CN114640950A - Mobile equipment positioning method and system based on Android source GPS positioning API - Google Patents

Mobile equipment positioning method and system based on Android source GPS positioning API Download PDF

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Publication number
CN114640950A
CN114640950A CN202210216884.9A CN202210216884A CN114640950A CN 114640950 A CN114640950 A CN 114640950A CN 202210216884 A CN202210216884 A CN 202210216884A CN 114640950 A CN114640950 A CN 114640950A
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positioning
data
android
api
gps
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张伟斌
吕明霄
张诚鑫
刘小秋
刘玮
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Jinyan Zhanyun Information Technology Nanjing Co ltd
Nanjing University of Science and Technology
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Jinyan Zhanyun Information Technology Nanjing Co ltd
Nanjing University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention provides a mobile equipment positioning method and system based on an Android originated GPS positioning API, wherein original GPS data and network positioning data are obtained through the Android API; based on a Kalman filtering method, noise, data jumping fluctuation and unsmooth longitude and latitude traces in original GPS data and network positioning data are filtered, and interpolation is carried out on a filtering result; and outputting and uploading the predicted trace points and drawing the trace according to the user request. The invention has low cost of hardware equipment, and can better meet the requirements of improving positioning precision, uploading cloud and drawing local tracks of users.

Description

Mobile equipment positioning method and system based on Android originated GPS positioning API
Technical Field
The invention relates to a positioning technology, in particular to a mobile equipment positioning method and system based on an Android-originated GPS positioning API.
Background
At present, Android equipment carries GPS positioning and network positioning functions, the accuracy of chips does not reach a high-accuracy level, under the condition that GPS signals are weak or network signals are weak, the positioning of the equipment can have phenomena of drifting, jumping and the like, and the positioning of the equipment has a hysteresis phenomenon, so that the Android equipment is necessary to research on the source GPS positioning of the Android equipment. At present, high-precision positioning has means such as RTK (real time kinematic) which can realize centimeter-level positioning, but requires a reference station and a user receiver to carry out difference solving, so that the high-precision positioning is expensive in manufacturing cost and not easy to carry.
Disclosure of Invention
The invention aims to provide a mobile equipment positioning method and system based on an Android originated GPS positioning API.
The technical solution for realizing the purpose of the invention is as follows: a mobile equipment positioning method based on an Android originated GPS positioning API comprises the following steps:
step 1, acquiring original GPS data and network positioning data through android API;
step 2, based on a Kalman filtering method, filtering noise, data jumping fluctuation and unsmooth longitude and latitude traces in original GPS data and network positioning data, and interpolating a filtering result;
and 3, outputting and uploading the predicted trace points and drawing the trace according to the user request.
Further, the mobile device comprises a mobile phone or a tablet device equipped with an android system.
Further, step 1, original GPS data and network positioning data are obtained through the android API, wherein the original GPS data and the network positioning data comprise longitude and latitude, speed and acceleration information of the mobile equipment.
Further, in step 3, a network request is made through encapsulated retrofitttps, a retrofit interface is used, when a user needs to upload positioning data, longitude and latitude of the positioning data are stored in the bean, and the bean is converted into a character string to be uploaded to the server.
Further, in step 3, after the user uploads the positioning data, the historical data is obtained through a get request, the request includes token data used for determining the identity of the user, the historical data is obtained through a network request, and the data points are plotted through addPloyline in a drawLines () method.
The mobile equipment positioning system based on the Android-originated GPS positioning API realizes the positioning of the mobile equipment based on the Android-originated GPS positioning API based on the mobile equipment positioning method based on the Android-originated GPS positioning API.
Compared with the prior art, the invention has the following remarkable advantages: the cost of the hardware equipment is low, and the requirements of improving the positioning precision, uploading cloud and drawing local tracks of a user can be better met.
Drawings
Fig. 1 is a schematic diagram of the overall architecture of the positioning system of the mobile device of the present invention.
Fig. 2 is a block diagram of a mobile device location method of the present invention.
