CN114501300A - Distributed positioning algorithm based on space environment error model - Google Patents
Distributed positioning algorithm based on space environment error model Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention provides a distributed positioning algorithm based on a space environment error model. The algorithm is as follows: firstly, acquiring spatial information of a positioning environment, and acquiring a prior information graph containing line-of-sight base station distribution and a non-line-of-sight error compensation function in a positioning area by combining a known positioning environment space according to an error model of a UWB signal in a spatial structure; selecting and compensating an UWB base station in a positioning process according to a spatial prior information graph; judging whether the base station is shielded by the body of the pedestrian or not by using the course information of the PDR in the positioning process, and correcting the ranging value if the base station is shielded by the body of the pedestrian; and finally, positioning is realized by combining the compensated multi-base station information with the PDR through a distributed error state algorithm. The invention fully utilizes prior information such as a spatial structure and a UWB non-line-of-sight error model to optimize the base station to realize accurate positioning, and has the advantages of high precision and strong robustness.
Description
The technical field is as follows:
the invention relates to an indoor positioning method, in particular to a distributed positioning algorithm based on a space environment error model.
Technical background:
the potential commercial value of indoor location services is rising year by year compared to outdoors. At present, stations, airports, chemical plants, hospitals, nursing homes, prisons/guard houses/drug-relief houses, construction sites and the like are introduced into indoor positioning in a large scale, and the increasing speed is very fast. Specifically, in the industry, the report of the development prospect analysis of indoor positioning in 2018 indicates that the total direct market amount of indoor positioning in China has broken through 3000 billion yuan. Currently, there are infrared, bluetooth, inertial navigation, wireless fidelity (Wi-Fi), Ultra Wide Band (UWB) technologies, and the like. UWB has emerged due to its advantages of high positioning accuracy, low power consumption, certain signal penetration, and the like, and has become an increasingly interesting technical field.
Any radio technology is inevitably flawed, and UWB technology is no exception. Ultra-wideband can reach centimetre level positioning accuracy under ideal indoor environment, but its signal transmission is easily disturbed by non-line of sight. Because the transmission distance of the UWB signal in the room is limited, and the signal of the UWB signal generates a large error when the UWB signal bypasses an obstacle, the UWB signal cannot be used in an NLOS scene or an indoor scene having a blind area. The general idea to solve this problem is to combine inertial navigation technology with UWB technology. The accuracy of the position fix can be improved to a certain extent, but the range error in the case of non-line-of-sight UWB is not solved essentially, especially because the presence of non-line-of-sight measurements will enlarge the position fix error. Therefore, the invention makes full use of the environment information and the UWB space propagation error model, and provides a distributed positioning algorithm based on the space environment error model, thereby fundamentally solving the non-line-of-sight error of the UWB caused by the space environment characteristics.
The invention content is as follows:
the invention provides a distributed positioning algorithm based on a space environment error model. The algorithm is as follows: firstly, acquiring spatial information of a positioning environment, arranging a UWB positioning base station in the positioning space, and acquiring a prior information graph containing line-of-sight base station distribution and a non-line-of-sight error compensation function in a positioning area by combining a known positioning environment space according to an error model of a UWB signal in a spatial structure; selecting UWB base stations in the positioning process according to the spatial prior information graph, only selecting line-of-sight base stations when the number of line-of-sight base stations is more than 3, and performing error compensation on non-line-of-sight base stations according to an error function when the number of line-of-sight base stations is less than 3; and in the positioning process, the shielding error of the pedestrian body is compensated by combining the course information of the PDR, and finally, the positioning is realized by combining the compensated multi-base-station information with the PDR. The invention fully utilizes prior information such as a spatial structure and a UWB non-line-of-sight error model to optimize the base station to realize accurate positioning, and has the advantages of high precision and strong robustness.
