 A CN 109239654A
- Authority
- CN
- China
- Prior art keywords
- neural network
- sensor
- positioning
- radiation source
- source
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0205—Details
- G01S5/021—Calibration, monitoring or correction
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The present invention relates to a kind of positioning using TDOA result method for correcting error neural network based, include: sensor being divided into multiple groups according to different reference clocks；Calibration source time difference information known to position is successively obtained using sensor, calibration source is positioned using the priori observation and each calibration source time difference information, iteration of sensor position, obtains calibration source positioning result；Study is trained to multilayer feedforward neural network using calibration source positioning result and calibration source known position information；The time difference information about target radiation source is obtained by sensor, and combines the priori observation of sensor position, iteration positions target radiation source, obtains target radiation source positioning result；Target radiation source positioning result is input in neural network, is output it as the final positioning result of target radiation source.The positioning of target under the conditions of present invention guarantee clock jitter and sensor position error exist simultaneously improves target radiation source positioning accuracy, and performance is stable, reliable, and efficiently.
Description
Technical field
The invention belongs to radio signal field of locating technology, in particular to a kind of positioning using TDOA knot neural network based
Fruit method for correcting error is applicable to exist simultaneously the positioning scene of clock jitter and sensor error.
Background technique
It is well known that localization of emitter is in all multiplexings such as wireless communication, telemetering navigation, Situation Awareness, electronic countermeasures
Journey field is with a wide range of applications.The basic process of radiation source positioning is to extract to have with target position from electromagnetic signal
Then the parameter (also referred to as positioning view measurement) of pass calculates the location information of target based on these parametric solutions again.It is fixed for radiation source
The observed quantity of position be related to sky, when, the multiple domains parameter such as frequency, energy, wherein reaching time-difference (can be equivalent to range difference) is to apply
More frequent a kind of observed quantity.Time difference locating technology technology is by multiple receivers (or sensor) collected target
Signal arrival time difference is positioned, and the time difference that target radiation source reaches two different receivers determines a hyperboloid
The location information of target can be obtained in (line), multiple hyperboloid (line) intersections.With modern communication technology and time difference measurement technology
Continuous development, time difference position technolot has become one of localization of emitter means of mainstream the most.
According to the algebraic characteristic of time difference observational equation, domestic and foreign scholars propose the positioning using TDOA side of many function admirables
Method, however, the positioning accuracy big city of these methods is influenced by clock jitter and sensor position error.Clock jitter is
The error as caused by the asynchronous-sampling between different receivers, and sensor position error usually occurs to be mounted in receiver
In the case where the motion platforms such as airborne or carrier-borne.In order to eliminate the influence of clock jitter, existing technology is usually inclined by clock
Difference regards unknown parameter as, and it is carried out Combined estimator with target radiation source location parameter.In order to inhibit sensor position to miss
The influence of difference, existing technology can be divided into two major classes, and the first kind is to be dissolved into the statistical property of sensor position error to determine
In the method for position, to obtain the localization method of robust, the calculation amount of such method is relatively small, but can not further improve sensing
The position precision of device；Second class is then to carry out Combined estimator, the meter of such method to sensor position and target emanation source position
Calculation amount is relatively high, but available more accurate sensor position estimated value.It should be pointed out that by theoretical performance
The restriction of limit, the above method are all extremely limited to the promotion of TOA difference locating accuracy, and usually require sensor position error
Priori statistics, but this is difficult to obtain in practice.
Summary of the invention
For the influence of clock jitter and sensor position error etc., the present invention provides a kind of time difference neural network based
Positioning result method for correcting error promotes the TOA difference locating accuracy under the conditions of clock jitter and sensor position error by a relatively large margin, leads to
It crosses and is placed around the accurately known calibration source in position in target radiation source, and obtain the time difference about calibration source using sensor
Then information promotes the positioning accuracy to target radiation source using time difference information or the positioning result to target radiation source carries out
Correction guarantees the positioning of target under the conditions of clock jitter and sensor position error exist simultaneously, improves to target radiation source
Positioning accuracy.