FIG. 3 is a diagram of a new time series after interpolation according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
According to the mobile equipment positioning method based on the Android source GPS positioning API, the Android API is used for acquiring original GPS data and network positioning data, noise, data jumping fluctuation and unsmooth longitude and latitude traces generated by the GPS and network positioning are subjected to real-time data processing through a Kalman filtering method, predicted traces can be output, and the predicted traces are uploaded to a cloud terminal for trace drawing.
(one) acquiring data
In the system architecture diagram of fig. 1, a user carries an Android device to keep a stationary state or a mobile state in an open GPS and a place with a good network signal. The Android device can be a mobile phone or a tablet device, and longitude and latitude, speed and acceleration information of the device are acquired through an Android system native API.
(II) Kalman filtering
The Kalman filtering has a five-core formula, the one-step prediction is obtained by a measurement updating value at the last moment through a state transition relation, and the current filtering value is only related to the measurement value at the current moment and the one-step prediction value, so that the Kalman filtering is suitable for computer programming because the Android equipment is determined to be used for carrying out data processing on GPS and network positioning data by using the Kalman filtering.
(1) The state equation is as follows:
xk＝Axk-1+Buk-1+wk-1 (1)
where A is the state transition matrix of the system, B represents the control gain, uk-1Representing a control signal.
Figure BDA0003533570760000031
The used data are longitude and latitude, which are obtained by the position request of Android, wherein w in the last itemk-1Is the noise generated by the prediction process and E (w)k-1)＝0，xkIs the state x from the last moment in timek-1And control variables.
(2) The observation equation:
zk＝Hxk+vk (3)
wherein v iskIs the noise in the observation, H represents the measurement matrix, zkThe measured values are indicated.
(3) Filter time update equation of kalman:
Figure BDA0003533570760000032
wherein A ═ 10]，Φ＝[noise2]And phi is the noise matrix of the system,
Figure BDA0003533570760000033
refers to the prior estimated covariance, Γ, at time kk-1Representing the a posteriori estimated covariance at time k-1.
(4) The state update equation of the kalman filter:
Figure BDA0003533570760000034
Figure BDA0003533570760000035
Figure BDA0003533570760000036
Gkexpressed is the Kalman gain, which is the intermediate result of the filtering, zkRepresenting the measured values, H representing a measurement matrix, capable of converting the measured values of m dimensions to n dimensions corresponding to the state variables, R representing the covariance of the measurement noise,
Figure BDA0003533570760000037
the prior state estimate representing time k is the intermediate result of the filtering, the result of the k time predicted from the optimal estimate of the last time (time k-1), the result of the prediction equation,
Figure BDA0003533570760000038
the Kalman gain G is sought for the a posteriori estimate of the time k, the so-called optimum estimatekIt can make
Figure BDA0003533570760000039
Has a minimum variance of gammakRepresenting the a posteriori estimated covariance at time k.
(III) interpolation output
For acquiring original GPS data and network positioning data within a certain time t, the original GPS data and the network positioning data are acquiredThe network positioning data are independent of each other, and a user can select to filter the GPS data through Kalman filtering or filter the network positioning data, for example, the GPS data is selected as raw data input, the data comprise longitude and latitude, speed and acceleration information of an Android system originated API acquisition device, and the information is defined as R ═ R0,r1,...,rn]Comprising n time series. Because the GPS data and the network positioning data acquired by the equipment may have position drift within a certain time caused by jumping and shielding, more accurate longitude and latitude values are acquired through prediction and correction by combining the longitude and latitude, the speed and the acceleration through Kalman filtering, and a new time sequence Rk (r) is obtained through interpolation0,r1,r2,...,ri,...,rn,rn+1]Adding more detail to the data.
(IV) uploading and drawing tracks
And carrying out network request through the encapsulated retrofithttps, using a retrofit interface, starting to store the longitude and latitude of the positioning data into the bean when a user clicks a Button control of a map interface for starting positioning, and converting the bean into a character string and uploading the character string to the server.
After uploading a track, a user acquires historical data through a get request, the request comprises token data, a corresponding user can be determined, the historical data is acquired through a network request, one of the historical records is selected to jump to a map drawing track interface, and data points are drawn through addPloyline in a drawLines () method.
The invention further provides a mobile equipment positioning system based on the Android source GPS positioning API.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims (6)

