CN109425341B - Positioning method, positioning device and electronic equipment - Google Patents
Positioning method, positioning device and electronic equipment Download PDFInfo
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- CN109425341B CN109425341B CN201710724036.8A CN201710724036A CN109425341B CN 109425341 B CN109425341 B CN 109425341B CN 201710724036 A CN201710724036 A CN 201710724036A CN 109425341 B CN109425341 B CN 109425341B
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The embodiment of the application provides a positioning method, a positioning device and electronic equipment, wherein the positioning device comprises: a first determination unit configured to determine a set of candidate positions of an object to be positioned at a current time; a first calculation part for calculating the course and step length of the object to be positioned; a second calculating part for calculating the posterior probability corresponding to each candidate position in the candidate position set based on a continuous time hidden Markov model according to the course and the step length; and a third calculation unit that calculates the position of the object to be positioned based on the posterior probabilities corresponding to the respective candidate positions. According to the embodiment, the fusion of the measured motion information of the inertial sensor and the wireless signal is realized based on the continuous-time hidden markov model, so that high positioning accuracy can be maintained without limiting the posture of the mobile terminal, and the convenience in use of the mobile terminal is improved.
Description
Technical Field
The present disclosure relates to the field of positioning technologies, and in particular, to a positioning method, a positioning apparatus, and an electronic device.
Background
The high-precision positioning technology is beneficial to popularization of location-based services, so that better service quality is provided for customers, and the method is widely researched.
With the development of technology, various sensors are integrated into a mobile terminal, such as an inertial sensor including an accelerometer, a gyroscope, and the like, an environmental sensor including a magnetic sensor, and the like, for example.
A body to be positioned, for example a person, may move with the mobile terminal, whereby the inertial sensor is able to make measurements to obtain absolute movement information of the body to be positioned, such as absolute movement direction (i.e. heading) and movement distance (i.e. step size) information.
Generally, the mobile terminal is also provided with a wireless signal transceiving unit, so that the wireless signals received by the mobile terminal and the motion information measured by the inertial sensor can be fused, and higher positioning performance can be obtained.
It should be noted that the above background description is only for the convenience of clear and complete description of the technical solutions of the present application and for the understanding of those skilled in the art. Such solutions are not considered to be known to the person skilled in the art merely because they have been set forth in the background section of the present application.
Disclosure of Invention
The inventors of the present application have found that, in a method for detecting motion information of an object to be positioned by using an inertial sensor mounted on a mobile terminal, it is often required that the object to be positioned has a fixed or specific holding manner for the mobile terminal so that the mobile terminal is held in a fixed or specific posture, for example, the mobile terminal needs to be held by a pedestrian in a fixed posture.
However, in daily life, the holding manner of the pedestrian for the mobile terminal is various and varied, and the absolute heading and step information of the pedestrian calculated based on the measured motion information of the inertial sensor have a large error, so that the accuracy of the positioning result is also lowered.
Embodiments of the present application provide a positioning method, a positioning apparatus, and an electronic device, which implement fusion of motion information measured by an inertial sensor and a wireless signal based on a Continuous time Hidden Markov Model (CHMM), so that a high positioning accuracy can be maintained without limiting the posture of a mobile terminal, and convenience in use of the mobile terminal is improved.
According to a first aspect of embodiments of the present application, there is provided a positioning apparatus, including:
a first determination unit configured to determine a set of candidate positions of an object to be positioned at a current time;
a first calculation part for calculating the course and step length of the object to be positioned;
a second calculating part, which is used for calculating the posterior probability corresponding to each candidate position in the candidate position set based on a continuous time hidden Markov model according to the course and the step length; and
and a third calculation unit that calculates the position of the object to be positioned based on the posterior probabilities corresponding to the respective candidate positions.
According to a second aspect of the present embodiment, there is provided a positioning method, including:
determining a set of candidate positions of an object to be positioned at the current moment;
calculating the course and the step length of the object to be positioned;
calculating the posterior probability corresponding to each candidate position in the set of candidate positions based on a continuous time hidden Markov model according to the course and the step length; and
and calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position.
According to a third aspect of the present embodiment, there is provided an electronic device comprising the positioning apparatus of the first aspect of the embodiment.
