CN109490914B - Object positioning method, server and system - Google Patents
Object positioning method, server and system Download PDFInfo
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- CN109490914B CN109490914B CN201811459520.3A CN201811459520A CN109490914B CN 109490914 B CN109490914 B CN 109490914B CN 201811459520 A CN201811459520 A CN 201811459520A CN 109490914 B CN109490914 B CN 109490914B
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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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/14—Receivers specially adapted for specific applications
Abstract
The invention discloses an object positioning method, a server and a system. The method comprises the following steps: acquiring the geographical positions of the target object reported continuously and repeatedly; determining the distance between the geographical positions reported twice in the adjacent modes as the drift distance of the former geographical position or the latter geographical position; determining the cumulative distribution probability of the drift distance of the geographic position according to the obtained cumulative distribution function of the drift distance of the geographic unit to which the geographic position belongs and the drift distance of the geographic position; screening the geographic positions according to the cumulative distribution probability of the drift distance of the geographic positions; and determining the position of the target object according to the screened geographic position.
Description
Technical Field
The present invention relates to the field of positioning technologies, and in particular, to an object positioning method, a server, and a system.
Background
In a satellite positioning system, when an obstructing object exists around an object to be positioned, a GPS signal received by the object to be positioned also comprises a signal reflected once or for many times by the surrounding object besides a direct wave. Because the paths and lengths of the direct wave and the reflected wave are different, the final synthesized signal waveform is distorted, and thus, a multipath effect error is generated. When the blocking object produces a specular reflection of the GPS signal, significant positioning errors are caused.
In the shared bicycle positioning which is an application scene of satellite positioning, a user is used to park a shared bicycle around a building and in a cell, and the error range caused by multipath effect is from 10 meters to thousands of meters. In a parking state of a vehicle, errors caused by multipath effects may cause inconvenience to a user or a maintenance person to find a position of the vehicle. The probability distribution of the offset distance caused by the multipath effect on different geographic spaces is often greatly different, for example, the probability of large-distance offset occurring in open places such as parks and playgrounds is often smaller than that occurring in building sheltered areas such as office buildings and residential quarters.
In the positioning of an object such as the positioning of a shared bicycle, how to eliminate the influence of multipath effect errors to obtain a more accurate positioning result is a problem to be solved.
Disclosure of Invention
It is an object of the present invention to provide a new solution for object positioning.
According to a first aspect of the present invention, there is provided an object positioning method comprising:
acquiring the geographical positions of the target object reported continuously and repeatedly;
determining the distance between the geographical positions reported twice in the adjacent modes as the drift distance of the geographical position at the previous time or the geographical position at the next time;
determining the cumulative distribution probability of the drift distance of the geographic position according to the obtained cumulative distribution function of the drift distance of the geographic unit to which the geographic position belongs and the drift distance of the geographic position;
screening the geographic position according to the cumulative distribution probability of the drift distance of the geographic position;
and determining the position of the target object according to the screened geographic position.
Optionally, the screening the geographic location according to the cumulative distribution probability of the drift distance of the geographic location includes:
screening the geographical position corresponding to the drift distance with the cumulative distribution probability less than or equal to a preset threshold value.
Optionally, wherein the determining the position of the target object according to the screened geographic position includes:
taking the geographical position of the last time in the screened geographical positions as the position of the target object; alternatively, the first and second electrodes may be,
and determining the central positions of the screened geographic positions, and taking the central positions as the positions of the target objects.
Optionally, before obtaining the drift distance cumulative distribution function of the geographic unit to which the geographic location belongs, the method further includes a step of determining the drift distance cumulative distribution function of the geographic unit in advance:
acquiring sample geographical positions of sample objects reported continuously and repeatedly;
determining the distance between the sample geographic positions reported twice in the adjacent time as the drift distance of the previous sample geographic position or the next sample geographic position;
determining a cumulative distribution function of drift distances for the geographic cells based on drift distances for sample geographic locations located within the geographic cells.