The technical solution for realizing the invention is as follows: a distributed positioning algorithm based on a space environment error model comprises the following steps:
s1, acquiring environmental space information of a positioning area, and laying a positioning base station;
s2, establishing a positioning environment UWB station-building line-of-sight or non-line-of-sight information distribution map;
s3, establishing a positioning environment prior information graph by using a non-line-of-sight error compensation function and combining the distribution situation of the base stations in the S2;
s4, selecting the UWB base station in the positioning process;
s5, carrying out error compensation on the base station under the influence of the human body by using a human body error compensation function;
s6, establishing a dynamic distributed error filtering algorithm;
s7, taking the current positioning result obtained in S6 as the positioning starting point of the next moment and feeding back the positioning starting point to the PDR for information correction;
further, the establishing of the positioning environment prior information map in step 3 is specifically as follows:
firstly, scanning and positioning a regional space environment through laser radar equipment, and establishing a regional space model according to laser point cloud; carrying out UWB base station deployment according to PDOP optimization criterion and space environment; dividing the ground into square small squares with the side length of 0.5 meter in a space model, traversing each small square in the area, adding base station number information with visible distance into each small square, and obtaining a base station visible distance environment map; fitting a distance and angle related signal propagation error function by a ranging experiment aiming at a UWB base station signal propagation shielding barrier in a space environment; calculating an error value in a non-line-of-sight environment of the base station at the central point of each small square grid, storing the corresponding number and the error value of the base station into the base station line-of-sight environment graph established in the last step, and finally establishing an environment prior information graph; each small square in the information graph comprises a line-of-sight base station number and a non-line-of-sight base station number, and a ranging error compensation value under the condition that the position point is not in line-of-sight aiming at the non-line-of-sight base station.
Further, the selection strategy for the UWB positioning base station in step 4 is specifically as follows:
the UWB positioning algorithm needs to acquire the position data of at least three base stations, and the real-time position of the dynamic tag is acquired through a triangulation method.
On the basis of the positioning position at the previous moment, the position of the UWB mobile station at the current moment is pre-judged by using a filtering state prediction equation and the course information of the PDR, the positioning base stations are selected by combining the UWB station building sight distance or non sight distance information distribution diagram established by S2, and when the visual base stations at the estimation point are more than or equal to three, only the sight distance positioning base stations are selected for positioning; when the estimated point visible distance base stations are less than three, according to the step S3, a total of three base stations including the visible distance base station and the non-visible distance base station with a relatively weak non-visible distance condition are selected as the positioning base stations.
Further, a method for judging the strength of the non-line-of-sight condition of the non-line-of-sight base station specifically comprises the following steps: and judging according to the error function fitted by the non-line-of-sight base station in the S3, and sequencing according to the magnitude of the error values.
Further, in step 5, the error compensation is performed on the base station under the influence of the human body by using the human body error compensation function, specifically as follows:
and calculating the direction angle information of the selected base station and the human body in the S4 according to the position point at the current moment and the space position information of the base station by using the advancing course information acquired in the PDR positioning, judging whether the selected base station is influenced by the human body shielding, and performing error compensation on the influenced base station by adopting a human body influence error function.
Further, the establishing of the dynamic distributed error filtering algorithm in step 6 specifically includes the following steps:
the measurement information is obtained by using the difference between RTT (round trip time) ranging information of UWB (ultra-wideband) and PDR (packet data radio) positioning; using UWB measured value as PDR positioning constraint to update PDR information;
the state equation is:
wherein the content of the first and second substances,
is the state vector of the carrier at time k,
the position quantities of the PDR in both directions of the water surface at time k,
is the amount of velocity corresponding to the horizontal position of the PDR at time k,
the course angle of PDR at the moment k; a (k) is the acceleration of PDR at time k; f is a state matrix;
process noise that is a state;
the measurement equation is as follows:
wherein Z (k) is measurement information,. DELTA.d1，kSelecting the number of UWB base stations for a distributed measurement equation, wherein n is the positioning point, H is a measurement matrix, and v (k) is measurement noise;
wherein the content of the first and second substances,
the distance between the UWB tag and the ith UWB reference station calculated by the PDR at the k moment;
position coordinates in the east-west direction at time k are located for the PDR,
positioning position coordinates of the PDR in the north-south direction at the moment k;
for the UWB ith fixed base station position coordinates in the east-west direction,
position coordinates in the north-south direction of the ith UWB fixed base station under the condition of line of sight
And
for direct range values, in the case of non-line-of-sight
And
the distance value after the error function correction is combined;
distance between UWB mobile station and ith fixed base station;
and solving the modified measurement equation and the state equation according to the steps of the EKF to obtain the optimal estimation value of the state quantity, and further calculating to obtain the optimal estimation value of the UWB mobile base station.