According to design scheme provided by the present invention, a kind of positioning using TDOA result method for correcting error neural network based, packet
Containing following content:
Sensor is divided into multiple groups according to different reference clocks；The sensor being utilized respectively in the multiple groups successively obtains
Calibration source time difference information known to position, priori observation and each calibration source time difference information using sensor position are led to
It crosses iteration successively to position calibration source, obtains calibration source positioning result；
Study is trained to multilayer feedforward neural network using calibration source positioning result and calibration source known position information,
Multilayer feedforward neural network after being trained；
The time difference information about target radiation source is obtained by sensor, and combines the priori observation of sensor position,
Successively target radiation source is positioned by iteration, obtains target radiation source positioning result；
Target radiation source positioning result is input in the multilayer feedforward neural network after training and is tested, it is more to obtain this
Layer feedforward neural network exports and using the output as the final positioning result of target radiation source.
It is above-mentioned, by M sensor according to reference clock different demarcation at N group, the sensor in same group is with reference to same
One local clock, there are clock jitters between group and group；Calibration source known to D position is placed, is successively obtained using sensor
Obtain the time difference information about each calibration source；And it is obtained using the priori observation of sensor position and for each calibration source
The time difference information obtained, successively positions calibration source by Taylor series alternative manner, obtains calibration source positioning result.
Above-mentioned, in one master reference of M sensor settings, other are aiding sensors.
Preferably, in the time difference information that each calibration source is obtained using sensor, according to the known location vector of calibration source
With sensor position vector, each correction source signal is obtained using modulated parameter estimating method and reaches an aiding sensors and master
Time difference between sensor；By it is each correction source signal M-1 time difference by signal space spread speed be converted into away from
Deviation, and the M-1 range difference is merged into observation vector.
Further, its covariance matrix is calculated according to observation vector, and position is joined by Taylor series alternative manner
Number is solved, and the positioning result of calibration source is obtained.
It is above-mentioned, using calibration source positioning result as the input value of multilayer feedforward neural network, by calibration source known bits confidence
Breath is used as multilayer feedforward neural network output valve, using the data of calibration source known to D position by BP algorithm to the multilayer
Feedforward neural network is trained study, the multilayer feedforward neural network after obtaining training.
Preferably, multilayer feedforward neural network is the two-dimensional planar location model of neuron there are two input and output contain,
Or contain three dimension location model there are three neuron for input and output.
Above-mentioned, the position vector about target radiation source is obtained by sensor, and combine sensor position vector, benefit
The target radiation source, which is obtained, with modulated parameter estimating method reaches a time difference between aiding sensors and master reference.
Preferably, for M sensor, the M-1 time difference is converted into M-1 distance using signal space spread speed
Difference, and the M-1 range difference is merged into observation vector；Its covariance matrix is calculated according to observation vector, and is utilized embedded
The location parameter of Taylor series alternative manner estimation target radiation source.
Above-mentioned, successively target radiation source is positioned by iteration to obtain in target radiation source positioning result and include
The deviations as caused by sensor position error, after the target radiation source positioning result that will acquire is input to training
It is tested in multilayer feedforward neural network, eliminates the deviations as caused by sensor position error, test output is
The final positioning result of the target radiation source.
Beneficial effects of the present invention:
The present invention is based on location information Training Multilayer Feedforward Neural Networks provided by calibration source near target radiation source, benefits
It can effectively eliminate the deviations as caused by sensor position error with the neural network, and utilize " embedded " Taylor grade
Number alternative manners can also effectively inhibit the influence of clock jitter, to be obviously improved clock jitter and sensor position error is same
When existence condition under TOA difference locating accuracy, Robust Performance, operation efficiently, have stronger practical application value.
