WO2013147560A1 - Procédé de correction d'informations de position - Google Patents

Procédé de correction d'informations de position Download PDF

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Publication number
WO2013147560A1
WO2013147560A1 PCT/KR2013/002663 KR2013002663W WO2013147560A1 WO 2013147560 A1 WO2013147560 A1 WO 2013147560A1 KR 2013002663 W KR2013002663 W KR 2013002663W WO 2013147560 A1 WO2013147560 A1 WO 2013147560A1
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WIPO (PCT)
Prior art keywords
speed
time stamp
error
value
tuple
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PCT/KR2013/002663
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English (en)
Korean (ko)
Inventor
송하윤
Original Assignee
Song Ha Yoon
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Priority claimed from KR1020120103572A external-priority patent/KR101467317B1/ko
Priority claimed from KR1020130010032A external-priority patent/KR101447582B1/ko
Application filed by Song Ha Yoon filed Critical Song Ha Yoon
Priority to US14/389,475 priority Critical patent/US20150087333A1/en
Publication of WO2013147560A1 publication Critical patent/WO2013147560A1/fr

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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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/52—Determining velocity
    • 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

Definitions

  • the present invention relates to a method for determining whether there is an error in position data collected by a user device and a technique for correcting a position value in which an error exists.
  • a method for solving this problem is to provide a method for real-time implementation so that it can be performed in a user device with limited computing ability.
  • a method according to an aspect of the present invention for determining whether there is an error in position data measured by a user device is a statistical technique using mean, expectation, and standard deviation calculated for the measured speed. Can be used. Also, a technique using the measured value of acceleration can be used.
  • the method according to an aspect of the present invention for correcting a position value determined to be an error may use a statistical technique based on a calculated difference with respect to the measured position value.
  • 'measurement' may mean directly acquiring information about a position, a speed, and an acceleration from a user device that does not use the position data filtering method and the position data correction method according to the present invention.
  • the measured values of the speed and acceleration of the user can be obtained from the movement position data having the format of ⁇ latitude, longitude, time> measured from the user equipment.
  • the moving position data having a format of ⁇ latitude, longitude, time> may be referred to as a 'tuple' or 'location data tuple'.
  • the present invention using the data selected by the moving window (or sliding window), it is possible to calculate the statistics of position and velocity and to update the calculated values repeatedly.
  • the measured position data may be filtered using the controllable parameter. Since the algorithm according to the embodiment of the present invention is simple, the present invention can be applied to a mobile user equipment having a small computing power.
  • the size of the moving window may be optimized to improve performance.
  • a backtracking interpolation method can be used to replace the speed value and / or position value determined to be an error with an appropriate estimate.
  • the mean value and standard deviation of the velocity at a certain time interval are known, for example, a significant interval following a normal distribution can be set and this interval can be set.
  • the passing speed can be determined as an error value.
  • the location information processing method processes information on a tuple including latitude, longitude, and time stamp collected from the location information collecting device.
  • the current speed at the time stamp [i] (V i) is, the time stamp [i -1] in the case of an exchange included in the window that includes greater than a statistical value calculated from the speed, the time stamp [i
  • a second step of replacing the information about the tuple [ i ] with an estimated value when it is determined that the error exists.
  • the second step may be performed at a time stamp immediately after the time stamp on which the first step is performed.
  • the statistical value may be a mobile confidence interval obtained from the past speeds.
  • the mobile confidence interval may be a value obtained by adding an average value MA speed of the past speeds to a first value s ⁇ MSD speed that is proportional to the standard deviation of the past speeds.
  • this processing method includes a speed [ i + 1] at timestamp [i + 1] including timestamp [ i ⁇ n + 1] to timestamp [ i ] before calculating the estimated value. Determining that an error exists in the speed [ i + 1] if it is greater than a second statistical value calculated from past speeds included in the window; And if it is determined that the velocity error in [i +1] is present, can be prior to the step of the estimated the speed comprising [i +1] replace previously in the second statistics more.
  • the second statistical value may be MA speed + s 99.5 ⁇ MSD speed .
  • s 99.5 means a confidence interval of 99.5%, but another parameter indicating a different range of confidence intervals, for example, s 99.9 which means a confidence interval of 99.9% may be used.
