CN112612044B - Method and system for drift point filtering - Google Patents
Method and system for drift point filtering Download PDFInfo
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- 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
- G01S19/42—Determining position
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Abstract
The invention provides a method for drift point filtering, which comprises the following steps: acquiring continuous track point information in a time range of terminal equipment; the point inspection, namely calculating the speed, the acceleration and the acquisition azimuth angle of the track point information during acquisition and the corresponding threshold value, judging abnormal points in the track point, and acquiring effective points; according to the sequence of the effective point acquisition time, sorting the effective point acquisition coordinates to obtain a distribution sequence, and judging drift points in the effective points according to the numerical relation between the effective points in the distribution sequence and weight points obtained by the extended Kalman filtering module based on the effective points to obtain trusted points; and calculating a horizontal included angle formed between adjacent trusted points, judging drift points in the trusted points, and obtaining authoritative points. The judgment basis of the invention is simple and reliable; the drift points with unobvious drift phenomenon can be filtered, track points such as turning or turning of a vehicle can be prevented from being deleted by mistake, and the filtering method is simple and efficient and can be used for realizing more accurate drift point filtering.
Description
Technical Field
The invention relates to the technical field of GPS positioning monitoring, in particular to a method and a system for drift point filtering.
Background
In the GPS positioning monitoring system widely used at present, GPS terminal equipment is adopted to collect position information in real time and upload the position information to a monitoring center, and the monitoring center processes track data reported by the GPS terminal equipment.
Because GPS satellite signals are influenced by complex factors such as atmospheric ionosphere change, cloud layer shielding, multipath reflection of tall buildings and the like, the phenomenon of pose drift often occurs in GPS positioning, namely, the position information calculated by a GPS receiver has deviation with different degrees from the actual situation. When the deviation exceeds the accuracy error allowable range, the GPS position drift is considered to occur. Some GPS location points drift even a large distance, such as to the outer province, or even to other countries. For example, when the driving distance of a vehicle is counted by the GPS, if drift point filtering is not performed on the GPS, a phenomenon that the distance deviation is large is likely to occur. A typical GPS receiver can correct drift, but cannot correct all drift points, thus requiring a secondary screening of the GPS data uploaded to the monitoring center.
The monitoring center can carry out back-end processing on estimated data reported by the GPS terminal equipment, for example, suspicious invalid points are filtered according to parameters such as satellite signal to noise ratio, precision error factor and the like corresponding to the position points, and then track fitting is carried out on a plurality of position points; in fact, the filtering effect of the drift points in the method is not obvious, only small drift points with large drift distances can be filtered, and slow drift, namely drift points with small drift distances, cannot be solved.
Therefore, a method and system for accurately filtering drift points is needed.
Content of the application
The invention aims to solve the technical problems that the filtering effect of drift points is not obvious in the prior art, only small drift points with large drift distance can be filtered, and the defect of slow drift, namely the defect of small drift distance of the drift points can not be solved.
The embodiment of the invention is realized by the following technical scheme: a method for drift point filtering, comprising the steps of:
step 1: acquiring continuous track point information in a time range of terminal equipment;
step 2: the point inspection, namely calculating the speed, the acceleration and the acquisition azimuth angle of the track point information during acquisition and the corresponding threshold value, judging abnormal points in the track point, and acquiring effective points;
step 3: according to the sequence of the effective point acquisition time, sorting the effective point acquisition coordinates to obtain a distribution sequence, and judging drift points in the effective points according to the numerical relation between the effective points in the distribution sequence and weight points obtained by the extended Kalman filtering module based on the effective points to obtain trusted points;
step 4: and calculating a horizontal included angle formed between adjacent trusted points, judging drift points in the trusted points, and obtaining authoritative points.