Detailed description of the invention:
Fig. 1 is the positioning using TDOA result method for correcting error flow diagram in embodiment；
Fig. 2 is sensor group schematic diagram in embodiment；
Fig. 3 is multilayer feedforward neural network schematic diagram one in embodiment；
Fig. 4 is multilayer feedforward neural network schematic diagram two in embodiment；
Fig. 5 is neural metwork training result schematic diagram in embodiment；
Fig. 6 is that positioning result spreads schematic diagram in embodiment；
Fig. 7 is that target position estimates that root-mean-square error is shown with the change curve of range difference observation error standard deviation in embodiment
It is intended to.
Specific embodiment:
The present invention is described in further detail with technical solution with reference to the accompanying drawing, and detailed by preferred embodiment
Describe bright embodiments of the present invention in detail, but embodiments of the present invention are not limited to this.
For the influence of clock jitter and sensor position error etc., the embodiment of the present invention is provided a kind of based on nerve net
The positioning using TDOA result method for correcting error of network includes following content:
Sensor is divided into multiple groups according to different reference clocks；The sensor being utilized respectively in the multiple groups successively obtains
Calibration source time difference information known to position, priori observation and each calibration source time difference information using sensor position are led to
It crosses iteration successively to position calibration source, obtains calibration source positioning result；
Study is trained to multilayer feedforward neural network using calibration source positioning result and calibration source known position information,
Multilayer feedforward neural network after being trained；
The time difference information about target radiation source is obtained by sensor, and combines the priori observation of sensor position,
Successively target radiation source is positioned by iteration, obtains target radiation source positioning result；
Target radiation source positioning result is input in the multilayer feedforward neural network after training and is tested, it is more to obtain this
Layer feedforward neural network exports and using the output as the final positioning result of target radiation source.
Shown in Figure 1, the accurately known calibration source in several positions is placed in timesharing first near target radiation source, then
Calibration source is positioned using the priori observation of sensor position and for each calibration source time difference information obtained,
Localization method uses alternative manner, effectively inhibits the influence of clock jitter, but can not inhibit the influence of sensor position error；
Then the positioning result and its actual position Training Multilayer Feedforward Neural Networks of each calibration source are utilized, and calibration source will be determined
Input value of the position result as neural network, using its actual position as the output valve of neural network, after training terminates just
It can use this neural network and correct the positioning using TDOA deviation as caused by sensor position prior uncertainty.Last same use changes
Positioning using TDOA is carried out to target radiation source for method, and is located result and is input to trained multilayer feedforward neural network
In, the output valve of the neural network is the positioning result of target, significantly improves the positioning accuracy of target, and high reliablity has
Stronger practical application value.
Assuming that the sensor local clock referenced when acquiring signal difference, to generate synchronism deviation.This hair
In another bright embodiment, sensor is divided into multiple groups according to different reference clocks, by M sensor according to reference clock
Different demarcation at N group, the sensor in same group refers to the same local clock, and there are clock jitters between group and group.It puts
Calibration source known to D position is set, the time difference information about each calibration source is successively obtained using sensor；And utilize sensor
The priori observation of position and be directed to each calibration source time difference information obtained, it is successively right by Taylor series alternative manner
Calibration source is positioned, and calibration source positioning result is obtained.It is shown in Figure 2 according to the difference of reference clock, by all sensors
It is divided into N group, the sensor in each group refers to the same local clock, therefore not clock jitter between them, however group
But there is clock jitters between group, and the number of probes that wherein n-th group includes is Mn, then haveIt does not lose
Generality, the S in Fig. 2mM-th of sensor is represented, position vector is denoted as um(wherein 1≤m≤M).Preferably, it is sensed at M
Device sets a master reference, other are aiding sensors.S might as well be enabled1Indicate master reference, other is auxiliary sensing
Device.Packet mode according to figure 1, since master reference is at the 1st group, and each time difference observed quantity is with master reference
For reference, can then enable the 1st group of clock jitter is zero.