  • the processing method may further include determining whether an error has occurred for the tuple [ i -1] in the time stamp [i-1]; If the acceleration error for the tuple [i -1] is not generated speed error for the tuple [i -1] occurs, by calculating the statistical values described above, measured in the time stamp [i -1] Determining whether the determined speed value is smaller than the aforementioned statistical value; And is smaller than the actual speed value above which the statistics case, the step of replacing the speed, latitude, and longitude for the time stamp [i -1] to the values measured in the time stamp [i -1] It may further include. In this case, n is the size of the window.
  • the present invention it is possible to filter out the location data in error in real time, and provide a technique for replacing in real time using the corrected new location data instead of discarding the location data determined to be in error.
  • FIG. 2 is a view for explaining a speed calibration method of interpolating a speed value by a least square method according to another embodiment of the present invention.
  • 3 to 5 illustrate a position tuple filtering method, a speed estimation method using interpolation, and a speed error correction method according to an embodiment of the present invention.
  • FIG. 7 illustrates a speed error detected by applying a speed correction according to an interpolation technique according to an embodiment of the present invention.
  • Fig. 8 shows the speed measured using a commercially available position detection device.
  • 9 and 10 are graphs derived by applying the concept of a moving intention interval, acceleration error detection, and correction speed according to the present invention.
  • the movement trajectory (trace) of the user may be represented using a set of tuples (ie, a moving data tuple) measured and collected in the form of ⁇ latitude, longitude, time>.
  • a data set representing user mobility can be obtained.
  • P t One tuple obtained for the time t is herein referred to as P t, and can be referred to as t, respectively lat, lon t latitude and longitude provided by the P t.
  • Table 1 shows the typical maximum speeds that can occur depending on the mode of transport.
  • the maximum speed MAX speed for applying to an embodiment of the present invention may be defined as 250 m / s.
  • MAX acceleration can be expressed by defining the maximum value of acceleration considered to be possible in everyday environment. If the measured speed or acceleration is greater than MAX speed or MAX acceleration , it can be determined that an error has occurred.
  • the moving average of speed can be calculated and the moving standard deviation of speed can be calculated.
  • the moving average value of the speed and the moving standard deviation value can be expressed as MA speed (n) and MSD speed (n), respectively.
  • N represents the number of past data of ⁇ P x : t-n + 1 ⁇ x ⁇ t ⁇ , which is a set of tuples.
  • the past n data can be obtained by applying a window of size 'n'.
  • V t and a t calculated from the tuple P t are acceptable values in the everyday environment. If the calculated V t is outside the range of normal distribution according to the moving average value MA speed (n) and the moving standard deviation of speed MSD speed (n), the tuple P t is filtered out from a series of traces. (Ie, removed). The condition for removing P t may be given by Equation 1 below.
  • V t MA speed (n) + s * MSD speed (n)
  • 's' represents the sensitivity of filtering and is a user controllable parameter.
  • V t does not satisfy Equation 1, it is determined that the new tuple P t has a valid value, and the new values can be used to update the values of MA speed (n) and MSD speed (n). This calculation is relatively simple and can be performed in real time on a low power device such as a mobile user equipment.
  • the filtering characteristic may vary depending on the size n of the window described above. Therefore, in order to perform an experiment to determine the influence of the window size, it is possible to collect the value measured by a commercial mobile user equipment.
  • the user device may store and output location data whenever it detects a change in location. Alternatively, if the user device is in the stopped state, the location data may be stored and output every user defined interval, for example, every 3 to 60 seconds. The location data thus obtained may be displayed on a map using various techniques.
  • the location data obtained in three different ways at the same time that is, a location using a cellular base station, a location using a WiFi device for a crowd source, and a location using a GPS were simultaneously provided.
  • the values of the three types of location data provided at the same time indicate different values.
  • experience has shown that position data with the smallest velocity value is likely to be valid data. Therefore, in the first step of filtering the position data, it is possible to select a method of selecting the position data that has moved the least from the previous position among the plurality of position data provided by different methods at the same time.
  • the algorithm can successfully cope with successive errors in the absence of movement, but it can be expected that it will not respond to situations where the speed changes rapidly.
  • the window is small, it will show a quick response to sudden speed changes to the moving state, which is believed to contain consecutive error tuples. However, if there are m consecutive errors, m> n, i.e. if the window size is small, the errors cannot be filtered out.
  • n 5, 10, 25, 50, and 100 were set. For each case, the moving average of the speed and the moving standard deviation of the speed were calculated.
  • the window size is small, especially at the beginning of the speed change, it reacts quickly and sensitively to the speed change. However, they tend to overfilter out the right data.