Optionally, wherein the determining a cumulative distribution function of drift distances of the geographic cell according to drift distances of sample geographic locations located within the geographic cell comprises:
calculating a cumulative distribution probability of drift distances of the sample geographic positions located within the geographic cell according to the drift distances of the sample geographic positions located within the geographic cell;
and estimating parameters of the drift distance cumulative distribution function of the geographic unit by a least square method according to the cumulative distribution probability of the drift distance of the geographic position of the sample positioned in the geographic unit.
Optionally, wherein the drift distance cumulative distribution function employs a weibull cumulative distribution function; the estimating parameters of the drift distance cumulative distribution function of the geographic cell includes estimating shape parameters and scale parameters of the drift distance cumulative distribution function of the geographic cell.
Optionally, wherein the geographic cells are regular hexagonal regions, square regions, or regular triangular regions.
Optionally, wherein the side length of the regular hexagonal region is 3 meters.
According to a second aspect of the present invention, there is provided a server, comprising:
a memory for storing executable instructions;
a processor, configured to execute the server to perform the object positioning method according to any one of the aspects provided in the first aspect.
According to a third aspect of the present invention, there is provided an object positioning system comprising:
an object;
and a server as provided in the second aspect.
According to one embodiment of the disclosure, the technical scheme provided by the invention has the following advantages:
(1) the satellite positioning data is screened by combining the spatial characteristics of the specific geographic unit where the target object is located, the multipath effects of different geographic units are subjected to personalized processing, the positioning error caused by the multipath effect is effectively reduced, and the positioning precision of the target object is improved.
(2) The position of the target object is calibrated based on the cumulative distribution function of the drift distance of the geographic unit, extra hardware cost is not needed, the operation is simple and easy, and the coverage rate is high.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 shows a block diagram of an example of a hardware configuration of a vehicle system that may be used to implement an embodiment of the invention;
FIG. 2 illustrates a flow chart of an object locating method that may be used to implement an embodiment of the present invention;
FIG. 3 illustrates a flow diagram of a method of determining a drift distance cumulative distribution function that may be used to implement an embodiment of the present invention;
FIG. 4 illustrates a flow diagram of a method of parameter estimation of a drift distance cumulative distribution function that may be used to implement an embodiment of the present invention;
FIG. 5 shows a block diagram of a server of an embodiment of the invention;
FIG. 6 shows a block diagram of a vehicle localization system of an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
As shown in fig. 1, the vehicle system 100 includes a server 1000, a client 2000, a vehicle 3000, and a network 4000.
The server 1000 provides a service point for processes, databases, and communications facilities. The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.
In one example, the server 1000 may be as shown in fig. 1, including a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600. Although the server may also include speakers, microphones, etc., these components are not relevant to the present invention and are omitted here.
The processor 1100 may be, for example, a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, an infrared interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display, an LED display touch panel, or the like. The input device 1600 may include, for example, a touch screen, a keyboard, and the like.
In the present embodiment, the client 2000 is an electronic device having a communication function and a service processing function. The client 2000 may be a mobile terminal, such as a mobile phone, a laptop, a tablet, a palmtop, etc. In one example, the client 2000 is a device that performs management operations on the vehicle 3000, such as a mobile phone installed with an Application (APP) that supports operation and management of the vehicle.
As shown in fig. 1, the client 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, an output device 2700, a camera device 2800, and the like. The processor 2100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 2200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. Communication device 2400 is capable of wired or wireless communication, for example. The display device 2500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, or a microphone. The output device 2700 is for outputting information, and may be, for example, a speaker for outputting voice information to a user. The image pickup device 2800 is used for image pickup of acquisition information, and is, for example, a camera or the like.
The vehicle 3000 is any vehicle that can give the right to share the use by different users in time or separately, for example, a shared bicycle, a shared moped, a shared electric vehicle, a shared vehicle, and the like. The vehicle 3000 may be a bicycle, a tricycle, an electric scooter, a motorcycle, a four-wheeled passenger vehicle, or the like.
