CN114302359B - WiFi-PDR fusion-based high-precision indoor positioning method - Google Patents
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Abstract
The invention discloses a high-precision indoor positioning method based on WiFi-PDR fusion, which comprises the following steps: collecting environmental beacon data; matching a pre-established position-received signal strength fingerprint library to obtain preliminary positions of a plurality of targets; according to the reference path loss coefficient and the ITU model, calculating to obtain the weight coefficient of each AP, and calculating the coarse position of the target; collecting a course angle and an acceleration value of a target; smoothing the original acceleration value data to reduce noise interference; dynamically setting state conversion parameters according to the noise reduction data acquired in real time, and calculating the number of steps of the target; according to a nonlinear step length estimation method, calculating to obtain a single step length of a target; and carrying out fusion calculation through a self-adaptive unscented particle filtering algorithm to obtain the accurate motion state information of the target. The invention improves the WiFi fingerprint positioning precision, reduces the error accumulation effect of the PDR method, optimizes the WiFi-PDR fusion positioning method, improves the positioning continuity and stability, and ensures that the indoor positioning is more accurate and effective.
Description
Technical Field
The invention relates to the field of signal processing and information fusion, in particular to a precision indoor positioning method based on WiFi-PDR fusion.
Background
Thanks to the development of information technology, positioning technology is now widely used in the fields of industry, scientific research, commerce and the like. The high-precision indoor positioning technology plays an important role in production and life or national defense and military, and has wide development prospect. However, because the indoor space is relatively closed, the environment is complex, and outdoor positioning navigation means such as a global positioning system and the like have the problems of easy attenuation, serious degradation and the like of signals indoors, the indoor positioning precision requirement cannot be met.
At present, no single wireless positioning technology can simultaneously meet the requirements of accurate positioning, high real-time performance, strong adaptability, high reliability and low cost. The multi-sensor fusion positioning technology realizes the effects of complementation and cooperative work of the multi-sensor information by fusion processing of various sensor information, thereby improving the reliability of the whole system. Wireless networks are now popular and low cost, and WiFi-based positioning is widely used. WiFi positioning typically employs a fingerprinting method, where positioning is achieved by matching a location fingerprint database between pre-established location-signal strength (RSSI) feature vectors with RSSI information collected from real measurement points from various wireless Access Points (APs). Another positioning technology widely applied to mobile equipment is Pedestrian Dead Reckoning (PDR) positioning, which is to perform double integration on an acceleration value obtained by an accelerometer to detect the number of steps and the step length, and complete positioning by combining with azimuth information of a gyroscope or an electronic compass. These techniques have the following problems:
(1) The WiFi positioning accuracy is more dependent on the quality of a WiFi fingerprint database, the indoor environment is complex and changeable, and WiFi fingerprint data are easy to fluctuate.
(2) Because the built-in sensor of many mobile terminals is not high in accuracy, limbs can produce irregular rocking in walking simultaneously for PDR location has error and can accumulate constantly over time, and the continuous use PDR location error of a long time is great.
(3) The existing WiFi-PDR fusion positioning technology is not stable enough and cannot perform robust self-adaptation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a WiFi-PDR fusion-based high-precision indoor positioning method, which aims to solve the problems that WiFi fingerprint positioning data fluctuation and PDR positioning errors are continuously accumulated along with time and the stability of the existing WiFi-PDR fusion positioning technology is insufficient.
In order to solve the problems, the invention provides a high-precision indoor positioning method based on WiFi-PDR fusion, which comprises the following steps:
s1: calculating to obtain a coarse position, a step number and a single step length of the target; wherein,,
the coarse position calculation step of the target is as follows:
collecting environment beacon data, wherein the environment beacon data comprises a transmitting signal frequency, a ground loss penetration coefficient, the number of floors between a transmitting end and a receiving end and a path loss index;
matching a pre-established position-received signal strength fingerprint library according to target RSSI information received by a plurality of APs to obtain preliminary positions of a plurality of targets; calculating a reference path loss coefficient according to the acquired environmental beacon data, calculating a weight coefficient of each AP according to the reference path loss coefficient and a free space indoor path loss (ITU) model, and calculating a coarse position of a target according to the initial position and the weight of the initial position;
the steps of the target and the single step size are calculated as follows:
collecting a course angle and an acceleration value of a target; smoothing the original acceleration value data to reduce noise interference and obtain noise reduction data; then, according to the noise reduction data acquired in real time, calculating the number of steps of the target by dynamically setting state conversion parameters; according to the noise reduction data acquired in real time, calculating to obtain a single step size of the target through a nonlinear step size estimation method;
s2: and carrying out fusion calculation through a self-adaptive unscented particle filtering algorithm according to the coarse position, the step number and the single step length of the target to obtain the accurate motion state information of the target.