According to a preferred embodiment, in the step 2, the specific method for determining the abnormal point in the track points and obtaining the effective point includes the following steps:
s201: judging whether the track points have the same acquisition time or the same acquisition coordinates, and if the track points have the same acquisition time or the same acquisition coordinates, performing deduplication;
s202: judging whether the acceleration during track point acquisition is greater than a maximum acceleration threshold value, and if so, determining the track point as an abnormal point;
s203: judging whether the speed of the track point during acquisition is greater than a maximum speed threshold value, and if so, determining the track point as an abnormal point;
s204: judging whether the acquired azimuth angle variation value between adjacent track points is larger than a maximum azimuth angle variation threshold value, if so, determining the track point as an abnormal point, and marking the rest track points as effective points;
s205: filtering is performed on the outliers in step S202 to step S204.
According to a preferred embodiment, in the step 3, the specific method for determining the drift point in the effective point and obtaining the trusted point includes the following steps:
s301: sequencing the effective point acquisition coordinates according to the sequence of the effective point acquisition time to obtain a distribution sequence;
selecting a first effective point mark in the distribution sequence as an initial point;
s302: inputting the initial point and/or the trusted point into an extended Kalman filtering module to obtain an optimal estimated point of the initial point, and marking the optimal estimated point as a weight point;
s303: calculating the distance between the initial point and the weight point according to the acquired coordinates, if the distance exceeds a distance threshold value, determining the initial point as a drift point, filtering the initial point, and marking the weight point as a trusted point;
if the distance is smaller than the distance threshold, marking the initial point as a trusted point;
s304: acquiring a new initial point based on the next effective point, and if the effective point and the initial point are within 10 meters, filtering the effective point;
and continuing to acquire the next effective point for judgment until the effective point and the initial point are out of the range of 10 meters, marking the effective point as a new initial point, and executing the step S302 to acquire a new trusted point.
According to a preferred embodiment, in the step 4, the specific method for determining the drift point in the trusted point and obtaining the authoritative point includes the following steps:
s401: dividing the track composed of the trusted points into a plurality of segments through the drift points determined in the step S303;
s402: calculating a horizontal included angle formed between adjacent credible points in a certain track section, and judging the size of the horizontal included angle;
s403: if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is smaller than the angle threshold value, the first credible point and the second credible point are taken as authoritative points, and the next credibility of the second credible point is continuously judged;
if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points;
if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is larger than the angle threshold value and the horizontal included angle between the second credible point and the subsequent 5 credible points is larger than the angle threshold value, the credible point in the section of track is determined to be a drift point.
S404: and filtering the drift points, and connecting the authoritative points in sequence according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
According to a preferred embodiment, in the step 2, the maximum acceleration threshold, the maximum speed threshold and the maximum azimuth variation threshold are each twice the maximum value, taking into account the signal disturbance, the speed sensor, the acceleration and the azimuth angle sensor error.
The invention also provides a system for drift point filtering, comprising:
the first screening module is used for performing deduplication on track points with the same acquisition time or the same acquisition coordinates;
the second screening module is connected to the output end of the first screening module and is used for filtering abnormal points with acceleration larger than a maximum acceleration threshold value during acquisition;
the third screening module is connected to the output end of the second screening module and is used for filtering abnormal points with the speed greater than the maximum speed threshold value during collection;
the fourth screening module is connected to the output end of the third screening module and is used for filtering abnormal points, the azimuth angle variation value of which is larger than the maximum azimuth angle variation threshold value, between adjacent track points and marking the rest track points as effective points;
the storage module is connected to the output end of the fourth screening module and is used for sequencing the effective point acquisition coordinates according to the sequence of the effective point acquisition time to obtain a distribution sequence;
the extended Kalman filtering module is connected to the output ends of the first marking module and the second marking module and is used for calculating and obtaining the current state and the coordinates of the terminal equipment according to the initial point and/or the trusted point and predicting the weight point of the terminal equipment;
the first marking module is connected to the first output end of the storage module and is used for marking a first effective point in the selected distribution sequence as an initial point;
the second marking module is connected to the second output end of the storage module and is used for marking the effective points with the effective points and the initial points out of the range of 10 meters as new initial points and taking the new initial points as the input of the extended Kalman filtering module;