In the time difference information for obtaining each calibration source using sensor, in further embodiment of the present invention, according to calibration source
Known location vector sum sensor position vector, obtain each correction source signal arrival one using modulated parameter estimating method
Time difference between aiding sensors and master reference；The M-1 time difference of each correction source signal is passed by signal space
It broadcasts speed and is converted into range difference, and the M-1 range difference is merged into observation vector.By the position vector note of d-th of calibration source
For(wherein 1≤d≤D).Assuming that obtaining d-th of correction source signal using modulated parameter estimating method reaches m-th of sensor
SmWith arrival master reference S1Between time difference be td,m(wherein 2≤m≤M), if enabling the spatial velocity of signal is c,
It can be by time difference td,mIt is converted into range difference rd,m=ctd,m, and can be denoted as
In formula,ρnIt indicates as caused by the clock jitter of n-th group sensor
Range deviation；εd,mIndicate range difference observation error.Further, its covariance matrix is calculated according to observation vector, and passed through
Taylor series alternative manner solves location parameter, obtains the positioning result of calibration source.By m-th of sensor SmPosition
Priori observation be denoted as vm, and observation vector r will be merged into for M-1 range difference of d-th of calibration sourced, that is, have
rd=[rd,2 rd,3 Λ rd,M]T(1≤d≤D)
Then the location parameter of " embedded " Taylor series alternative manner estimation calibration source is utilized.It is provided first about clock
This closed expression, is then updated in original objective function by the closed expression of deviation (being equivalent to range deviation), and
And location parameter is solved using Taylor series alternative manner.This method can effectively inhibit the influence of clock jitter, phase
The iterative formula answered is
E in formulapIndicate the covariance matrix of the observation error about calibration source range difference,Representing matrix EpSquare
Root inverse of a matrix matrix, and the expression formula of remaining variables is respectively as follows:
In formulaThe Moore-Penrose of representing matrix is inverse.
Using calibration source positioning result as the input value of multilayer feedforward neural network, using calibration source known position information as
Multilayer feedforward neural network output valve, using the data of calibration source known to D position by BP algorithm to multilayer feedforward mind
It is trained study through network, the multilayer feedforward neural network after obtaining training.Assuming that being directed to the positioning result of d-th of calibration source
ForAnd its actual position isNow willAs the input value of multilayer feedforward neural network, and incite somebody to actionAs more
The output valve of layer feedforward neural network, is shared input-output pair as D group, is trained using them to neural network, is instructed
Practice algorithm using classical BP algorithm.Preferably, multilayer feedforward neural network is two of neuron there are two input and output contain
Dimensional plane location model, it is shown in Figure 3, or contain for input and output the three dimension location model there are three neuron, ginseng
As shown in Figure 4；After being trained using multilayer feedforward neural network to learning sample, which just has to positioning result
Deviation-correcting function, can be used to correct influences brought by sensor position error.
Obtain position vector about target radiation source by sensor, in conjunction with sensor position vector, it is of the invention again
In one embodiment, the target radiation source is obtained using modulated parameter estimating method and reaches an aiding sensors and master reference
Between time difference.Preferably, for M sensor, the M-1 time difference is converted to M-1 using signal space spread speed
Range difference, and the M-1 range difference is merged into observation vector；Its covariance matrix is calculated according to observation vector, and is utilized embedding
Enter the location parameter of formula Taylor series alternative manner estimation target radiation source.The position vector of target radiation source is denoted as q.Assuming that
Target emanation source signal, which is obtained, using modulated parameter estimating method reaches m-th of sensor SmWith arrival master reference S1Between
Time difference is τm(wherein 2≤m≤M), can be by time difference τ using signal space spread speed cmIt is converted into range difference lm=c τm,
And it can be denoted as
, in formula, φmIndicate range difference observation error.By for target radiation source M-1 range difference be merged into observe to
L is measured, that is, is had
L=[l2 l3 Λ lM]T
Then, the location parameter of " embedded " Taylor series alternative manner estimation target radiation source, corresponding iteration are utilized
Formula is
E in formulaqIndicate the covariance matrix of the observation error about target radiation source range difference,Representing matrix Eq's
The inverse matrix of On Square-Rooting Matrices, and the expression formula of remaining variables is respectively as follows:
Successively target radiation source is positioned by iteration to obtain in target radiation source positioning result comprising by sensing
Deviations caused by device location error, before the target radiation source positioning result that will acquire is input to the multilayer after training
It is tested in feedback neural network, eliminates the deviations as caused by sensor position error, test output is the target
The final positioning result of radiation source.