  • the window size is as small as 5 or 10 10, there are cases where two or more tuples that appear to be correct velocity data are removed.
  • the position data was collected for several hours while the position detection devices were fixed in the outdoor and the indoor.
  • the first position detection device is a commercial Garmin GPSMAP62s, used purely for GPS data collection.
  • the second location detector is a commercial Samsung Galaxy Tab, used to obtain location data from a 3G base station (3GBS) connected to the device. It was assumed that more error would be shown in the position data obtained using 3GBS, and both position data obtained from GPS and 3GBS would indicate an error. In particular, it was assumed that the position detection error would be shown indoors.
  • Table 2 shows the errors that occur when measuring location data. The unit of measurement is meters.
  • the error distance is calculated from the position data, and the variance and average of the error distance can be calculated.
  • 3GBS showed a larger error rate, larger error size, larger maximum error size, and larger standard deviation of error size.
  • indoor GPS data can be thought of as not meaningful data. GPS data in the open air has a sufficient level of accuracy to obtain sophisticated location data, and the maximum error size is within a reasonable range of 52 meters.
  • [Algorithm 1] of Table 3 is for determining whether there is an error in the measured position data and filtering the position data having an error. Upon acquiring new position data P i + 1 , this [Algorithm 1] can determine whether to filter P i + 1 .
  • Acceleration and velocity can be used as parameters for filtering and can be used in different ways, but they can all be considered as throttling parameters. Tuples with out-of-range or over-acceleration are filtered out of the so-called 'moving significant interval' values, where velocity V i + 1 is expressed as MA speed + s ⁇ MSD speed . s represents the significant level of the normal distribution.
  • Line 12 in Algorithm 1 99.5% confidence interval of the normal distribution to reflect the speed of this tuple in the moving window, even if a tuple needs to be filtered out by Equation 1: It may be limited or calibrated to be within, to include the speed of this tuple. This is intended to update the mobile window with fast speed values to cope with rapid changes in speed. That is, even if there is an error-free tuple, a sudden speed change may be filtered out. In such a case, even if the tuple is filtered, the moving window may reflect the speed change of the maximum allowable for this tuple.
  • n and s can be set by the user.
  • n is the number of tuples in the window, that is, the size of the window
  • s is the sensitivity level of the filtering (ie, the significance level of the normal distribution).
  • the size of the window may be made smaller.
  • the incorrect speed value is calibrated, the abnormal acceleration value is limited, and the speed value can be replaced by the average speed calculated by the moving window.
  • the smaller window size limits the trailing effect, but results in a more flexible response to speed changes.
  • Algorithm 1 can be modified in consideration of the following.
  • the size of the window instead of defining the size of the window as the number of tuples in one window, it can be defined by the time interval included in the window. Considering that speed is a function of time, it can be seen that this modified approach will be effective and accurate when collecting location data regularly.
  • the window size may be dynamically increased or decreased according to the number of consecutive errors. Once we find that the number of consecutive errors is large, we can increase the window size to minimize the effect of the continuous errors on the moving average and the moving standard deviation. If the number of consecutive errors is small, the window size can be reduced to respond appropriately to rapid speed changes and to reduce the amount of computation for filtering.
  • [Algorithm 1] can be transformed into a pseudo real time algorithm rather than a real time algorithm.
  • window size n we're going to be in the middle of the window instead of filtering the (n + 1) th tuple
  • the first tuple can be filtered. This method can reduce the tendency of underfiltering and overfiltering even though it cannot be implemented in full real time.
  • Algorithm 2 adds the steps for interpolation to Algorithm 1 (see lines 18 to 21 of Algorithm 2).
  • Algorithm 2 adds the steps for interpolation to Algorithm 1 (see lines 18 to 21 of Algorithm 2).
  • the speed of this last tuple can be replaced with the interpolated value.
  • the last tuple marked as filtered out in the window can be interpolated.
  • Another variation for the construction of moving windows for more precise approximation is that the approximate midpoint of the window The first tuple is interpolated using n tuples in the window.
  • an asymptotic curve estimated from n tuples can be used to interpolate the middle tuple in the window to enable more precise interpolation.
  • this may result in computational overhead and may be difficult to apply to mobile user equipment.
  • the horizontal axis of FIG. 1 represents an index (ie, a timestamp) of tuples obtained continuously, and the vertical axis is a speed according to each tuple stored in a memory.