the third marking module is connected to the first output end of the extended Kalman filtering module and is used for marking the optimal estimated point obtained by the extended Kalman filtering module as a weight point;
a fourth marking module connected to the second output end of the extended kalman filter module and the output end of the third marking module, marking a weight point corresponding to the initial point as a trusted point based on the initial point determined as a drift point, wherein the initial point with a distance greater than a distance threshold value between the weight points is determined as the drift point;
the fifth marking module is connected to the third output end of the extended Kalman filtering module and is used for marking an initial point, the distance between the initial point and the weight point of which is smaller than a distance threshold value, as a trusted point;
the sixth marking module and the judging module are used for dividing the track formed by the trusted points into a plurality of sections through the drift points determined by the fourth marking module, calculating the horizontal included angle formed between the adjacent trusted points in a certain section of track, judging the size of the horizontal included angle, comparing the size of the horizontal included angle formed between the adjacent trusted points in the track with an angle threshold value, and if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is smaller than the angle threshold value, taking the first trusted point and the second trusted point as authoritative points, and continuing judging the next credibility of the second trusted point;
if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points;
if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is larger than an angle threshold value and the horizontal included angle between the second credible point and the subsequent 5 credible points is larger than the angle threshold value, determining the credible point in the section of track as a drift point;
and filtering the drift points, and connecting the authoritative points in sequence according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
According to a preferred embodiment, the maximum acceleration threshold, the maximum velocity threshold and the maximum azimuth variation threshold each take twice the maximum value.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects: compared with the prior art, the embodiment of the invention judges obvious drift points and repeated track points in the track according to the speed, acceleration, coordinates and azimuth information uploaded by the terminal equipment, acquires effective points, calculates the numerical relation with the effective points through the weight point predicted value acquired by the extended Kalman filtering module, acquires reliable points according to the calculation result, filters out the drift points with obvious drift in the track, and judges the basis to be simple and reliable; furthermore, through calculating the horizontal included angle between the credible points, the drift points with unobvious drift phenomenon can be filtered, and the error deletion of track points such as turning or turning of a vehicle can be avoided, so that the filtering method is simple and efficient, and more accurate drift point filtering is realized.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method for drift-point filtering provided in embodiment 1;
fig. 2 is a schematic diagram of the system for drift-point filtering provided in embodiment 2.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Example 1
Fig. 1 is a flow chart of the steps of a method for drift-point filtering in this implementation. In fig. 1, continuous track point information in a time range of a terminal device is first obtained, then, according to the acquisition time and the acquisition coordinates in the track point information, whether track points with the same acquisition time and acquisition coordinates exist in the track points is judged, further, repeated track points are subjected to de-duplication, and further screening is performed on the remaining track points.
Further, according to the acceleration acquired in the track point information and according to the acceleration information, such as average acceleration, in the track point information in the track, the determined maximum acceleration threshold is used for judging whether the acceleration acquired in the track point is greater than the maximum acceleration threshold, if so, the track point is determined to be an abnormal point, further, filtering is performed on the abnormal point, and further screening is performed on the rest track points.
Further, according to the speed collected in the track point information and according to the speed information in the track point information in a plurality of tracks, such as average speed, determining a maximum speed threshold, judging whether the speed of the track point during collection is greater than the maximum speed threshold, if so, determining the track point as an abnormal point, further filtering the abnormal point, and further screening the rest track points.
Further, according to the azimuth angle acquired in the track point information and according to azimuth angle information in a plurality of track point information in the track, for example, an average azimuth angle variation value is calculated, a maximum azimuth angle variation threshold value is determined, whether the acquired azimuth angle variation value between adjacent track points is larger than the maximum azimuth angle variation threshold value is judged, if so, the track point is determined to be an abnormal point, further, filtering is carried out on the abnormal point, and the rest track points are marked as effective points, so that the effective points are screened out from a plurality of random points, and secondary screening is carried out.
Further, according to the sequence of the effective point acquisition time, the effective point acquisition coordinates are ordered to obtain a distribution sequence, and then the first effective point is selected from the distribution sequence to be marked as an initial point.
Further, the initial point is input into an extended Kalman filtering module to obtain an optimal estimated point of the initial point, and the optimal estimated point is marked as a weight point.