Further, the specific calculation process of the coarse position of the target in step S1 is as follows:
calculating a reference path loss coefficient PL according to the environmental beacon data 0 ：
PL 0 ＝20log(f)+c(k,f)-28
Where f is the frequency of the transmitted signal, c is the ground loss penetration coefficient, and k is the number of floors between the transmitting end and the receiving end;
according to the reference path loss coefficient PL 0 The distance between the ith AP at the moment t and the target is calculated through an ITU model
Wherein the method comprises the steps ofThe RSSI value of the ith AP at the moment t, and alpha is the path loss index;
according toCalculating the weight coefficient of the ith AP at the moment t>The method comprises the following steps:
wherein N is the number of APs;
according to the weight coefficientAnd the preliminary position of the target, calculating to obtain the coarse position (x, y) of the target:
wherein (x) i ,y i ) Is the preliminary location of the target.
Further, the smoothing process in step S1 uses a Simple Moving Average (SMA) algorithm.
Further, the state transition parameters in step S1 include a state threshold, a demarcation reference value, and a zero reference value.
Further, the calculation of the nonlinear step estimation method in step S1 is as follows:
where k is the step size estimation parameter, a max And a min The maximum acceleration value and the minimum acceleration value acquired during one step of target walking respectively size Is a single step size of the target.
Further, the fusion calculation in S2 by the adaptive unscented particle filter algorithm includes the following sub-steps:
s21, modeling the motion of the target in motion according to the coarse position, the step number and the single step length of the target to obtain an actual moving target motion model;
s22: according to the actual moving target motion model, a state equation and an observation equation of a target motion system are established;
s23: calculating to obtain a priori probability density function according to the state equation;
s24: calculating to obtain a likelihood function according to the observation equation and the prior probability density function;
s25: according to the likelihood function, calculating to obtain a posterior probability density function through a Bayes formula;
s26: if the current iteration number does not reach the preset number, substituting the posterior probability density function into the state equation, and repeating S23-S26;
and if the current iteration times reach the preset times, extracting the accurate motion state information of the target from the posterior probability density function.
Further, in S22, the state equation and the observation equation of the target motion system are established as follows:
x k ＝f(x k-1 ,v k-1 ),y k ＝h(x k ,n k )
wherein x is k Is a state squareProcess x k ，y k To observe equation, x k And y k Are all nonlinear equations, n k V for system process noise k-1 To observe noise.
Further, the posterior probability density function includes a particle filter observation, a state value, an error covariance, and a kalman gain.
Preferably, the target is a mobile intelligent terminal, which comprises a central processing unit, a WiFi module, a gyroscope and a magnetic force sensor.
In general, compared with the prior art, the technical scheme of the invention is used for obtaining the following beneficial effects:
the invention provides a high-precision indoor positioning method based on WiFi-PDR fusion, which aims at the problem of low WiFi positioning precision and optimizes a WiFi fingerprint positioning algorithm by using a weighted path loss algorithm;
aiming at the error accumulation effect of the PDR, the thought of dynamically setting a threshold value to perform gait detection is adopted, and the acceleration value data is subjected to smooth noise reduction;
on the basis of improving WiFi positioning and PDR algorithm, a method for fusion positioning by adopting robust self-adaptive unscented particle filtering is provided, so that the robust positioning and the PDR algorithm are mutually corrected and supplemented, error accumulation of an inertial sensor is eliminated to a certain extent, continuity and stability of WiFi fingerprint positioning are optimized, and indoor positioning is more accurate and effective.
Drawings
FIG. 1 is a block diagram of the overall flow of a high-precision indoor positioning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a specific flow for calculating a weighted coarse position in a high-precision indoor positioning method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a specific flow chart of fusion calculation performed by the adaptive unscented particle filter algorithm in the high-precision indoor positioning method according to the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a high-precision indoor positioning method based on WiFi-PDR fusion, which comprises the following steps of: the positioning scene selects an office building, an active area comprises a corridor and an exhibition hall, and an AP is arranged on a target travelling path to collect RSSI fingerprints. Setting a sampling point every 1m, collecting RSSI values 5 times for each sampling point, and constructing a position-received signal strength fingerprint library, wherein the target is millet 8 intelligent mobile phone terminal equipment based on an Android operating system.