Compared with the prior art, the invention has the following remarkable advantages:
(1) the method comprises the steps of fully utilizing environment prior information, obtaining a prior information graph containing line-of-sight base station distribution and a non-line-of-sight error compensation function in a positioning area by combining a known positioning environment space according to an error model of a UWB signal in a space structure;
(2) the UWB positioning base station is optimized according to the environmental characteristics, the line-of-sight base station is used for positioning, and when the line-of-sight base station is not enough to complete positioning, the non-line-of-sight base station is subjected to ranging compensation and then positioning;
(3) and combining the compensated multi-base station information with the PDR to realize positioning through a distributed error state algorithm, and acquiring high-precision positioning information.
Description of the drawings:
FIG. 1 is a schematic diagram of the algorithm
FIG. 2 UWB station-building line-of-sight or non-line-of-sight information distribution diagram
FIG. 3 is a diagram of UWB station building local line-of-sight information
FIG. 4 is a diagram of UWB station building local non-line-of-sight information and error compensation
FIG. 5 PDR pedestrian dead reckoning map
FIG. 6 distributed Algorithm Block diagram
The specific implementation mode is as follows:
the following description will explain embodiments of the present invention in further detail with reference to the accompanying drawings. The general algorithm principle of the invention is shown in fig. 1, and a distributed positioning algorithm based on a space environment error model comprises the following steps: s1, acquiring environmental space information of a positioning area, and laying a positioning base station; s2, establishing a positioning environment UWB station-building line-of-sight or non-line-of-sight information distribution map; s3, establishing a positioning environment prior information graph by using a non-line-of-sight error compensation function and combining the distribution situation of the base stations in the S2; s4, selecting the UWB base station in the positioning process; s5, carrying out error compensation on the base station under the influence of the human body by using a human body error compensation function; s6, establishing a dynamic distributed error filtering algorithm to obtain a final positioning result; and S7, taking the current positioning result obtained in S6 as the positioning starting point of the next time and feeding back the positioning starting point to the PDR for information correction.
The first embodiment is as follows: establishing a sight distance and non-sight distance information graph of a positioning base station specifically as follows:
firstly, scanning and positioning a regional space environment through laser radar equipment, and establishing a regional space model according to laser point cloud; carrying out UWB base station deployment according to PDOP optimization criterion and space environment; as shown in fig. 2, which is a simple schematic diagram, base stations are 0 and 1, the ground is divided into square small squares with a side length of 0.5 m in a spatial model, each small square in an area is traversed, visible-distance base station number information is added to the small squares, and a base station visible-distance environment diagram is obtained, as shown in fig. 3, the numbers of the visible-distance base stations are contained in the squares; fitting a distance and angle related signal propagation error function by a ranging experiment aiming at a UWB base station signal propagation shielding barrier in a space environment; calculating an error value in a non-line-of-sight environment of the base station at the central point of each small square grid, storing a corresponding number and the error value of the base station into a base station line-of-sight environment graph established in the previous step, and finally establishing an environment prior information graph, wherein the number of the non-line-of-sight base station and a ranging compensation value under an error model are contained in the square grid as shown in fig. 4; each small square in the information graph comprises a line-of-sight base station number and a non-line-of-sight base station number, and a ranging error compensation value under the condition that the position point is not in line-of-sight aiming at the non-line-of-sight base station.
The second embodiment: the UWB positioning model is concretely as follows:
the UWB positioning system mainly comprises more than or equal to three fixed base stations and a mobile station, and the distance between the base station and the tag is obtained by utilizing wireless communication two-way ranging, and the formula is as follows:
d＝c*TTOF (1)
in the formula: d represents the distance of the tag from the base station,
TTOT represents the time taken from the sending of data by the positioning tag to the reception of the corresponding reply data transmitted by the base station, TTATRepresenting the time between receipt of data by the positioning base station and transmission of a response data packet, TTOFRepresenting the time consumed by the data in the propagation process.