For verifying effectiveness of the invention, explanation is further explained below by specific experiment data:
The true location coordinate (unit: rice) of 1 sensor of table
As shown in table 1,18 sensors are shared, positioning using TDOA are carried out to target radiation source, according between sensor whether into
Row synchronized sampling, can be classified as 6 groups, and the number of each group of sensor is respectively that 4,3,3,3,3,2 (master reference is first
Group), range deviation caused by the clock jitter of each group of sensor is respectively 0 meter, -20 meters, 25 meters, -30 meters, 35 meters, -40
Rice, since with first group to refer to, first group of range deviation is 0 meter.Assuming that the position coordinates of target radiation source are
(2942 meters, 3238 meters).Below by positioning using TDOA neural network based method for correcting error disclosed in this patent with do not rectify a deviation
The performance of time difference positioning method be compared.
Firstly, Fig. 5 gives the training result of neural network, as can be seen from the figure its training effect is very good.Then,
Range difference observation error standard deviation is fixed as 1 meter, Fig. 6 has given positioning result scatter diagram.It is equal that Fig. 7 gives target position estimation
Square error with range difference observation error standard deviation change curve.From Fig. 5~Fig. 7 as can be seen that disclosed in this patent
Positioning using TDOA method for correcting error neural network based can be eliminated obviously to be influenced brought by sensor position error, while can also
Effectively to inhibit the influence of clock jitter, to be obviously improved clock jitter and under the conditions of sensor position error exists simultaneously
TOA difference locating accuracy.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For device disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
The unit and method and step of each example described in conjunction with the examples disclosed in this document, can with electronic hardware,
The combination of computer software or the two is realized, in order to clearly illustrate the interchangeability of hardware and software, in above description
In generally describe each exemplary composition and step according to function.These functions are held with hardware or software mode
Row, specific application and design constraint depending on technical solution.Those of ordinary skill in the art can be to each specific
Using using different methods to achieve the described function, but this realization be not considered as it is beyond the scope of this invention.
Those of ordinary skill in the art will appreciate that all or part of the steps in the above method can be instructed by program
Related hardware is completed, and described program can store in computer readable storage medium, such as: read-only memory, disk or CD
Deng.Optionally, one or more integrated circuits also can be used to realize, accordingly in all or part of the steps of above-described embodiment
Ground, each module/unit in above-described embodiment can take the form of hardware realization, can also use the shape of software function module
Formula is realized.The present invention is not limited to the combinations of the hardware and software of any particular form.
The foregoing description of the disclosed embodiments makes professional and technical personnel in the field can be realized or use the application.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the application.Therefore, the application
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of positioning using TDOA result method for correcting error neural network based, which is characterized in that include following content:
Sensor is divided into multiple groups according to different reference clocks；The sensor being utilized respectively in the multiple groups successively obtains position
Known calibration source time difference information, priori observation and each calibration source time difference information using sensor position, by repeatedly
In generation, successively positions calibration source, obtains calibration source positioning result；
Study is trained to multilayer feedforward neural network using calibration source positioning result and calibration source known position information, is obtained
Multilayer feedforward neural network after training；
The time difference information about target radiation source is obtained by sensor, and combines the priori observation of sensor position, is passed through
Iteration successively positions target radiation source, obtains target radiation source positioning result；
Target radiation source positioning result is input in the multilayer feedforward neural network after training and is tested, before obtaining the multilayer
Present neural network output and using the output as the final positioning result of target radiation source.