As shown in fig. 1, the high-precision indoor positioning method based on WiFi-PDR fusion provided by the embodiment of the invention includes the following steps:
s1: calculating to obtain a coarse position, a step number and a single step length of the target;
the step of calculating the rough position of the target in the step S1 is as follows:
specifically, environmental beacon data are collected, wherein the environmental beacon data comprise the frequency of a transmitted signal, the ground loss penetration coefficient, the floor number between a transmitting end and a receiving end and the access loss index;
calculating a preliminary position: according to the received target RSSI information in a plurality of APs, matching a pre-established position-received signal strength fingerprint library to obtain the preliminary positions (x i ,y i ) Wherein (x) i ,y i ) The preliminary position of the target matched with the ith AP at the moment t;
calculating a weighted coarse position: calculating a reference path loss coefficient according to the acquired environmental beacon data, calculating a weight coefficient of each AP according to the reference path loss coefficient and the ITU model, and calculating a coarse position of the target according to the initial position and the weight of the initial position;
further, as shown in fig. 2, the step of calculating the weighted coarse position includes:
calculating a reference path loss coefficient PL according to the environmental beacon data 0 ：
PL 0 ＝20log(f)+c(k,f)-28
Where f is the frequency of the transmitted signal, c is the ground loss penetration coefficient, and k is the number of floors between the transmitting end and the receiving end;
according to the reference path loss coefficient PL 0 The distance between the ith AP at the moment t and the target is calculated through an ITU model
Wherein the method comprises the steps ofThe RSSI value of the ith AP at the moment t, and alpha is the path loss index;
according toCalculating the weight coefficient of the ith AP at the moment t>The method comprises the following steps:
wherein N is the number of APs;
according to the weight coefficientAnd the preliminary position of the target, calculating to obtain the coarse position (x, y) of the target:
wherein (x) i ,y i ) Is the preliminary location of the target.
The step number and the single step size of the target in the step S1 are calculated as follows:
collecting a course angle and an acceleration value of a target;
specifically, the acceleration value of the target is measured by an accelerometer in the smart phone terminal;
the course angle of the target is obtained by combining a gyroscope and a magnetometer which are arranged in the smart phone terminal. The gyroscope direction measurement has good effect on short-time measurement, but is not suitable for long-time measurement due to error accumulation effect, and the magnetometer has better effect in long-time measurement, but is easily interfered by an external magnetic field, so that larger deviation of azimuth occurs, and therefore, better performance and effect are provided for short-time and long-time experiments by utilizing the complementary characteristics of the magnetometer and the gyroscope;
and (3) data denoising: smoothing the original acceleration value data to reduce noise interference and obtain noise reduction data;
specifically, the original acceleration data has obvious noise because of irregular random swing of the human body in the walking process. In this regard, the raw acceleration data is smoothed based on a Simple Moving Average (SMA) algorithm to reduce noise interference.
The step number calculation sub-steps are as follows:
calculating the number of steps of a target by dynamically setting state transition parameters according to the noise reduction data acquired in real time;
specifically, a walking cycle can be divided into a static state, a peak state and a trough state, the change of speed of a person in the walking process can cause the up-and-down deviation of an acceleration curve, and in order to reduce the error of the identification of the walking cycle, parameters of state transition are dynamically set according to the real-time change of the acceleration curve, wherein the parameters comprise a state threshold value, a demarcation reference value and a zero reference value. First, a state threshold is dynamically set according to a real-time change of an acceleration curve, and maximum and minimum peaks are determined using 0.5 as an upper and lower threshold. The demarcation reference value is a data point representing the start and end of each state, and a state can be considered to be the end of the state and the start of the next state as long as the certain state breaks through the demarcation reference value. Because of the change of the speed in walking, the acceleration curve can deviate up and down, zero or other fixed values are used as zero reference values, and the calculated speed and step errors are large. The zero reference value is dynamically set, so that errors caused by up-and-down offset of an acceleration curve can be reduced, and the accuracy of the calculated speed and step size is ensured.
The single step calculation sub-steps are as follows:
according to the noise reduction data acquired in real time, calculating to obtain a single step size of the target through a nonlinear step size estimation method:
where k is the step size estimation parameter, a max And a min The maximum acceleration value and the minimum acceleration value acquired during one step of target walking respectively size Is a single step size of the target.
S2: according to the coarse position, the step number and the single step length of the target, carrying out fusion calculation through a self-adaptive unscented particle filtering algorithm to obtain the accurate motion state information of the target;
further, as shown in fig. 3, the fusion calculation performed by the adaptive unscented particle filtering algorithm in S2 includes the following sub-steps:
s21, modeling the motion of the target in motion according to the coarse position, the step number and the single step length of the target to obtain an actual moving target motion model;
s22: according to the actual moving target motion model, a state equation and an observation equation of a target motion system are established;
s23: calculating to obtain a priori probability density function according to the state equation;
s24: calculating to obtain a likelihood function according to the observation equation and the prior probability density function;
s25: according to the likelihood function, calculating to obtain a posterior probability density function through a Bayes formula;
s26: if the current iteration number does not reach the preset number, substituting the posterior probability density function into the state equation, and repeating S23-S26;
and if the current iteration times reach the preset times, extracting the accurate motion state information of the target from the posterior probability density function.