After the distance between the base station and the tag is obtained through TOF two-way ranging, the position of the tag is located by using a trilateration method, taking three base stations as an example, assuming A1, A2 and A3 as three locating base stations, taking the base stations as the circle centers, taking the measured distance between the tag and the base stations as the radius to make a circle, and the coordinate positions of the base stations are (x) respectively1，y1)，(x2，y2)，(x3，y3) Ideally, the three circles made in the ideal case intersect at a point O, the intersection point O is the location label position, and assuming that the coordinates of the intersection point O are (x, y), the label position calculation formula is as follows:
and solving by adopting a least square optimization method, and performing optimization solving by minimizing the error function to obtain the label coordinate. The error function has various forms, and one of the expression methods is:
assuming that the coordinates to be solved are (x ', y'), this can be obtained by minimizing the objective function E (x, y):
(x ', y') -argminE (x, y) (6) embodiment three: the pedestrian dead reckoning PDR model specifically comprises the following steps:
the PDR estimates the step length of the pedestrian and the direction of each step by using the raw data of the inertial sensor, thereby achieving the purpose of positioning and tracking the pedestrian. The principle of the PDR algorithm is shown in FIG. 5, and the track recurrence formula is shown in the formula
X, Y are x-axis and y-axis coordinates, SL is the step length, and theta is the heading angle, i.e. the included angle between the moving direction and the x-axis.
The PDR algorithm generally comprises 3 parts: gait detection, step length estimation and heading calculation.
(1) Gait detection
The method comprises the steps of judging by adopting acceleration data in an inertia device, setting an acceleration wave crest threshold value, detecting an acceleration value at the moment k for the first time, comparing the acceleration value with adjacent k +1 and k-1 to judge whether the acceleration value is a peak value, comparing the acceleration value at the current moment judged as the peak value with the set acceleration threshold value, counting steps when the acceleration value is greater than the threshold value, and judging as noise disturbance when the acceleration value is less than the threshold value.
(2) Step size estimation
For the step-size estimation, a non-linear step-size estimation model is used, i.e.
In the formula: a is amaxAnd aminC is a calibration constant, wherein c is the maximum value and the minimum value in the acceleration data in the kth step time range in the gait detection result.
(3) Course calculation
Course calculation is mainly completed by a magnetometer and a gyroscope, initial course is determined through output of the magnetometer, and direction change amount is determined by the gyroscope. The earth is a dipolar magnet with a fixed north-pointing magnetic component, and the orientation of the inertial positioning node can be determined by measuring the projection of this component on three axes using a magnetometer. Integrating the angular velocity values allows the calculation of the angle change.
The fourth embodiment is as follows: establishing a distributed filtering algorithm state and a measurement equation specifically comprises the following steps:
a block diagram of a distributed filtering algorithm is shown in fig. 6, in which the measurement information is obtained by using the difference between RTT ranging information of UWB and PDR positioning; using UWB measured value as PDR positioning constraint to update PDR information;
the state equation is:
wherein the content of the first and second substances,
is the state vector of the carrier at time k,
the position quantities of the PDR in both directions of the water surface at time k,
is the amount of velocity corresponding to the horizontal position of the PDR at time k,
the course angle of PDR at the moment k; a (k) is the acceleration of PDR at time k; f is a state matrix;
process noise that is a state;
the measurement equation is:
wherein Z (k) is measurement information,. DELTA.d1，kFor the distributed measurement equation, n is the number of UWB base stations selected for the anchor point, H is the measurement matrix, u (k) is the measurement noise;
wherein the content of the first and second substances,
the distance between the UWB tag and the ith UWB reference station calculated by the PDR at the k moment;
position coordinates in the east-west direction at time k are located for the PDR,
positioning position coordinates of the PDR in the north-south direction at the moment k;
for the UWB ith fixed base station position coordinates in the east-west direction,
position coordinates in the north-south direction of the ith UWB fixed base station under the condition of line of sight
And
for direct range values, in the case of non-line-of-sight
And
the distance value after the error function correction is combined;
the distance between the UWB mobile station and the ith fixed base station;
and solving the modified measurement equation and the state equation according to the steps of the EKF to obtain the optimal estimation value of the state quantity, and further calculating to obtain the optimal estimation value of the UWB mobile base station.