2. positioning using TDOA result method for correcting error neural network based according to claim 1, which is characterized in that by M
Sensor according to reference clock different demarcation at N group, the sensor in same group refers to the same local clock, group and group
Between there are clock jitters；Calibration source known to D position is placed, the time difference about each calibration source is successively obtained using sensor
Information；And using the priori observation of sensor position and it is directed to each calibration source time difference information obtained, pass through Taylor
Series alternative manner successively positions calibration source, obtains calibration source positioning result.
3. positioning using TDOA result method for correcting error neural network based according to claim 1 or 2, which is characterized in that in M
One master reference of a sensor settings, other are aiding sensors.
4. positioning using TDOA result method for correcting error neural network based according to claim 3, which is characterized in that utilize biography
Sensor obtains in the time difference information of each calibration source, according to the known location vector sum sensor position vector of calibration source, utilizes
Modulated parameter estimating method obtains each correction source signal and reaches a time difference between aiding sensors and master reference；It will
The M-1 time difference of each correction source signal is converted into range difference by signal space spread speed, and by the M-1 range difference
It is merged into observation vector.
5. positioning using TDOA result method for correcting error neural network based according to claim 4, which is characterized in that according to sight
Direction finding amount calculates its covariance matrix, and is solved by Taylor series alternative manner to location parameter, obtains calibration source
Positioning result.
6. positioning using TDOA result method for correcting error neural network based according to claim 1, which is characterized in that will correct
Input value of the source positioning result as multilayer feedforward neural network, using calibration source known position information as multilayer feedforward nerve net
Network output valve is trained the multilayer feedforward neural network by BP algorithm using the data of calibration source known to D position
Study, the multilayer feedforward neural network after obtaining training.
7. positioning using TDOA result method for correcting error neural network based according to claim 1 or 6, which is characterized in that more
Layer feedforward neural network is the two-dimensional planar location model of neuron there are two input and output contain, or is contained for input and output
The three dimension location model of three neurons.
8. positioning using TDOA result method for correcting error neural network based according to claim 1, which is characterized in that pass through biography
Sensor obtains the position vector about target radiation source, and combines sensor position vector, is obtained using modulated parameter estimating method
It obtains the target radiation source and reaches a time difference between aiding sensors and master reference.
9. positioning using TDOA result method for correcting error neural network based according to claim 8, which is characterized in that be directed to M
The M-1 time difference is converted to M-1 range difference using signal space spread speed, and the M-1 range difference is closed by a sensor
And at observation vector；Its covariance matrix is calculated according to observation vector, and estimates mesh using embedded Taylor series alternative manner
Mark the location parameter of radiation source.
10. according to claim 1 or positioning using TDOA result method for correcting error neural network based described in 9, which is characterized in that logical
Iteration is crossed successively to position target radiation source to obtain in target radiation source positioning result comprising by sensor position error
Caused deviations, the target radiation source positioning result that will acquire are input to the multilayer feedforward neural network after training
In tested, eliminate the deviations as caused by sensor position error, test output be the target radiation source it is final