As shown in fig. 1, vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, an output device 3500, an input device 3600, a positioning device 3700, sensors 3800, and so forth. The processor 3100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 3200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface 3300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3400 can perform wired or wireless communication, for example. The output device 3500 may be, for example, a device that outputs a signal, may be a display device such as a liquid crystal display panel or a touch panel, or may be a speaker or the like that outputs voice information or the like. The input device 3600 may include, for example, a touch panel, a keyboard, or the like, and may input voice information through a microphone. The positioning device 3700 is used to provide positioning function, and may be, for example, a GPS positioning module, a beidou positioning module, etc. The sensor 3800 is used for acquiring vehicle attitude information, and may be, for example, an accelerometer, a gyroscope, or a three-axis, six-axis, nine-axis micro-electro-mechanical system (MEMS), or the like.
The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. In the vehicle system shown in fig. 1, a vehicle 3000 and a server 1000, and a client 2000 and the server 1000 can communicate with each other through a network 4000. The vehicle 3000 may be the same as the server 1000, and the network 4000 through which the client 2000 communicates with the server 1000 may be different from each other.
It should be understood that although fig. 1 shows only one server 1000, client 2000, vehicle 3000, it is not meant to limit the corresponding number, and multiple servers 1000, clients 2000, vehicles 3000 may be included in the vehicle system 100.
Taking the vehicle 3000 as an example of a shared bicycle, the vehicle system 100 is a shared bicycle system. The server 1000 is used to provide all the functionality necessary to support shared bicycle use. The client 2000 may be a mobile phone on which a shared bicycle application is installed, which may help a user to obtain a corresponding function using the vehicle 3000, and the like.
The vehicle system 100 shown in FIG. 1 is illustrative only and is not intended to limit the invention, its application, or uses in any way.
In an embodiment of the present invention, the memory 1200 of the server 1000 is used for storing instructions for controlling the processor 1100 to operate so as to execute the vehicle positioning method provided by the embodiment of the present invention.
Although a number of devices are shown in fig. 1 for server 1000, the present invention may relate to only some of the devices, for example, server 1000 may relate to only memory 1200 and processor 1100.
In an embodiment of the present invention, the memory 2200 of the client 2000 is configured to store instructions for controlling the processor 2100 to operate the client 2000 to execute the vehicle positioning method according to the embodiment of the present invention.
Although a number of devices are shown in fig. 1 for client 2000, the present invention may relate to only some of the devices, for example, client 2000 may relate to only memory 2200 and processor 2100.
In an embodiment of the present invention, the memory 3200 of the vehicle 3000 is configured to store instructions for controlling the processor 3100 to operate so as to perform the vehicle localization method provided by the embodiment of the present invention.
Although a plurality of devices are shown for the vehicle 3000 in fig. 1, the present invention may relate only to some of the devices, for example, the vehicle 3000 relates only to the memory 3200 and the processor 3100.
In the above description, the skilled person will be able to design instructions in accordance with the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
< example >
The general concept of this embodiment is to provide an object positioning scheme, which screens reported geographic locations according to a distribution rule of drift distances of geographic units in which target objects are located, and performs personalized processing on multipath errors in different geographic units, thereby effectively reducing the multipath errors and improving the accuracy of object positioning.
The object positioning scheme provided by the embodiment is particularly suitable for slow moving objects. A slowly moving object may for example be an object moving at a speed of less than 1m/s, possibly a stationary object.
< method >
In the present embodiment, an object positioning method is provided. In one specific application scenario, the target object to be located is a vehicle 3000 shown in fig. 1. It should be understood that the vehicle is a transportation device that is released for the user to obtain the usage right in a time-sharing lease, a local lease, or the like mode, and may be a two-wheeled or three-wheeled bicycle, a power-assisted vehicle, an electric vehicle, or a motor vehicle with four or more wheels.
The object positioning method is implemented by the server 1000 shown in fig. 1.
As shown in fig. 2, the object positioning method includes: steps S2100-S2500.
In step S2100, the server 1000 obtains the geographic positions reported by the vehicle 3000 continuously for a plurality of times.