The invention provides a distributed positioning algorithm based on a space environment error model. The algorithm is as follows: firstly, acquiring spatial information of a positioning environment, and acquiring a prior information graph containing line-of-sight base station distribution and a non-line-of-sight error compensation function in a positioning area by combining a known positioning environment space according to an error model of a UWB signal in a spatial structure; selecting and compensating errors of the UWB base station in the positioning process according to the spatial prior information map; judging whether the base station is shielded by the body of the pedestrian or not by using the course information of the PDR in the positioning process, and correcting the ranging value if the base station is shielded by the body of the pedestrian; and finally, positioning is realized by combining the compensated multi-base station information with the PDR through a distributed error state algorithm. The invention fully utilizes the prior information such as a space structure, a UWB non-line-of-sight error model and the like to optimize the base station to realize accurate positioning, and compared with the prior art, the invention has the remarkable advantages that: (1) the method comprises the steps of fully utilizing environment prior information, obtaining a prior information graph containing line-of-sight base station distribution and a non-line-of-sight error compensation function in a positioning area by combining a known positioning environment space according to an error model of a UWB signal in a space structure; (2) the UWB positioning base station is optimized according to the environmental characteristics, the line-of-sight base station is used for positioning, and when the line-of-sight base station is not enough to complete positioning, the non-line-of-sight base station is subjected to ranging compensation and then positioning; (3) and combining the compensated multi-base station information with the PDR to realize positioning through a distributed error state algorithm, and acquiring high-precision positioning information.
Claims (6)
1. A distributed positioning algorithm based on a space environment error model is characterized by comprising the following steps:
s1, acquiring environmental space information of a positioning area, and laying a positioning base station;
s2, establishing a UWB station-building sight distance or non-sight distance information distribution map in a positioning environment;
s3, establishing a positioning environment prior information graph by using a non-line-of-sight error compensation function and combining the distribution situation of the base stations in the S2;
s4, selecting the UWB base station in the positioning process;
s5, carrying out error compensation on the base station under the influence of the human body by using a human body error compensation function;
s6, establishing a dynamic distributed error filtering algorithm;
and S7, taking the current positioning result obtained in S6 as the positioning starting point of the next time and feeding back the positioning starting point to the PDR for information correction.
2. The distributed positioning algorithm based on the spatial environment error model according to claim 1, wherein the step 3 of establishing the positioning environment prior information map specifically includes the following steps:
firstly, scanning and positioning a regional space environment through laser radar equipment, and establishing a regional space model according to laser point cloud; carrying out UWB base station deployment according to PDOP optimization criterion and space environment; dividing the ground into square small squares with the side length of 0.5 meter in a space model, traversing each small square in the area, adding base station number information with visible distance into each small square, and obtaining a base station visible distance environment map; fitting a distance and angle related signal propagation error function by a ranging experiment aiming at a UWB base station signal propagation shielding barrier in a space environment; calculating an error value in a non-line-of-sight environment of the base station at the central point of each small square grid, storing the corresponding number and the error value of the base station into the base station line-of-sight environment graph established in the last step, and finally establishing an environment prior information graph; each small square in the information graph comprises a line-of-sight base station number and a non-line-of-sight base station number, and a ranging error compensation value under the condition that the position point is not in line-of-sight aiming at the non-line-of-sight base station.
3. The distributed positioning algorithm based on the spatial environment error model according to claim 1, wherein the selection strategy for the UWB positioning base station in step 4 is as follows:
the UWB positioning algorithm needs to acquire the position data of at least three base stations, and the real-time position of the dynamic tag is acquired through a triangulation method.
On the basis of the positioning position at the previous moment, the position of the UWB mobile station at the current moment is pre-judged by using a filtering state prediction equation and the course information of the PDR, the positioning base stations are selected by combining the UWB station building sight distance or non-sight distance information distribution diagram established by S2, and when the visual base stations at the estimation point are more than or equal to three, only the sight distance positioning base stations are selected for positioning; when the estimated point visible distance base stations are less than three, according to the step S3, a total of three base stations including the visible distance base station and the non-visible distance base station with a relatively weak non-visible distance condition are selected as the positioning base stations.