The beneficial effect of this application lies in: the mobile terminal can keep high positioning precision under the condition of not limiting the posture of the mobile terminal, and the use convenience of the mobile terminal is improved.
Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and 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. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 is a schematic diagram of a positioning method according to embodiment 1 of the present application;
fig. 2 is a schematic diagram of a method for determining a set of candidate positions of an object to be located at the current time in embodiment 1 of the present application;
FIG. 3 is a schematic diagram of each grid point determined based on the distribution area obtained in the method (a) in example 1 of the present application;
FIG. 4 is a schematic diagram of each grid point determined based on the distribution area obtained in the method (b) in example 1 of the present application;
FIG. 5 is a flow chart of a heading estimation method of embodiment 1 of the present application;
FIG. 6 is a flowchart of a method of calculating the posterior probability of each candidate position according to embodiment 1 of the present application;
FIG. 7 is a schematic diagram of a method of calculating the probability of homogeneous transition according to embodiment 1 of the present application;
FIG. 8 is a schematic view of a positioning device according to embodiment 2 of the present application;
fig. 9 is a schematic diagram of a first determination section in embodiment 2 of the present application;
fig. 10 is a schematic view of a first calculation section in embodiment 2 of the present application;
fig. 11 is a schematic view of a second calculation section of embodiment 2 of the present application;
fig. 12 is a schematic view of a tenth calculation section in embodiment 2 of the present application;
fig. 13 is a schematic view of an electronic device according to embodiment 3 of the present application.
Detailed Description
The foregoing and other features of the invention will become apparent from the following description taken in conjunction with the accompanying drawings. In the description and drawings, particular embodiments of the invention have been disclosed in detail as being indicative of some of the embodiments in which the principles of the invention may be employed, it being understood that the invention is not limited to the embodiments described, but, on the contrary, is intended to cover all modifications, variations, and equivalents falling within the scope of the appended claims.
Example 1
The embodiment 1 of the application provides a positioning method.
Fig. 1 is a schematic diagram of a positioning method of embodiment 1, and as shown in fig. 1, the positioning method may include:
102, calculating the course and the step length of the object to be positioned;
and 104, calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position.
According to the embodiment, the fusion of the measured motion information of the inertial sensor and the wireless signal is realized based on a Continuous time Hidden Markov Model (CHMM), so that high positioning accuracy can be maintained without limiting the posture of the mobile terminal, and the convenience of using the mobile terminal is improved.
In this embodiment, the object to be located may be a pedestrian, a vehicle, or another moving object.
In this embodiment, the movable terminal may be, for example, a smart watch, a smart phone, a portable tablet computer, or the like, the movable terminal may be disposed on the object to be positioned, and move together with the object to be positioned, and the posture of the movable terminal relative to the object to be positioned may be changed.
In the present embodiment, the mobile terminal may be provided therein with an inertial sensor, which may include at least one of an accelerometer, a gyroscope, and the like, for example, wherein the accelerometer may be a three-axis accelerometer, for example. The detection signal output by the inertial sensor may include at least one of an acceleration signal output by an accelerometer and an angular velocity signal output by a gyroscope, where the acceleration signal may be a three-axis acceleration signal, and the angular velocity signal may be a three-axis angular velocity signal.
In this embodiment, the mobile terminal may also receive wireless signals, which may be, for example, high fidelity (WIFI) signals, Bluetooth (Bluetooth) signals, Global Positioning System (GPS), cellular network signals, and/or the like.
In the following description of the present embodiment, the position may be represented by an X coordinate value and a Y coordinate value in a two-dimensional plane, but the present embodiment may not be limited thereto, and for example, the position may also be a position in a two-dimensional plane represented by other coordinate values, and furthermore, the position may also refer to a position in a three-dimensional space represented by a corresponding coordinate value.
Fig. 2 is a schematic diagram of a method for determining a set of candidate positions of an object to be located at the current time in the present embodiment, and the method in fig. 2 is used to implement step 101. As shown in fig. 2, the method may include:
In step 201 of this embodiment, the distribution area of the candidate position of the object to be positioned at the current time may be determined according to the manner (a), that is, the distribution area of the candidate position of the object to be positioned at the current time may be determined according to the wireless signal acquired by the mobile terminal and/or the detection signal output by the inertial sensor before the current time.