Positioning result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810952670.1A CN109239654A (en) | 2018-08-21 | 2018-08-21 | Positioning using TDOA result method for correcting error neural network based |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810952670.1A CN109239654A (en) | 2018-08-21 | 2018-08-21 | Positioning using TDOA result method for correcting error neural network based |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109239654A true CN109239654A (en) | 2019-01-18 |
Family
ID=65069877
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810952670.1A Pending CN109239654A (en) | 2018-08-21 | 2018-08-21 | Positioning using TDOA result method for correcting error neural network based |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109239654A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975749A (en) * | 2019-02-26 | 2019-07-05 | 中国人民解放军战略支援部队信息工程大学 | A kind of shortwave list under calibration source existence condition, which is stood erectly, connects localization method |
| CN109975755A (en) * | 2019-02-26 | 2019-07-05 | 中国人民解放军战略支援部队信息工程大学 | A kind of shortwave multistation direct localization method under calibration source existence condition |
| CN110418278A (en) * | 2019-07-25 | 2019-11-05 | 李印 | A kind of 3 D positioning system based on Evolutionary Neural Network |
| CN110673196A (en) * | 2019-09-20 | 2020-01-10 | 中国人民解放军战略支援部队信息工程大学 | Time difference positioning method based on multidimensional calibration and polynomial root finding |
| CN113281700A (en) * | 2021-04-20 | 2021-08-20 | 宜宾学院 | Wireless electromagnetic positioning system and method |
| CN115508773A (en) * | 2022-10-27 | 2022-12-23 | 中国电子科技集团公司信息科学研究院 | Time difference method multi-station passive positioning method, system, electronic equipment and storage medium |
| CN115598592A (en) * | 2022-10-27 | 2023-01-13 | 中国电子科技集团公司信息科学研究院（Cn） | Time-frequency difference joint positioning method, system, electronic equipment and storage medium |
| CN116567800A (en) * | 2023-06-30 | 2023-08-08 | 湖南时空信安科技有限公司 | Time calibration method, adjustment model training method and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103969622A (en) * | 2014-04-25 | 2014-08-06 | 西安电子科技大学 | Time difference positioning method based on multiple motion receiving stations |
| US20160360362A1 (en) * | 2015-06-05 | 2016-12-08 | Qualcomm Incorporated | Hybrid positioning techniques based on rtt and toa/tdoa |
| CN106488548A (en) * | 2016-10-25 | 2017-03-08 | 北京邮电大学 | A kind of determination method and device of indoor multipath error |
| CN108037520A (en) * | 2017-12-27 | 2018-05-15 | 中国人民解放军战略支援部队信息工程大学 | Direct deviations modification method based on neutral net under the conditions of array amplitude phase error |
-
2018
- 2018-08-21 CN CN201810952670.1A patent/CN109239654A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103969622A (en) * | 2014-04-25 | 2014-08-06 | 西安电子科技大学 | Time difference positioning method based on multiple motion receiving stations |
| US20160360362A1 (en) * | 2015-06-05 | 2016-12-08 | Qualcomm Incorporated | Hybrid positioning techniques based on rtt and toa/tdoa |
| CN106488548A (en) * | 2016-10-25 | 2017-03-08 | 北京邮电大学 | A kind of determination method and device of indoor multipath error |
| CN108037520A (en) * | 2017-12-27 | 2018-05-15 | 中国人民解放军战略支援部队信息工程大学 | Direct deviations modification method based on neutral net under the conditions of array amplitude phase error |
Non-Patent Citations (2)
| Title |
|---|
| 乔明等: "BP神经网络在BD1定位技术中的应用研究", 《电视技术》 * |
| 王鼎等: "同步时钟偏差存在下的时差定位性能分析及改进的定位方法", 《电子学报》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975749B (en) * | 2019-02-26 | 2021-04-20 | 中国人民解放军战略支援部队信息工程大学 | Short-wave single-station direct positioning method under condition of existence of correction source |
| CN109975755A (en) * | 2019-02-26 | 2019-07-05 | 中国人民解放军战略支援部队信息工程大学 | A kind of shortwave multistation direct localization method under calibration source existence condition |
| CN109975749A (en) * | 2019-02-26 | 2019-07-05 | 中国人民解放军战略支援部队信息工程大学 | A kind of shortwave list under calibration source existence condition, which is stood erectly, connects localization method |
| CN109975755B (en) * | 2019-02-26 | 2021-04-20 | 中国人民解放军战略支援部队信息工程大学 | Short-wave multi-station direct positioning method under condition of existence of correction source |
| CN110418278A (en) * | 2019-07-25 | 2019-11-05 | 李印 | A kind of 3 D positioning system based on Evolutionary Neural Network |
| CN110418278B (en) * | 2019-07-25 | 2021-11-09 | 李印 | Three-dimensional positioning system based on evolutionary neural network |