  • the size n of the moving window is illustrated as 10.
  • the MA speed and MA speed + s 99.5 ⁇ MSD speed can be calculated from the 10 speeds present in the moving window, an example of which is shown in FIG.
  • acceleration a i + 1 at index [i + 1] is greater than MAX acceleration . Therefore, the speed V i + 1 at the index [i + 1] was corrected to MA speed (row 15 of [Algorithm 2]).
  • the newly acquired tuple is first calibrated by lines 10 to 17 of Algorithm 2. Then, when another new tuple is acquired, the calibrated rate is [algorithm]. Can be calibrated secondaryly by lines 18 to 21 of 2]. It can be appreciated that the primary and secondary corrections described above for the speed of any tuple may occur, only one of them, or none at all.
  • Speed V i is the interpolation with the calibration value of the aforementioned tuple P i in [Algorithm 2], but little is usually less than the value (MA speed + s 99.5 ⁇ MSD speed) corrected by the second line 11, in the next time stamp It may be larger depending on the interval of movement or the corrected value of.
  • Algorithm 2 the newly acquired mobile data tuple P i + 1 and the latest mobile data tuple P i present in the mobile window are processed.
  • it can be modified to perform processing for the moving data tuple P m for any time stamp in the moving window.
  • rows 18 to 21 of Algorithm 2 may be modified as shown in Table 6.
  • FIG. 2 is a view for explaining a speed correction method of interpolating a speed value by a least square method according to another embodiment of the present invention.
  • FIG. 2 shows the results of using the modified interpolation method from the interpolation method according to the 18th to 21st lines of [Algorithm 2].
  • a nonlinear interpolation method using a plurality of data in a moving window is used.
  • a moving data tuple having an index [i-4] may be corrected using a plurality of data included in the moving window and a value corresponding to an asymptote approximated using a least square method.
  • 3 to 5 illustrate a position tuple filtering method, a speed estimation method using interpolation, and a speed error correction method according to another embodiment of the present invention.
  • 3 to 5 represent the index of the time stamp and the vertical axis represents the speed.
  • Reference numeral 100 denotes the measured velocity in the corresponding timestamp
  • reference numeral 101 denotes the measured velocity obtained from the tuple filtered out using one of the algorithms disclosed herein.
  • 3 to 5 show an example in which the window size is 10.
  • Reference numeral 103 denotes an average value of the speed in the window
  • reference numeral 102 denotes a value 102 of the moving significant value described above.
  • the position tuple of reference numeral 101 shown in Fig. 3 is filtered out because its speed is larger than the value 102 of the movement significance section.
  • Reference numeral 101 (index i) of FIG. 4 represents an actual velocity obtained from the filtered position tuple according to the algorithm of the present invention.
  • Reference numeral 200 in FIG. 4 denotes an estimated value of the speed provided by the speed value of index i being linearly interpolated by the speed values of index i-1 and index i + 1 before and after it.
  • the tuples of the index i and the index i + 1 are both filtered out.
  • the measured speed of the index i (see reference numeral 101 of the index i) is larger than the value of the movement intention interval, it has already been calibrated to a predetermined size (see reference numeral 300 of the index i).
  • the measured speed of index i + 1 (see reference numeral 101 of index i + 1) is also calibrated to a predetermined size because it is larger than the value of the movement intention interval (see reference numeral 300 of index i + 1).
  • the speed of index i is interpolated using the speeds of index i + 1 and index i-1.
  • a calibration of speed may be used as a pre-step for accurate estimation of speed.
  • the parameter ET D for introducing a normal distance error is introduced (line 4).
  • ET D can be set to 15 m for a GPS system with SA enabled.
  • ET D may determine the minimum speed, minimum difference between latitude and longitude, and affect the moving standard deviation, resulting in a non-zero moving significance interval. That is, by using ET D , the concept of CEP of the positioning system can be introduced in Algorithm 3 according to one embodiment of the present invention, which overfilters tuples because of small possible speed error within the accuracy of the positioning system. Can be avoided.
  • Algorithm 3 similar to the velocity estimation process of [Algorithm 2], a position estimation process can be introduced for the filtered tuple.
  • the main difference in the location estimation process included in Algorithm 3 lies in the direction of latitude and longitude. In other words, only a positive value needs to be considered for the velocity value, but a negative difference between latitude and longitude cannot be ignored to estimate the position.