In one embodiment, in step 201, the distribution area may be determined according to the position of the object to be located estimated based on the wireless signal within a first predetermined time period before the current time.
For example, the current time is the t-th time, the first predetermined time period includes t +1 times from the (t- τ) th time to the t-th time, and the position of the object to be positioned estimated based on the wireless signal at each time may be l t ＝[l t-τ ,l t-τ+1 ,...,l t ]Wherein τ is a natural number; l t Maximum value X of X-coordinate values at respective positions within the block max And the minimum value x min And the maximum value Y of the Y coordinate value max And the minimum value y min To determine a distribution area of the candidate position, i.e., a range of the X-coordinate value X of the distribution areaThe value Y of the circumference and Y coordinate may satisfy the following formulas (1), (2):
x min -δ≤x≤x max +δ (1)
y min -δ≤y≤y max +δ (2)
where δ is a constant that can be determined by the wireless positioning error performance, e.g., δ may be equal to 1 meter; further, δ may be a step length from the t-1 th time to the current t-th time calculated based on the detection signal output from the inertial sensor.
In this embodiment, both the method for estimating the position of the object to be positioned based on the wireless signal at each time and the method for calculating the step length based on the detection signal output by the inertial sensor may refer to the prior art, and are not described herein again.
In this embodiment, the gap between adjacent time instants may be a period or time interval, e.g. 1 second, at which the mobile terminal is positioned.
In another embodiment, in step 201, the distribution area of the candidate position of the object to be positioned at the current time may be determined according to the method (b), that is, the distribution area of the candidate position may be determined according to the position of the object to be positioned calculated based on the posterior probability of each candidate position at the first predetermined time before the current time, that is, the position of the object to be positioned calculated by step 104 corresponding to the first predetermined time before the current time.
For example, the current time is the t-th time, the first predetermined time before the current time may be, for example, the (t- τ) th time, and the position of the object to be positioned at the (t- τ) th time calculated in step 104 may be
To be provided with
Centered on the step from the first predetermined time (i.e., the (t- τ) -th time) to the current time (the t-th time), the distribution area of the candidate positions can be determined.
In this embodiment, the step length from the first predetermined time to the current time may be calculated based on the detection signal output by the inertial sensor, and the specific method may refer to the prior art, which is not described herein again.
In this embodiment, the distribution area may also be determined in other manners, and this embodiment is not limited to the above two manners.
In step 202 of this embodiment, the distribution area determined in step 201 may be subjected to rasterization to form a plurality of grid dots, and the position of each grid dot is taken as each candidate position, and the size of the area represented by each grid dot may be, for example, α meters × β meters.
In the present embodiment, each candidate position
Can be expressed as
Respectively represent candidate positions
N is less than or equal to N, both N and N are natural numbers, and N represents the number of candidate positions.
Fig. 3 is a schematic diagram of each grid point determined based on the distribution area obtained in the method (a), and fig. 4 is a schematic diagram of each grid point determined based on the distribution area obtained in the method (b).
As shown in fig. 3, 301, 302, and 303 respectively indicate the positions of the object to be positioned estimated based on the wireless signal at the (t- τ) th time, the (t- τ +1) th time, and 304 indicates the grid point.
As shown in fig. 4, 401 represents the position of the object to be positioned calculated in step 104 at a first predetermined time before the current time, and 402 represents a grid point.
In this embodiment, the method shown in fig. 2 may further include step 203:
and step 203, correcting the distribution area of the candidate position by combining the map information.
In step 203, the map information may be compared with the approximate position at the current time, so as to correct the distribution area determined in step 201, thereby making the distribution area of the candidate position more accurate, for example, if the distribution area determined in step 201 is a square and has a larger area, and the approximate position at the current time is within a bar-shaped road area on the map, the distribution area of the square determined in step 201 may be corrected into a bar shape according to the information of the bar-shaped road area.
In this embodiment, the approximate position of the current time may be estimated in various ways, for example, according to the wireless signal of the current time, or according to the position of the object to be positioned calculated in step 104 at a second predetermined time before the current time, for example, the second predetermined time may be a time before the current time, that is, the (t-1) th time.