| CN110673196B (en) * | 2019-09-20 | 2021-01-22 | 中国人民解放军战略支援部队信息工程大学 | Time difference positioning method based on multidimensional calibration and polynomial root finding |
| CN110673196A (en) * | 2019-09-20 | 2020-01-10 | 中国人民解放军战略支援部队信息工程大学 | Time difference positioning method based on multidimensional calibration and polynomial root finding |
| CN113281700A (en) * | 2021-04-20 | 2021-08-20 | 宜宾学院 | Wireless electromagnetic positioning system and method |
| CN113281700B (en) * | 2021-04-20 | 2023-10-31 | 宜宾学院 | Wireless electromagnetic positioning system and method |
| CN115508773A (en) * | 2022-10-27 | 2022-12-23 | 中国电子科技集团公司信息科学研究院 | Time difference method multi-station passive positioning method, system, electronic equipment and storage medium |
| CN115598592A (en) * | 2022-10-27 | 2023-01-13 | 中国电子科技集团公司信息科学研究院（Cn） | Time-frequency difference joint positioning method, system, electronic equipment and storage medium |
| CN115508773B (en) * | 2022-10-27 | 2023-09-19 | 中国电子科技集团公司信息科学研究院 | Multi-station passive positioning method and system by time difference method, electronic equipment and storage medium |
| CN115598592B (en) * | 2022-10-27 | 2023-09-19 | 中国电子科技集团公司信息科学研究院 | Time-frequency difference joint positioning method, system, electronic equipment and storage medium |
| CN116567800A (en) * | 2023-06-30 | 2023-08-08 | 湖南时空信安科技有限公司 | Time calibration method, adjustment model training method and electronic equipment |
| CN116567800B (en) * | 2023-06-30 | 2023-09-08 | 湖南时空信安科技有限公司 | Time calibration method, adjustment model training method and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109239654A (en) | Positioning using TDOA result method for correcting error neural network based | |
| CN106940439B (en) | K mean cluster based on wireless acoustic sensor network weights sound localization method | |
| CN110058236A (en) | It is a kind of towards three-dimensional Ground Deformation estimation InSAR and GNSS determine Quan Fangfa | |
| Lo | Population estimation using geographically weighted regression | |
| CN102175209B (en) | Effective sampling method for crop cultivated area measurement under support of historical remote sensing product data | |
| CN109461188B (en) | A kind of two-dimensional x-ray cephalometry image anatomical features point automatic positioning method | |
| CN109002835A (en) | A kind of particle filter data correlation method based on maximum entropy fuzzy clustering | |
| CN104931960A (en) | Trend message and radar target state information whole-track data correlation method | |
| CN105389589A (en) | Random-forest-regression-based rib detection method of chest X-ray film | |
| CN109425348A (en) | A kind of while positioning and the method and apparatus for building figure | |
| CN110221244B (en) | Robust positioning method based on arrival time difference under non-line-of-sight condition | |
| CN109633703B (en) | Beidou navigation passive positioning method for responding to sheltered scene | |
| CN107492120A (en) | Point cloud registration method | |
| CN106291607A (en) | GNSS multipath signal simulation-generation method and system | |
| CN109521444B (en) | Self-adaptive least square fitting estimation algorithm for GPS horizontal velocity field of crustal movement | |
| CN110376290A (en) | Acoustic emission source locating method based on multidimensional Density Estimator | |
| CN110307903A (en) | A kind of method of the contactless temperature dynamic measurement of poultry privileged site | |
| CN109359631A (en) | A kind of Sea Ice Types Classification in Remote Sensing Image method based on convolutional neural networks | |
| CN106168662A (en) | The error registration method of passive sensor based on Maximum-likelihood estimation and device | |
| CN108888918A (en) | One kind is for multiple target movement velocity measuring system and method under pahtfinder hard | |
| CN105184829A (en) | Closely spatial object detection and high-precision centroid location method | |
| CN110045342A (en) | Radar relative systematic error valuation efficiency evaluation method | |
| CN113935402A (en) | Training method and device for time difference positioning model and electronic equipment | |
| CN106815607A (en) | A kind of isogram based on anti-distance weighting interpolation inverse function is as data extraction method | |
| CN103983269B (en) | A kind of asynchronous track association algorithm based on area's reality mixed sequence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190118 |