  • [Algorithm 3] shows the entire process for position estimation, which corresponds to the entire process for velocity estimation of [Algorithm 2].
  • Vlat and Vlon in [Algorithm 3] refer to the difference between latitude and longitude, respectively. Algorithm 3 calculates the moving average of Vlat and Vlon. If one tuple is filtered by excessive speed, this indicates that the difference in position is excessive. Once this is done, we need to estimate the correct position. When an acceleration error occurs, the position data is corrected to a moving average value of the difference value (rows 25 and 26), similarly to that performed for the speed. Finally, as with the velocity estimation, interpolation is performed for latitude and longitude at the end of the travel window (lines 31 and 32). The direction of the difference is also preserved (lines 25, 26, 31, and 32).
  • FIG. 6 illustrates speed errors obtained from measured data
  • FIG. 7 illustrates speed errors detected by applying speed correction according to an interpolation technique according to an exemplary embodiment of the present invention.
  • Each figure contains information about the actual speed, the acceleration, the section of motion, the calibrated speed, the estimated speed, and the detected acceleration error. Acceleration errors are shown in inverted triangle shapes. 6 does not include information on the interpolated speed.
  • An empty rectangle represents a limited speed according to a calibration mechanism according to an embodiment of the present invention, and a black rectangle represents an estimated speed by an interpolation technique.
  • the velocity on the y-axis is in m / s
  • the x-axis represents time of day.
  • the algorithm according to an embodiment of the present invention estimated the possible speed value well.
  • the speed error was limited to an appropriate range by the calibration method of the present invention, and then the speed was corrected by the speed estimation method. .
  • the method of estimating the speed by the interpolation method can further reduce the influence on the moving window. Therefore, in FIG. 7, the moving significance interval following the interpolation shows a difference, since the statistical value of the moving window varies according to the interpolated speed value. The difference at 13:33:04 is quite large.
  • Tables 9 to 11 are for explaining the fourth algorithm for position estimation according to another embodiment of the present invention.
  • [Algorithm 4] is the same as [Algorithm 3] except lines 2, 5, 6, 17-25, 28-32, and 38 of [Algorithm 4]. Hereinafter, the lines 5, 6, 17-25, 28-32, 38 of [Algorithm 4] are demonstrated.
  • V can be changed to another value by another part of [Algorithm 4].
  • V may be replaced by another calibrated or estimated value.
  • Line 6 of Algorithm 4 sets the MIN speed for the minimum speed to the smaller of ET D / ( t i +1 - t i ) the predetermined minimum value MINSPEED . That is, it determines the minimum value of the moving standard deviation.
  • Lines 17 and 18 of [Algorithm 4] Different parts of [Algorithm 4] can change lat and lon to different values. For example, in lines 41, 42, 47, and 48 of Algorithm 4, lat and lon can be replaced with other calibrated or estimated values. However, in lines 21 and 22 of Algorithm 4, we can replace this replaced value with the original value. To do this, we store the original values of lat and lon separately in lines 17 and 18.
  • Line 19 of [Algorithm 4] represents a condition for performing backtracking shown in lines 20 to 22 of [Algorithm 4].
  • Lines 20 to 24 of [Algorithm 4] Replace the V, lon, and lat values with the original values when the condition of Line 19 of [Algorithm 4] is satisfied. have. That is, it is determined that the original V, lon and lat values are more accurate than the V, lon and lat values that have been corrected or estimated by other parts of [Algorithm 4], and return to the measured data (backtracking). If backtracking occurs, initialize NCE to 0 on line 23 and reduce window size n on line 24. However, the window size n cannot be smaller than the minimum value IWS .
  • Lines 28 and 29 of [Algorithm 4] Increase NCE on line 28 if the tuple P i +1 is filtered. That is, to increase the number of consecutive errors.
  • line 29 we increase the window size n.
  • the window size n cannot be larger than the maximum value MWS .
  • Figure 8 shows the speed measured using a commercial position detection device iPhone and Garmin. In this experiment, the speed changes with a continuous value over time, but the actual data of iPhone and Garmin show that the sudden increase and decrease of the speed sometimes occurs, which is an error.
  • FIG. 9 and 10 are graphs derived by applying the concepts of a moving significant interval, acceleration error detected, and calibrated speed described in the above-described embodiment of the present invention. 9 shows that the window size is set to 5, and FIG. 10 shows the result of setting the window size to 100. FIG. 9 and 10 use 287 measured position data acquired on February 27, 2012.