The vehicle 3000 may be a two-wheeled or three-wheeled bicycle, a power-assisted vehicle, an electric vehicle, or a motor vehicle having four or more wheels.
The time period for which the vehicle 3000 is observed and positioned is the observation period. In the present embodiment, the observation period is set to a period of time from when the lock of the vehicle 3000 is closed to when the lock is opened next time. During the observation period, the vehicle 3000 is in a non-moving state.
In this embodiment, a GPS positioning mode is adopted. The positioning device 3700 of the vehicle 3000 is able to acquire satellite signals of a GPS positioning system and calculate therefrom the coordinates, such as longitude and latitude, of the geographic position of the vehicle 3000. The communication device 3400 of the vehicle 3000 can report the geographical location information to the server 1000 via the network 4000. During the observation period, the vehicle 3000 reports the geographic position to the server 1000 at regular time intervals, and the geographic positions reported by the vehicle 3000 continuously and repeatedly are recorded as x in sequence1,x2,x3……xn。
In step S2200, the server 1000 determines a distance between two adjacent reported geographic positions as a drift distance of the previous geographic position or the next geographic position.
The vehicle 3000 is in a non-moving state during the observation period, and the actual geographic position of the vehicle 3000 is kept unchanged. But due to the presence of multipath errors, the plurality of geographic locations x of vehicle 3000 measured by GPS positioning1,x2,x3……xnThere is usually a difference, i.e. there is a drift between the geographical locations of two adjacent reports.
The server 1000 calculates the geographical position x reported by the vehicle 3000 last timei-1And geographical position x reported lateriCalculating the distance between the two points, and recording the distance as the geographical position x reported last timei-1Drift distance l ofi-1At this point, the first measured geographic location x1There is no corresponding drift distance; or, the distance is recorded as the geographical position x reported lateriDrift distance l ofiAt this point, the last measured geographic location xnThere is no corresponding drift distance. Wherein i is more than or equal to 2 and less than or equal to n, li-1Drift distance, l, for geographical position reported i-1iAnd the drift distance of the geographical position reported in the ith time.
Step S2300, the server 1000 is according toAcquired geographic location xiDetermining the drift distance cumulative distribution function F (l) of the geographic unit to which the geographic unit belongsiDrift distance l ofiCumulative distribution probability of F (l)i)。
And in the same place, carrying out multiple positioning observation on a plurality of target objects, wherein the obtained drift distance value of each target object is a random variable, and the drift distances of all the target objects obey Weibull distribution. Therefore, for any geographic cell, the drift distance cumulative distribution function of the geographic cell may be considered to follow a weibull distribution, and when the drift distance cumulative distribution function adopts the weibull distribution, the expression of the drift distance cumulative distribution function of the geographic cell may be:
where l represents the drift distance in meters. In the mathematical sense of the weibull distribution function, λ represents the scale parameter of the data distribution, and in this embodiment, λ represents the scale parameter of the drift distance distribution, in meters. In the mathematical sense of the weibull distribution function, k represents the shape parameter of the data distribution, and in the present embodiment, k represents the shape parameter of the drift distance distribution, being dimensionless. The value of F (l) is the cumulative distribution probability of the drift distance l.
Because the multipath effect error is closely related to the spatial characteristics of the geographical position of the target object, the distribution rule of the drift distance l is also closely related to the position of the target object. If a geographical area is divided into a plurality of geographical units, the probability distribution rules of the drift distance l in different geographical units are different, which is embodied that the values of the parameters λ and k are usually different for different geographical units. The parameters λ, k may reflect the multipath characteristics of the geographic unit.
Prior to this step, server 1000 has acquired each geographic location xiA cumulative distribution function of drift distances for the geographic unit. On the basis of the above-mentioned information, every geographical position xiDrift distance l ofiBrought into corresponding geographical cellsAccumulating the distribution function, calculating to obtain each geographical position xiDrift distance l ofiCumulative distribution probability of F (l)i)。
Mathematically, the cumulative distribution function is used to describe the probability that a random variable falls on any interval for which it is more reliable when the cumulative distribution probability is smaller, indicating that the probability that the random variable falls within that interval is higher. Further, the drift distance cumulative distribution function can adopt a Weibull distribution, and the Weibull distribution is applied to credibility analysis, so that failure (unreliable) data can be analyzed easily, and the method is suitable for failure prediction of small data samples.