Further, calculating the distance between an initial point and a weight point according to the collected coordinates summarized by the track point information, if the distance exceeds a distance threshold value, determining the initial point as a drift point, filtering the initial point, and marking the weight point as a trusted point; if the distance is less than the distance threshold, the initial point is marked as a trusted point.
Further, based on the next effective point, a new initial point is obtained, and if the effective point and the initial point are within 10 meters, filtering is performed on the effective point; continuously acquiring the next effective point for judgment until the effective point and the initial point are out of the range of 10 meters, marking the effective point as a new initial point, inputting the new initial point and the acquired credible point into an extended Kalman filtering module, predicting the weight point of the acquired information through the extended Kalman filtering module, and calculating the distance between the new initial point and the new weight point; by the method, the effective points are replaced by the trusted points, and the drift points with obvious drift can be filtered.
It will be appreciated that after filtering the drift points with significant drift, there remain a number of drift points with less significant drift in the trajectory, and the method for filtering such drift points is as follows:
firstly, a track formed by trusted points is divided into a plurality of sections through drift points, and drift points can be judged on the whole section by dividing the track into a plurality of sections, so that the overall efficiency is improved.
Further, calculating a horizontal included angle formed between adjacent trusted points in a certain track section, and judging the size of the horizontal included angle; if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is smaller than the angle threshold, the first trusted point and the second trusted point are used as authoritative points, and the next trusted point of the second trusted point is continuously judged.
Further, if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than an angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points; it can be understood that, for example, a vehicle turns or turns, that is, a track similar to drift appears, and by the angle judging method, the track points turning or turning can be avoided from being deleted by mistake to a certain extent.
Further, if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold, and the horizontal included angle between the second trusted point and the subsequent 5 trusted points is larger than the angle threshold, determining that the trusted point in the section of track is a drift point. It will be appreciated that if the drift point filtering is performed by the above method, for example, the GPS acquisition interval is long, then the filtering of the track of the vehicle that continuously turns or turns may also occur; therefore, the above method needs to take into account the GPS acquisition time interval, for example, the GPS acquisition time interval is short, for example, 2S/time, and the above problem does not occur.
Further, filtering is performed on the drift points, and the authoritative points are sequentially connected according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
It will be appreciated that the maximum acceleration threshold, the maximum velocity threshold, and the maximum azimuth variation threshold are each twice the maximum value, taking into account signal disturbances, velocity sensors, acceleration and azimuth sensor errors.
Example 2
Fig. 2 is a schematic structural diagram of a system for drift point filtering according to the present embodiment, where the system mainly includes: the first screening module is used for performing deduplication on track points with the same acquisition time or the same acquisition coordinates; the second screening module is connected to the output end of the first screening module and is used for filtering abnormal points with acceleration larger than a maximum acceleration threshold value during acquisition; the third screening module is connected to the output end of the second screening module and is used for filtering abnormal points with the speed greater than the maximum speed threshold value during collection; the fourth screening module is connected to the output end of the third screening module and is used for filtering abnormal points, the azimuth angle variation value of which is larger than the maximum azimuth angle variation threshold value, between adjacent track points and marking the rest track points as effective points; the storage module is connected to the output end of the fourth screening module and is used for sequencing the effective point acquisition coordinates according to the sequence of the effective point acquisition time to obtain a distribution sequence; the extended Kalman filtering module is connected to the output ends of the first marking module and the second marking module and is used for calculating and obtaining the current state and the coordinates of the terminal equipment according to the initial point and/or the trusted point and predicting the weight point of the terminal equipment; the first marking module is connected to the first output end of the storage module and is used for marking a first effective point in the selected distribution sequence as an initial point; the second marking module is connected to the second output end of the storage module and is used for marking the effective points with the effective points and the initial points out of the range of 10 meters as new initial points and taking the new initial points as the input of the extended Kalman filtering module; the third marking module is connected to the first output end of the extended Kalman filtering module and is used for marking the optimal estimated point obtained by the extended Kalman filtering module as a weight point; a fourth marking module connected to the second output end of the extended kalman filter module and the output end of the third marking module, marking a weight point corresponding to the initial point as a trusted point based on the initial point determined as a drift point, wherein the initial point with a distance greater than a distance threshold value between the weight points is determined as the drift point; the fifth marking module is connected to the third output end of the extended Kalman filtering module and is used for marking an initial point, the distance between the initial point and the weight point of which