4. According to the selection strategy for the UWB positioning base station, the method for judging the strength of the non-line-of-sight condition of the non-line-of-sight base station is characterized in that the method comprises the following specific steps: and judging according to the error function fitted by the non-line-of-sight base station in the S3, and sequencing according to the magnitude of the error values.
5. The distributed positioning algorithm based on the spatial environment error model according to claim 1, wherein the error compensation is performed on the base station under the influence of the human body by using the human body error compensation function in step 5, specifically as follows:
and calculating the direction angle information of the selected base station and the human body in the S4 according to the position point at the current moment and the space position information of the base station by using the advancing course information acquired in the PDR positioning, judging whether the selected base station is influenced by the human body shielding, and performing error compensation on the influenced base station by adopting a human body influence error function.
6. The distributed positioning algorithm based on the spatial environment error model according to claim 1, wherein the dynamic distributed error filtering algorithm established in step 6 specifically includes the following steps:
the measurement information is obtained by using the difference between RTT (round trip time) ranging information of UWB (ultra-wideband) and PDR (packet data radio) positioning; using UWB measured value as PDR positioning constraint to update PDR information;
the state equation is:
wherein the content of the first and second substances,
is the state vector of the carrier at time k,
the position quantities of the PDR in both directions of the water surface at time k,
is the amount of velocity corresponding to the horizontal position of the PDR at time k,
the course angle of PDR at the moment k; a (k) is the acceleration of PDR at time k; f is a state matrix;
process noise that is a state;
the measurement equation is as follows:
wherein Z (k) is measurement information,. DELTA.d1,kSelecting the number of UWB base stations for a distributed measurement equation, wherein n is a positioning point, H is a measurement matrix, and upsilon (k) is measurement noise;
wherein the content of the first and second substances,
the distance between the UWB tag and the ith UWB reference station calculated by the PDR at the k moment;
position coordinates in the east-west direction at time k are located for the PDR,
positioning position coordinates of the PDR in the north-south direction at the moment k;
for the UWB ith fixed base station position coordinates in the east-west direction,
for the ith fixed base station of UWBPosition coordinates in north-south direction, in line of sight
And
for direct range values, in the case of non-line-of-sight
And
the distance value after the error function correction is combined;
distance between UWB mobile station and ith fixed base station;
and solving the modified measurement equation and the state equation according to the steps of the EKF to obtain the optimal estimation value of the state quantity, and further calculating to obtain the optimal estimation value of the UWB mobile base station.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116299167A (en) * | 2022-07-07 | 2023-06-23 | 广东师大维智信息科技有限公司 | Elongated space positioning method, computer readable storage medium and computer device |
| CN116582818A (en) * | 2023-07-06 | 2023-08-11 | 中国科学院空天信息创新研究院 | Non-line-of-sight effect compensation indoor positioning method based on UWB ranging |
| CN116939815A (en) * | 2023-09-15 | 2023-10-24 | 常熟理工学院 | UWB positioning base station selection method based on laser point cloud map |
| CN117545070A (en) * | 2024-01-09 | 2024-02-09 | 宁波市阿拉图数字科技有限公司 | UWB high-precision positioning method suitable for indoor shielding environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116299167A (en) * | 2022-07-07 | 2023-06-23 | 广东师大维智信息科技有限公司 | Elongated space positioning method, computer readable storage medium and computer device |
| CN116582818A (en) * | 2023-07-06 | 2023-08-11 | 中国科学院空天信息创新研究院 | Non-line-of-sight effect compensation indoor positioning method based on UWB ranging |
| CN116939815A (en) * | 2023-09-15 | 2023-10-24 | 常熟理工学院 | UWB positioning base station selection method based on laser point cloud map |
| CN116939815B (en) * | 2023-09-15 | 2023-12-05 | 常熟理工学院 | UWB positioning base station selection method based on laser point cloud map |
| CN117545070A (en) * | 2024-01-09 | 2024-02-09 | 宁波市阿拉图数字科技有限公司 | UWB high-precision positioning method suitable for indoor shielding environment |
| CN117545070B (en) * | 2024-01-09 | 2024-04-02 | 宁波市阿拉图数字科技有限公司 | UWB high-precision positioning method suitable for indoor shielding environment |
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