In this embodiment, according to step 101, at each time, the set of candidate positions may change, that is, the set of candidate positions dynamically changes with time, so that the set of candidate positions of this embodiment can more accurately reflect the current position of the object to be positioned, compared to the technical solution in which the set of candidate positions is fixed with respect to time.
In step 102 of the present embodiment, the method for calculating the step size may refer to the prior art. In this embodiment, the step size may be calculated using an empirical model based on a modulus of the acceleration signal in the detection signal output by the inertial sensor, for example, the moving distance of the object to be positioned in a period from a third predetermined time before the current time to the current time may be calculated, and the moving distance may be used as the step size corresponding to the current time, and the third predetermined time may be, for example, a time before the current time.
In step 102 of this embodiment, the heading estimation method of the object to be positioned may be different according to whether the posture of the mobile terminal changes.
Fig. 5 is a flowchart of the method for estimating heading in step 102, as shown in fig. 5, the method comprising:
In step 501 of this embodiment, if the attitude of the mobile terminal has not changed within a second predetermined time period before the current time, the heading change amount of the object to be positioned within the second predetermined time period may be calculated by using the detection signal output by the inertial sensor in the mobile terminal, and the heading of the object to be positioned at the current time may be calculated according to the heading change amount in combination with the wireless signal and/or the map information.
In this embodiment, the heading change amount in the second predetermined time period may be calculated according to an acceleration signal and an angular velocity signal in a detection signal output by the inertial sensor, for example, a specific calculation manner may refer to the prior art, and this embodiment is not described again.
In step 501 of this embodiment, a method for calculating the heading of the object to be located at the current time according to the heading variation and by combining the wireless signal and/or the map information may be, for example: and calculating the position of the object to be positioned at each moment of a second preset time period according to the wireless signal received by the movable terminal, fitting the position of the object to be positioned at each moment, wherein the direction obtained by the fitting is used as a reference course, and the reference course can be further corrected according to road information on a map so as to enable the corrected reference course to be along the road extending direction on the map, and the corrected reference course is used as a reference and is combined with course variation to determine the course at the current moment.
In addition, the above embodiments are only examples, and other methods may be adopted to obtain the reference heading and/or the corrected reference heading.
In this embodiment, as shown in fig. 5, the method for calculating the heading may further include:
In step 502, when the attitude of the mobile terminal changes, the detection signal output by the inertial sensor cannot accurately reflect the motion condition of the object to be positioned, and therefore, a predetermined candidate moving direction can be directly selected as the heading of the object to be positioned.
In this embodiment, the predetermined candidate moving direction may be: the direction obtained from the map information and the position at the third predetermined time before the current time, for example, the third predetermined time may be the (t-1) th time, and the position of the object to be positioned at the (t-1) th time calculated in step 104 may be
The position is
The map is located in the area of a certain strip road, so that the strip road can be located at the position
As the candidate movement direction, the extension direction (e.g., two directions) of (b) is set. When the predetermined candidate moving direction includes a plurality of directions, the plurality of directions may be selected.
In this embodiment, the predetermined candidate moving direction may be: n1 directions are preset in the two-dimensional plane, and the N1 directions may be 4 directions or 8 directions, for example.
In this embodiment, as shown in fig. 5, the method may further include step 503:
and 503, judging whether the posture of the movable terminal is changed in a second preset time period before the current time.
In step 503 of this embodiment, it may be determined whether the posture of the mobile terminal is changed according to a change of the acceleration signal and/or the angular velocity signal in the detection signal output by the inertial sensor within a predetermined time period. For example, a difference value of a mean value of the acceleration signal in a third predetermined period of time and a mean value in a fourth predetermined period of time before the current time may be calculated, and if the difference value is less than a threshold, it may be determined that the attitude of the movable terminal has not changed, and if the difference value is greater than or equal to the threshold, it may be determined that the attitude of the movable terminal has changed.
Fig. 6 is a flowchart of a method for calculating posterior probability of each candidate position according to the present embodiment, and as shown in fig. 6, the method may include:
and step 604, calculating the posterior probability of each candidate position at the current moment based on the continuous time hidden Markov model according to the initial probability vector, the probability vector and the candidate position transfer matrix.