In the following of the description, it is described how to predetermine the drift distance cumulative distribution function of a geographical cell, wherein it is described how to determine the scale parameter λ of the drift distance distribution and the shape parameter k of the drift distance distribution.
Step S2400, the server 1000 according to the geographic location xiDrift distance l ofiCumulative distribution probability of F (l)i) For the geographic position xiAnd (5) screening.
Geographical location xiDrift distance l ofiCumulative distribution probability of F (l)i) The confidence level of the corresponding geographical location is reflected. Local geographic location xiDrift distance l ofiCumulative distribution probability of F (l)i) The smaller the corresponding drift distance l is illustratediSmaller, its corresponding geographic location xiHigher confidence level of, conversely, geographic location xiDrift distance l ofiCumulative distribution probability of F (l)i) The larger the drift distance l, the corresponding drift distance l is indicatediThe larger, the corresponding geographic location xiThe lower the confidence level of. Therefore, geographical locations with low confidence levels may be filtered out by step S2400. In subsequent step S2500, the geographical position of the vehicle 3000 is determined by using the geographical position with higher confidence level that is retained, so that the geographical position of the vehicle 3000 can be determined more accurately.
In this embodiment, the cumulative distribution probability F (l) is removed by screeningi) Drift distance l greater than a predetermined thresholdiCorresponding geographical position xiRetention of cumulative distribution probability F (l)i) Drift distance l less than or equal to preset thresholdiCorresponding geographical position xi. For example, in practical applications, the preset threshold may be set to 95%.
In this embodiment, if the distance between two adjacent reported geographical locations is taken as the drift distance of the next geographical location, the geographical location sequence x1,x2,x3……xnFirst geographical position x in (2)1There is no corresponding drift distance and it cannot pass the screening, so the geographical position x reported for the first time1Will not be retained.
On the contrary, if the distance between two adjacent reported geographical positions is used as the drift distance of the previous geographical position, the geographical position sequence x1,x2,x3……xnLast geographical position x in (2)nThere is no corresponding drift distance and the geographical position x reported last time can not pass the screeningnWill not be retained.
In step S2500, the server 1000 determines the position of the vehicle 3000 according to the screened geographic position.
For example, in step S2500, of the screened geographic positions, the last geographic position is selected as the position of the target object.
For example, in step S2500, the center position of the screened plurality of geographic positions is calculated, and the center position is set as the position of the target object. Using the center position of the screened plurality of geographic positions as the position of the target object allows the position of the target object to have a higher possibility of accuracy. For example, after the filtering step S2400, if only two geographic positions are reserved, the center point of the connection line between the two geographic positions is used as the position of the target object.
For example, after the filtering step S2400, only three geographical positions are reserved, and a triangle formed by the three geographical positions is used, and in step S2500, the intersection point of three central lines of the triangle is taken as the position of the target object.
For example, after the filtering step S2400, only four geographic positions are reserved, and a quadrangle formed by the four geographic positions is determined as the position of the target object at the intersection of two diagonal lines of the quadrangle in step S2500.
The object positioning method provided by the embodiment has the following advantages:
(1) the satellite positioning data is screened by combining the spatial characteristics of the specific geographic unit where the target object is located, the multipath effects of different geographic units are subjected to personalized processing, the positioning error caused by the multipath effect is effectively reduced, and the positioning precision of the target object is improved.
(2) The position of the target object is calibrated based on the cumulative distribution function of the drift distance of the geographic unit, extra hardware cost is not needed, the operation is simple and easy, and the coverage rate is high.
As shown in fig. 3, before acquiring the drift distance cumulative distribution function of the geographic cell to which the geographic location belongs in step S2300, a step of determining the drift distance cumulative distribution function of the geographic cell in advance is further included.