  • FIG. 11 shows an example of data processed according to [Algorithm 4].
  • This graph was created using nine position data measured on August 28, 2012, and it detects filtered original speed, moving significant interval, acceleration, and acceleration error. (acceleration error detected), interpolated speed, calibrated speed, and revived speed by backtracking.
  • the filtered original velocity is not only out of the range of movement mean but acceleration error is detected, thus maintaining the interpolated velocity.
  • time 08:49:56 and time 08:49:59 since the original speed is in the range of movement significance and acceleration error was not detected, it was confirmed that the playback was replaced by the interpolated speed by [Algorithm 4]. Can be.
  • the following steps may be performed.
  • step S11 it may be determined whether there is an error in the speed [i + 1] (V i + 1 ) at the time stamp [i + 1] (see line 34).
  • step S12 if it is determined in step S11 that there is an error in speed [i + 1] ( V i +1 ⁇ MA speed + s 99.5 ⁇ MSD speed ), the speed [i + 1] is corrected in advance. Calibrate or restrict (see line 35). At this time, if the speed [i + 1] is less than or equal to the predetermined minimum speed (MIN speed ), it is determined that there is no error in the speed [i + 1] (see line 35). In this case, the minimum speed may be determined by an error tolerance of distance (ET D ) or a predetermined minimum value MINSPEED (see line 6).
  • step S13 it is determined whether there is an error in the acceleration [i + 1] (a i + 1 ) at the time stamp [i + 1] (see line 37). In this case, when the acceleration [i + 1] is greater than or equal to the preset maximum acceleration (MAX acceleration ), it may be determined that there is an error in the acceleration [i + 1] (see line 37).
  • MAX acceleration preset maximum acceleration
  • step S14 when it is determined in step S13 that there is an error in acceleration [i + 1], latitude [i + 1] and longitude [i + 1] are corrected in advance (lines 41 and 42). Reference).
  • the hardness [i + 1] is corrected using the average hardness difference (MA Vlongitude ) up to the time stamp [i] and the hardness [i] (lon i ) in the time stamp [i], and the time stamp [i] Latitude [i + 1] can be corrected using the mean latitude difference (MA Vlatitude ) and latitude [i] (lat i ) in time stamp [i] (see lines 41 and 42).
  • the average hardness difference (MA Vlongitude ) set in the 16th row of Algorithm 4 means the average value of the hardness difference in each timestamp set in the 14th row according to the time
  • the average latitude difference set in the 13th row Vlatitude means the average of the latitude difference in each timestamp set in row 11 over time.
  • step S21 it is checked whether there is an error in the tuple [ i ] ( P i ) at the time stamp [ i ] ( t i ) (see line 44).
  • an error means that the tuple [ i ] ( P i ) is filtered by the speed error as described above.
  • step (S22) if it is determined that the error is present, the time stamp [i +1] (t i +1) latitude in [i +1] (lat i +1) and the time stamp [i ] the interpolation of one or more previously acquired latitude estimation (estimation) latitude [i] (lat i) in the time stamp [i], and the hardness at the time stamp [i +1] [i +1 ] ( lon i +1 ) and one or more longitudes obtained before the timestamp [ i ] are interpolated to estimate the longitude [ i ] ( lon i ) at the timestamp [ i ] (see lines 47 and 48). ).
  • 'acquired' may mean that the algorithm is processed by [Algorithm 4] and stored in the memory.
  • the location calibration method according to the embodiment may further include step S23.
  • step S23 the speed [i + 1] (V i + 1 ) at timestamp [i + 1] and one or more speeds obtained before timestamp [i] are interpolated to generate the timestamp [i].
  • the velocity [i] (V i ) at can be estimated (see line 45).
  • step S31 if the error in time stamp [ i -1] is caused by an error due to speed and not due to an error due to acceleration, time stamp [ in +1] to time stamp [ i].
  • Statistical values using the mean of the stored speeds ( MA speed ) and the standard deviation ( MSD speed ) can be calculated to determine whether the measured speed value in the timestamp [ i -1] is less than the statistical value. (See line 19).
  • 'stored speed' refers to a value stored in the memory processed by [Algorithm 4].
  • step S32 when it is determined in step S31 that the measured speed value is smaller than the statistical value, the speed, latitude, and longitude for the time stamp [ i ] may be replaced by the original value measured. (See lines 20-22). In this case, n is the size of the window.