Specifically, according to an actual moving object motion model, a state equation and an observation equation of a motion system are established, wherein the state equation x k And observation equation y k The formula is as follows:
x k ＝f(x k-1 ,v k-1 ),y k ＝h(x k ,n k )
x is as above k And y k Are all nonlinear equations, n k V for system process noise k-1 To observe noise, both process noise and observation noise obey independent processes with zero mean.
Specifically, the posterior probability density function includes a particle filter observation, a state value, an error covariance, and a kalman gain.
Specifically, the target is a mobile intelligent terminal, which comprises a central processing unit, a WiFi module, a gyroscope and a magnetic force sensor.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device to perform the method described in the embodiments or some parts of the embodiments.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The high-precision indoor positioning method based on WiFi-PDR fusion is characterized by comprising the following steps of:
s1: calculating to obtain a coarse position, a step number and a single step length of the target; wherein,,
the coarse position calculation step of the target is as follows:
collecting environmental beacon data; matching a pre-established position-received signal strength fingerprint library according to target RSSI information received by a plurality of APs to obtain preliminary positions of a plurality of targets; calculating a reference path loss coefficient according to the acquired environmental beacon data, calculating a weight coefficient of each AP according to the reference path loss coefficient and the ITU model, and calculating a coarse position of the target according to the initial position and the weight of the initial position;
the steps of the target and the single step size are calculated as follows:
collecting a course angle and an acceleration value of a target; smoothing the original acceleration value data to reduce noise interference and obtain noise reduction data; then, according to the noise reduction data acquired in real time, calculating the number of steps of the target by dynamically setting state conversion parameters; according to the noise reduction data acquired in real time, calculating to obtain a single step size of the target by a nonlinear step size estimation method,
the nonlinear step estimation method in step S1 is calculated as follows:
where k is the step size estimation parameter, a max And a min The maximum acceleration value and the minimum acceleration value acquired during one step of target walking respectively size A single step size for the target;
s2: according to the coarse position, the step number and the single step length of the target, the fusion calculation is carried out through the self-adaptive unscented particle filtering algorithm to obtain the accurate motion state information of the target,
the fusion calculation in the S2 through the self-adaptive unscented particle filter algorithm comprises the following substeps:
s21, modeling the motion of the target in motion according to the coarse position, the step number and the single step length of the target to obtain an actual moving target motion model;
s22: according to the actual moving target motion model, a state equation and an observation equation of a target motion system are established;
s23: calculating to obtain a priori probability density function according to the state equation;
s24: calculating to obtain a likelihood function according to the observation equation and the prior probability density function;
s25: according to the likelihood function, calculating to obtain a posterior probability density function through a Bayes formula;
s26: if the current iteration number does not reach the preset number, substituting the posterior probability density function into the state equation, and repeating S23-S26;
and if the current iteration times reach the preset times, extracting the accurate motion state information of the target from the posterior probability density function.
2. The high-precision indoor positioning method according to claim 1, wherein the specific calculation process of the coarse position of the target in step S1 is as follows:
calculating a reference path loss coefficient PL according to the environmental beacon data 0 ：
PL 0 ＝20log(f)+c(k,f)-28
Where f is the frequency of the transmitted signal, c is the ground loss penetration coefficient, and k is the number of floors between the transmitting end and the receiving end;
according to the reference path loss coefficient PL 0 The distance between the ith AP at the moment t and the target is calculated through an ITU model
Wherein the method comprises the steps ofThe RSSI value of the ith AP at the moment t, and alpha is the path loss index;
according toCalculating the weight coefficient of the ith AP at the moment t>The method comprises the following steps:
wherein N is the number of APs;
according to the weight coefficientAnd the preliminary position of the target, calculating to obtain the coarse position (x, y) of the target:
wherein (x) i ,y i ) Is the preliminary location of the target.
3. The method according to claim 1, wherein the smoothing in step S1 is performed by SMA algorithm.
4. The method according to claim 1, wherein the state transition parameters in step S1 include a state threshold, a demarcation reference value, and a zero reference value.
5. The high-precision indoor positioning method according to claim 4, wherein the establishing a state equation and an observation equation of the target motion system in S22 is:
x k ＝f(x k-1 ,v k-1 ),y k ＝h(x k ,n k )
wherein x is k For equation of state x k ，y k To observe equation, x k And y k Are all nonlinear equations, n k V for system process noise k-1 To observe noise.
6. The high-precision indoor positioning method according to claim 5, wherein the posterior probability density function comprises a particle filter observation, a state value, an error covariance, and a kalman gain.
7. The high-precision indoor positioning method according to claim 1, wherein the target is a mobile intelligent terminal comprising a central processor, a WiFi module, a gyroscope and a magnetic force sensor.
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