In one embodiment, a plurality of sample objects are preset in a whole geographic area containing a plurality of geographic units, wherein at least one sample object is arranged in each geographic unit, and the sample objects are slow-moving objects or static objects. And acquiring sample geographical positions reported by all sample objects continuously for multiple times, and determining the drift distance of the sample geographical positions according to the method for determining the drift distance. For any geographic cell, a sample geographic location belonging to the geographic cell is identified, the sample geographic location belonging to the geographic cell referring to a sample geographic location that is geographically located within the geographic cell. For any geographic cell, calculating the cumulative distribution probability of the drift distance of the sample geographic position belonging to the geographic cell according to the drift distance of the sample geographic position belonging to the geographic cell, and estimating the parameter of the drift distance cumulative distribution function of the geographic cell according to the cumulative distribution probability of the drift distance of the sample geographic position belonging to the geographic cell.
The following describes a process for determining a drift distance cumulative distribution function for a geographic unit a by using a specific example, and the process for determining the drift distance cumulative distribution function for other geographic units is similar to the process, and is not repeated:
s3100, acquiring sample geographical positions reported by sample objects continuously for multiple times;
s3200, determining the distance between the geographical positions of the samples reported twice in the adjacent time, wherein the distance is used as the drift distance of the geographical position of the previous sample or the geographical position of the next sample;
and S3300, determining a drift distance cumulative distribution function of the geographic unit A according to the drift distances of the sample geographic positions located in the geographic unit A.
In one example, the sample object is a vehicle. In total, 20 sample vehicles that appeared in the geographic area were selected, and each sample vehicle reported 10 geographic locations, for a total of 200 sample geographic locations. In calculating the cumulative distribution function of drift distances for a geographic cell of a geographic area, sample geographic locations within the geographic cell are summed, e.g., 20 sample geographic locations are located within the geographic cell, the 20 sample geographic locations possibly being from different vehicles. The drift distance of the 20 sample geographical positions is calculated, and the drift distance of the sample geographical positions is hereinafter referred to as a sample drift distance, so that 20 sample drift distances can be obtained. Determining a drift distance cumulative distribution function for the geographic cell based on the 20 sample drift distances.
As shown in fig. 4, in step S3300, the following steps are included:
s4100, calculating the cumulative distribution probability of the drift distance of the sample geographic position in the geographic unit A according to the drift distance of the sample geographic position in the geographic unit A;
s4200, estimating parameters of the drift distance cumulative distribution function of the geographic unit a by a least square method according to the cumulative distribution probability of the drift distance of the sample geographic position located within the geographic unit a.
The parameter estimation method for the cumulative distribution function is as follows:
located in a geographical unitThe sample drift distances in A are p in total, and p is a natural number. The sample drift distances for sample geographic locations located within geographic cell A are first ordered from small to large, such that l1≤l2≤l3…≤lm≤…lpFrom this, a sample drift distance sequence [ l ] is obtained1,l2,l3…lm…lp]。
Let ljIs the jth sample drift distance in the sequence, lj+1Is the (j + 1) th sample drift distance in the sequence,/mIs the shift distance of the mth sample in the sequence, j is more than or equal to 1 and less than or equal to p, and m is more than or equal to 1 and less than or equal to p.
Then the cumulative distribution probability can be calculated from the sample drift distance:
in one example, the sample drift sequence arranged from small to large is [1, 2, 2, 3, 3, 4 ]. The process of calculating the cumulative distribution probability corresponding to each sample drift distance is as follows:
due to l1＝1＜2＝l2Is provided with
Due to l2＝2＝2＝l3Having F (l)2)＝F(l3). Due to l3＝2＜3＝l4Is provided with
Due to l4＝2＝2＝l5Having F (l)4)＝F(l5). Due to l5＝3＜4＝l6Is provided with
Due to l6For the last value in the sample drift sequence, there are
From this calculation, each sample is obtainedAnd the cumulative distribution probability corresponding to the drift distance.