is smaller than a distance threshold value, as a trusted point; the sixth marking module and the judging module are used for dividing the track formed by the trusted points into a plurality of sections through the drift points determined by the fourth marking module, calculating the horizontal included angle formed between the adjacent trusted points in a certain section of track, judging the size of the horizontal included angle, comparing the size of the horizontal included angle formed between the adjacent trusted points in the track with an angle threshold value, and if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is smaller than the angle threshold value, taking the first trusted point and the second trusted point as authoritative points, and continuing judging the next credibility of the second trusted point; if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points; if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is larger than an angle threshold value and the horizontal included angle between the second credible point and the subsequent 5 credible points is larger than the angle threshold value, determining the credible point in the section of track as a drift point; and filtering the drift points, and connecting the authoritative points in sequence according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
Alternatively, the maximum acceleration threshold, the maximum velocity threshold, and the maximum azimuth variation threshold are each twice the maximum value.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A method for drift point filtering, comprising the steps of:
step 1: acquiring continuous track point information in a time range of terminal equipment;
step 2: the point inspection, namely calculating the speed, the acceleration and the acquisition azimuth angle of the track point information during acquisition and the corresponding threshold value, judging abnormal points in the track point, and acquiring effective points;
step 3: according to the sequence of the effective point acquisition time, sorting the effective point acquisition coordinates to obtain a distribution sequence, and judging drift points in the effective points according to the numerical relation between the effective points in the distribution sequence and weight points obtained by the extended Kalman filtering module based on the effective points to obtain trusted points;
step 4: calculating a horizontal included angle formed between adjacent trusted points, judging drift points in the trusted points, and obtaining authoritative points;
in the step 2, abnormal points in the track points are judged, and the specific method for acquiring the effective points comprises the following steps:
s201: judging whether the track points have the same acquisition time or the same acquisition coordinates, and if the track points have the same acquisition time or the same acquisition coordinates, performing deduplication;
s202: judging whether the acceleration during track point acquisition is greater than a maximum acceleration threshold value, and if so, determining the track point as an abnormal point;
s203: judging whether the speed of the track point during acquisition is greater than a maximum speed threshold value, and if so, determining the track point as an abnormal point;
s204: judging whether the acquired azimuth angle variation value between adjacent track points is larger than a maximum azimuth angle variation threshold value, if so, determining the track point as an abnormal point, and marking the rest track points as effective points;
s205: filtering the abnormal points in the steps S202 to S204;
in the step 3, the drift point in the effective point is judged, and the specific method for acquiring the trusted point comprises the following steps:
s301: sequencing the effective point acquisition coordinates according to the sequence of the effective point acquisition time to obtain a distribution sequence;
selecting a first effective point mark in the distribution sequence as an initial point;
s302: inputting the initial point and/or the trusted point into an extended Kalman filtering module to obtain an optimal estimated point of the initial point, and marking the optimal estimated point as a weight point;
s303: calculating the distance between the initial point and the weight point according to the acquired coordinates, if the distance exceeds a distance threshold value, determining the initial point as a drift point, filtering the initial point, and marking the weight point as a trusted point;
if the distance is smaller than the distance threshold, marking the initial point as a trusted point;
s304: acquiring a new initial point based on the next effective point, and if the effective point and the initial point are within 10 meters, filtering the effective point;
continuously acquiring the next effective point for judgment until the effective point and the initial point are out of the range of 10 meters, marking the effective point as a new initial point, and executing the step S302 to acquire a new trusted point;
in the step 4, the drift point in the trusted point is judged, and the specific method for acquiring the authoritative point comprises the following steps:
s401: dividing the track composed of the trusted points into a plurality of segments through the drift points determined in the step S303;
s402: calculating a horizontal included angle formed between adjacent credible points in a certain track section, and judging the size of the horizontal included angle;
s403: if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is smaller than the angle threshold value, the first credible point and the second credible point are taken as authoritative points, and the next credibility of the second credible point is continuously judged;
if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points;
if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is larger than an angle threshold value and the horizontal included angle between the second credible point and the subsequent 5 credible points is larger than the angle threshold value, determining the credible point in the section of track as a drift point;
s404: and filtering the drift points, and connecting the authoritative points in sequence according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
2. The method for drift-point filtering according to claim 1, wherein in said step 2, the maximum acceleration threshold, the maximum velocity threshold, and the maximum azimuth variation threshold are each twice as large as the maximum value taking into consideration signal interference, the velocity sensor, acceleration, and the azimuth angle sensor errors.