  • step S33 when it is determined in step S31 that the measured speed value is smaller than the statistical value, the n value can be decreased (see line 24).
  • step S34 if the time stamp [ i ] is found to have the above error, increase the size of the window (see line 29), and if it is confirmed that there is no error at the timestamp [ i ], The size can be reduced (see line 32).
  • an error may include both an error in speed and an error in acceleration.
  • a user device includes: a location information collecting unit configured to collect information about a tuple including latitude, longitude, and a timestamp; And a processing unit configured to process information about the tuple.
  • the processing unit may perform lines 19, 20 to 22, 24, 29, 32, 34, 35, 37, 41, 42, 44, 45, 47, and 48 of [Algorithm 4].
  • Algorithm 4 is described based on the time point of the time stamp [i + 1] at which the new tuple is obtained. Algorithm 4 may be described as the time stamp [i + 2]. In this case, for example, in the above description of the step S31, the index of the time stamp is increased by +1.
  • Each of the above steps may be executed in a user device including a location information collecting unit configured to collect information about a tuple including latitude, longitude, and a timestamp, and a processing unit configured to process information about the tuple. have.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Navigation (AREA)

Abstract

L'invention concerne un procédé d'estimation d'une valeur vraie de positions contenant une erreur collectées à partir d'un dispositif de collecte d'informations de position, la valeur vraie étant estimée par interpolation de valeurs de position collectées avant et après les positions contenant une erreur, et si les valeurs de position ont été collectées juste après que les positions contenant une erreur aient une erreur, une estimation est réalisée après que les valeurs de position soient collectées juste après que les positions contenant une erreur soient étalonnées.
PCT/KR2013/002663 2012-03-30 2013-03-29 Procédé de correction d'informations de position WO2013147560A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US14/389,475 US20150087333A1 (en) 2012-03-30 2013-03-29 Method for correcting position information

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
KR10-2012-0033533 2012-03-30
KR20120033533 2012-03-30
KR1020120103572A KR101467317B1 (ko) 2012-03-30 2012-09-18 위치 데이터의 오류여부 결정방법
KR10-2012-0103572 2012-09-18
KR1020130010032A KR101447582B1 (ko) 2013-01-29 2013-01-29 위치정보 보정방법
KR10-2013-0010032 2013-01-29

Publications (1)

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WO2013147560A1 true WO2013147560A1 (fr) 2013-10-03

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WO (1) WO2013147560A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100535394B1 (ko) * 2003-08-21 2005-12-08 현대자동차주식회사 Ｇｐｓ 신호 처리방법
JP4119256B2 (ja) * 2001-03-05 2008-07-16 クゥアルコム・インコーポレイテッド 初期粗位置推定値に基づいて改良された位置推定値を供給する方法および装置
KR20090089343A (ko) * 2006-11-07 2009-08-21 스카이후크 와이어리스, 인크. Ｗｌａｎ 기반의 위치 확인 시스템 내에서 위치 확인 에러를 추정하기 위한 시스템 및 방법
JP4348441B2 (ja) * 2007-01-22 2009-10-21 国立大学法人 大阪教育大学 位置検出装置、位置検出方法、データ判定装置、データ判定方法、コンピュータプログラム及び記憶媒体
KR20120079624A (ko) * 2011-01-05 2012-07-13 에스케이플래닛 주식회사 차량 항법 시스템, 이를 위한 위치 측정 방법 및 경로 재탐색 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4119256B2 (ja) * 2001-03-05 2008-07-16 クゥアルコム・インコーポレイテッド 初期粗位置推定値に基づいて改良された位置推定値を供給する方法および装置
KR100535394B1 (ko) * 2003-08-21 2005-12-08 현대자동차주식회사 Ｇｐｓ 신호 처리방법
KR20090089343A (ko) * 2006-11-07 2009-08-21 스카이후크 와이어리스, 인크. Ｗｌａｎ 기반의 위치 확인 시스템 내에서 위치 확인 에러를 추정하기 위한 시스템 및 방법
JP4348441B2 (ja) * 2007-01-22 2009-10-21 国立大学法人 大阪教育大学 位置検出装置、位置検出方法、データ判定装置、データ判定方法、コンピュータプログラム及び記憶媒体
KR20120079624A (ko) * 2011-01-05 2012-07-13 에스케이플래닛 주식회사 차량 항법 시스템, 이를 위한 위치 측정 방법 및 경로 재탐색 방법

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