1. A mobile equipment positioning method based on an Android originated GPS positioning API is characterized by comprising the following steps:
step 1, acquiring original GPS data and network positioning data through android API;
step 2, based on a Kalman filtering method, filtering noise, data jumping fluctuation and unsmooth longitude and latitude traces in original GPS data and network positioning data, and interpolating a filtering result;
and 3, outputting and uploading the predicted trace points and drawing the trace according to the user request.
2. The Android-originated GPS location API-based mobile device location method of claim 1, wherein the mobile device comprises a mobile phone or tablet device equipped with an Android system.
3. The Android-originated GPS positioning API-based mobile device positioning method of claim 1, wherein in step 1, raw GPS data and network positioning data, including longitude and latitude, speed and acceleration information of the mobile device, are acquired through the Android API.
4. The method for positioning the mobile device based on the Android originated GPS positioning API of claim 1, wherein in step 3, a network request is made through encapsulated retrofithttps, and using a retrofit interface, when a user needs to upload positioning data, the longitude and latitude of the positioning data are saved in a bean, and the bean is converted into a character string and uploaded to a server.
5. The Android source GPS positioning API-based mobile device positioning method as recited in claim 1, wherein in step 3, after the user uploads the positioning data, the user obtains historical data through get request, the request includes token data for determining user identity, the historical data is obtained through network request, and the data points are plotted through addPloyline in a drawLines () method.
6. A mobile device positioning system based on an Android originated GPS positioning API, characterized in that the mobile device positioning based on the Android originated GPS positioning API is realized based on the mobile device positioning method based on the Android originated GPS positioning API of any one of claims 1 to 5.
CN202210216884.9A 2022-03-06 2022-03-06 Mobile equipment positioning method and system based on Android source GPS positioning API Pending CN114640950A (en)

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CN107807373A (en) * 2017-10-17 2018-03-16 东南大学 GNSS high-precision locating methods based on mobile intelligent terminal
CN110118988A (en) * 2019-05-28 2019-08-13 上海擎感智能科技有限公司 A kind of combined positioning method and its device
CN110232169A (en) * 2019-05-09 2019-09-13 北京航空航天大学 Track denoising method based on two-way length memory models and Kalman filtering in short-term
CN110275192A (en) * 2019-05-22 2019-09-24 东南大学 A kind of high-precision point positioning method and device based on smart phone
CN112578419A (en) * 2020-11-24 2021-03-30 南京邮电大学 GPS data reconstruction method based on GRU network and Kalman filtering
CN114124033A (en) * 2021-10-11 2022-03-01 北京川速微波科技有限公司 Kalman filter implementation method, device, storage medium and equipment

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013003277A1 (en) * 2011-06-27 2013-01-03 Google Inc. Gps and mems hybrid location-detection architecture
EP2716117A1 (en) * 2011-06-27 2014-04-09 Google, Inc. Gps and mems hybrid location-detection architecture
US20130069821A1 (en) * 2011-09-21 2013-03-21 Cambridge Silicon Radio Ltd. Method and Apparatus of Using Height Aiding From a Contour Table for GNSS Positioning
US20150369924A1 (en) * 2013-02-04 2015-12-24 Vanderbilt University Method and system for high-accuracy differential tracking of global positioning system (gps) receivers
CN105005066A (en) * 2015-07-20 2015-10-28 上海北斗卫星导航平台有限公司 High-accuracy positioning system and method based on Android system
CN107807373A (en) * 2017-10-17 2018-03-16 东南大学 GNSS high-precision locating methods based on mobile intelligent terminal
CN110232169A (en) * 2019-05-09 2019-09-13 北京航空航天大学 Track denoising method based on two-way length memory models and Kalman filtering in short-term
CN110275192A (en) * 2019-05-22 2019-09-24 东南大学 A kind of high-precision point positioning method and device based on smart phone
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CN114124033A (en) * 2021-10-11 2022-03-01 北京川速微波科技有限公司 Kalman filter implementation method, device, storage medium and equipment

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