For estimating the parameters of the cumulative distribution function of drift distances for the geographic unit A, the cumulative distribution function of drift distances is
By performing an equivalent transformation, a
Wherein, let X be lnl,
the above equation can be written as
Y＝kX-klnλ
The above equation satisfies the form of a linear equation, and parameter estimation can be performed by a least square method.
Reissue to order
According to the least square method, the following can be estimated:
wherein, Xj，Yj，Can be calculated by the sample drift distance.
In this embodiment, the method for determining the geographic unit includes: a geographical area is divided into a plurality of grid areas in advance, and each grid area is used as a geographical unit.
In dividing the geographical cells, there are many options for the shape of the grid area, such as squares, triangles, or hexagons. The grid areas in this embodiment are all regular hexagons, that is, the geographic area is divided into honeycomb shapes. For one regular hexagon in the honeycomb shape, there are six regular hexagons adjacent to it (co-edge or co-point). Assuming that an object moves from the center of the hexagon in a random direction by a distance equal to the side length of a regular hexagon so that the object eventually falls into one of six adjacent regular hexagons, the probability of the object falling into each adjacent regular hexagon is the same. Therefore, the regular hexagon is adopted as the shape of the grid area, so that the positioning precision is improved, and the statistical deviation is not easy to occur.
Furthermore, the side length of the regular hexagonal area in the embodiment is 3 meters, and the regular hexagonal grid with the size can reasonably contain the target object and realize finer geographical division, and is particularly suitable for positioning a bicycle.
< example >
The drift distances in a geographic unit are counted and the located object is a shared bicycle. Two positioning methods are respectively adopted for positioning. The first method uses a positioning method in the prior art, i.e., does not screen the geographical position reported by the vehicle. The second method adopts the object positioning method of the invention to screen the geographical position reported by the vehicle according to the drift distance cumulative distribution function of the geographical unit. And taking the drift distance larger than 20 meters as the abnormal drift distance, and respectively calculating the proportion of the abnormal drift distance in the two methods. In the first method, the ratio of the abnormal drift distance is the ratio of the drift distance greater than 20 m to all reported drift distances. In the second method, the abnormal drift distance is the proportion of the drift distance larger than 20 m to the drift distance remained after screening. The comparative results are given in the following table:
| Prior Art | Positioning method of the invention | |
| Ratio of abnormal drift distance | 40％ | 5％ |
It can be seen from the comparison results that the proportion of abnormal drift distance is significantly reduced by adopting the object positioning method of the present invention.
< Server >
In this embodiment, there is also provided a server 200, as shown in fig. 5, including:
a memory 210 for storing executable instructions;
and the processor 220 is configured to execute the server to execute any one of the object positioning methods provided in the present embodiment according to the control of the executable instructions.
In this embodiment, the server 200 may be embodied in various forms of entities. For example, the server 200 may be a cloud server. The server 200 may also be the server 1000 as shown in fig. 1.
In one example, where the server 200 is used for vehicle positioning, the server 200 is capable of providing all of the functionality necessary to support vehicle use.
< object positioning System >
In this embodiment, there is provided an object positioning system comprising:
the server 200 provided in the foregoing embodiment;
and an object to be positioned.
< vehicle positioning System >
In the present embodiment, there is provided a vehicle positioning system 400, as shown in fig. 6, including:
the server 200 provided in the foregoing embodiment;
and a vehicle 300.
In this embodiment, the vehicle positioning system 400 may be a shared bicycle positioning system, a shared motor vehicle positioning system, or the like.
In one example, the vehicle 300 may be the shared vehicle 3000 shown in fig. 1.
In the vehicle positioning system 400, each shared bicycle is provided with an intelligent lock, and a GPS module is provided in the intelligent lock to obtain the position data of the shared bicycle.
For the shared bicycle as the target vehicle 300, the shared bicycle may periodically acquire its own position data through its GPS module according to a preset observation period, and send the position report information to the server 200.