3. A system for drift point filtering, comprising:
the first screening module is used for performing deduplication on track points with the same acquisition time or the same acquisition coordinates;
the second screening module is connected to the output end of the first screening module and is used for filtering abnormal points with acceleration larger than a maximum acceleration threshold value during acquisition;
the third screening module is connected to the output end of the second screening module and is used for filtering abnormal points with the speed greater than the maximum speed threshold value during collection;
the fourth screening module is connected to the output end of the third screening module and is used for filtering abnormal points, the azimuth angle variation value of which is larger than the maximum azimuth angle variation threshold value, between adjacent track points and marking the rest track points as effective points;
the storage module is connected to the output end of the fourth screening module and is used for sequencing the effective point acquisition coordinates according to the sequence of the effective point acquisition time to obtain a distribution sequence;
the extended Kalman filtering module is connected to the output ends of the first marking module and the second marking module and is used for calculating and obtaining the current state and the coordinates of the terminal equipment according to the initial point and/or the trusted point and predicting the weight point of the terminal equipment;
the first marking module is connected to the first output end of the storage module and is used for marking a first effective point in the selected distribution sequence as an initial point;
the second marking module is connected to the second output end of the storage module and is used for marking the effective points with the effective points and the initial points out of the range of 10 meters as new initial points and taking the new initial points as the input of the extended Kalman filtering module;
the third marking module is connected to the first output end of the extended Kalman filtering module and is used for marking the optimal estimated point obtained by the extended Kalman filtering module as a weight point;
a fourth marking module connected to the second output end of the extended kalman filter module and the output end of the third marking module, marking a weight point corresponding to the initial point as a trusted point based on the initial point determined as a drift point, wherein the initial point with a distance greater than a distance threshold value between the weight points is determined as the drift point;
the fifth marking module is connected to the third output end of the extended Kalman filtering module and is used for marking an initial point, the distance between the initial point and the weight point of which is smaller than a distance threshold value, as a trusted point;
the sixth marking module and the judging module are used for dividing the track formed by the trusted points into a plurality of sections through the drift points determined by the fourth marking module, calculating the horizontal included angle formed between the adjacent trusted points in a certain section of track, judging the size of the horizontal included angle, comparing the size of the horizontal included angle formed between the adjacent trusted points in the track with an angle threshold value, and if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is smaller than the angle threshold value, taking the first trusted point and the second trusted point as authoritative points, and continuing judging the next credibility of the second trusted point;
if the horizontal included angle between the first trusted point and the second trusted point in the adjacent trusted points is larger than the angle threshold value and the horizontal included angles between the second trusted point and the subsequent 5 trusted points are smaller than the angle threshold value, determining that the track is effective, and marking the first trusted point, the second trusted point and the subsequent 5 trusted points as authoritative points;
if the horizontal included angle between the first credible point and the second credible point in the adjacent credible points is larger than an angle threshold value and the horizontal included angle between the second credible point and the subsequent 5 credible points is larger than the angle threshold value, determining the credible point in the section of track as a drift point;
and filtering the drift points, and connecting the authoritative points in sequence according to the acquisition time of the authoritative points, namely the track data after the drift points are filtered.
4. The system for drift-point filtering of claim 3, wherein the maximum acceleration threshold, the maximum velocity threshold, and the maximum azimuth variation threshold each take twice as large as a maximum value.
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