In one example, the server 200 has obtained the cumulative distribution probability of the drift distance of the geographic location by performing steps S2100-2300 shown in fig. 1. During the observation period, the target vehicle 300 reports its own geographical position to the server 200 a plurality of times in succession. The server 200 determines the drift distance of each geographic position according to the received geographic position of the target vehicle 300, and then obtains the cumulative distribution probability corresponding to each drift distance according to the cumulative distribution probability of the drift distance of the geographic position. After obtaining the cumulative distribution probability corresponding to the drift distance, the server 200 screens out the drift distance with the cumulative distribution probability meeting the requirement according to a preset threshold value, and then obtains the geographical position meeting the requirement. The server 200 then determines the geographic location of the target vehicle 300 according to the screened geographic location.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. An object positioning method, comprising:
acquiring the geographical positions of the target object reported continuously and repeatedly;
determining the distance between the geographical positions reported twice in the adjacent modes as the drift distance of the geographical position at the previous time or the geographical position at the next time;
determining the cumulative distribution probability of the drift distance of the geographic position according to the obtained cumulative distribution function of the drift distance of the geographic unit to which the geographic position belongs and the drift distance of the geographic position;
screening the geographic position according to the cumulative distribution probability of the drift distance of the geographic position;
and determining the position of the target object according to the screened geographic position.
2. The method of claim 1, wherein the filtering the geographic locations according to the cumulative distribution probability of drift distances of the geographic locations comprises:
screening the geographical position corresponding to the drift distance with the cumulative distribution probability less than or equal to a preset threshold value.
3. The method of claim 1, wherein the determining the location of the target object according to the screened-out geographic locations comprises:
taking the geographical position of the last time in the screened geographical positions as the position of the target object; alternatively, the first and second electrodes may be,
and determining the central positions of the screened geographic positions, and taking the central positions as the positions of the target objects.
4. The method according to any one of claims 1-3, further comprising, before obtaining the cumulative distribution function of drift distance of the geographic cell to which the geographic location belongs, the step of predetermining the cumulative distribution function of drift distance of the geographic cell:
acquiring sample geographical positions of sample objects reported continuously and repeatedly;
determining the distance between the sample geographic positions reported twice in the adjacent time as the drift distance of the previous sample geographic position or the next sample geographic position;
determining a cumulative distribution function of drift distances for the geographic cells based on drift distances for sample geographic locations located within the geographic cells.
5. The method of claim 4, wherein said determining a cumulative distribution function of drift distances for the geographic cell as a function of drift distances for sample geographic locations located within the geographic cell comprises:
calculating a cumulative distribution probability of drift distances of the sample geographic positions located within the geographic cell according to the drift distances of the sample geographic positions located within the geographic cell;
and estimating parameters of the drift distance cumulative distribution function of the geographic unit by a least square method according to the cumulative distribution probability of the drift distance of the geographic position of the sample positioned in the geographic unit.
6. The method of claim 5, wherein the drift distance cumulative distribution function employs a Weibull cumulative distribution function; the estimating parameters of the drift distance cumulative distribution function of the geographic cell includes estimating shape parameters and scale parameters of the drift distance cumulative distribution function of the geographic cell.
7. The method of claim 1, wherein the geographic cells are regular hexagonal areas, square areas, or regular triangular areas.
8. The method of claim 7, wherein the sides of the regular hexagonal region are 3 meters.
9. A server, comprising:
a memory for storing executable instructions;
a processor for operating the server to perform the object localization method according to any one of claims 1-8, under the control of executable instructions.
10. An object positioning system, comprising:
an object;
and a server as claimed in claim 9.
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| CN110766518A (en) * | 2019-09-29 | 2020-02-07 | 北京摩拜科技有限公司 | Article positioning method, device, server and system |
| CN110716223A (en) * | 2019-09-30 | 2020-01-21 | 北京摩拜科技有限公司 | Vehicle positioning method and server |
| CN110927764A (en) * | 2019-11-08 | 2020-03-27 | 北京摩拜科技有限公司 | Vehicle positioning method and device and vehicle |
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