In the present embodiment, in the description of the steps of fig. 6, the fifth predetermined period of time may be, for example, from the (t- τ) th 1 ) The time period from the time to the current t-th time may include (tau) 1 +1) time instants, each of which may be denoted as the (t- τ) th time instant 1 + k) time, where k is ≦ τ 1 K and τ 1 Are all integers.
In step 601 of the present embodiment, an initial probability vector P t-τ1 Can be expressed as
Wherein the content of the first and second substances,
can be used forIndicates that the nth candidate position is at the (t-tau) th position 1 ) The probability of the moment, i.e. the initial probability.
In the present embodiment, the initialization probability of each candidate position may be set to an equivalent probability, for example,
alternatively, the initialization probability for each candidate location may be calculated based on a distance-dependent probability model, e.g., the value calculated at step 104 at the (t- τ) th position 1 ) The position of the object to be positioned at the moment is
The probabilistic model may be associated with each candidate location
And the position
The distance dependent probability model of (a), for example, may be represented by the following equation (3):
the expression after the above expression (3) is normalized is the following expression (4):
wherein j is less than or equal to N, and j is a natural number.
In addition, in the present embodiment, the initial probability of each candidate position may be obtained in other manners.
In step 602 of this embodiment, the (t- τ) th step 1 Probability vector P at + k) time t-τ1+k Can be expressed as
Wherein the content of the first and second substances,
it can be expressed that the nth candidate position is at the (t- τ) th position 1 + k) probability of time instant.
In step 602 of this embodiment, each candidate position is at the (t- τ) th time within the fifth predetermined time period 1 The probability of + k) time may be calculated based on a distance-based probability model, where the distance may be each candidate location
A distance from a position calculated based on the wireless signal, which may be the (t- τ) th position calculated based on the wireless signal 1 Position l at time + k) w t-τ1+k Or, an average value of the positions at each time in the fifth predetermined period calculated based on the wireless signal
The probability model may be expressed as the following formula (5a) or (5 b):
the expression after the above formula (5a) or (5b) is normalized is the following formula (6):
in step 602 of this embodiment, other methods may be used to calculate the probability
For example, the probability may be calculated as the transient state transition probability in a conventional continuous-time hidden Markov model (CHMM)
Wherein for the nth candidate position
All candidate positions can be found at the (t-t) th position 1 + k) instant state transition probability, then finding other candidate position instant transition to the nth candidate position
Thereby calculating the probability of
The specific manner of calculation can be found in the prior art.
In step 603 of the present embodiment, the (t- τ) th time period within the fifth predetermined time period 1 + k) time, nth candidate position
To the m-th candidate position
Has a homogeneous transition probability of
In step 603, the method of calculating the homogeneous transition probability may be different according to whether the posture of the mobile terminal is changed. Fig. 7 is a schematic diagram of the method for calculating the homogeneous transition probability of the present embodiment, as shown in fig. 7, the method includes:
step 701, under the condition that the posture of the mobile terminal is not changed in the fifth preset time period, calculating an estimated position taking the nth candidate position as a starting point according to the course and the step length of the object to be positioned;
In step 701, in case that the posture of the mobile terminal is not changed for a fifth predetermined period of time, the (t- τ) th time may be calculated according to the method based on step 102 1 + k) heading θ of the object to be positioned at time t-τ1+k And step size v t-τ1+k Calculating the (t-t) th 1 + k) time with the nth candidate position
The estimated position as a starting point may be calculated based on a Pedestrian Dead Reckoning (PDR) algorithm, for example
For example, the calculation can be performed by the following formula (7)
Wherein v is t-τ1+k Is shown at the (t-t) 1 The step size at time + k) may be, for example, from the (t- τ) th time 1 From the + k-1) th time to the (t- τ) th time 1 + k) the moving distance of the object to be positioned at the moment;
is the (t- τ) th 1 + k-1) x-coordinate value of the estimated position at time,
is the (t- τ) th 1 + k-1) the y coordinate value of the estimated position at time.
In this embodiment, the direction of the connection line between the nth candidate position and the mth candidate position and the heading θ of the object to be positioned t-τ1+k The angle therebetween is delta theta.
In step 702, a function can be estimatedPosition of
And the m-th candidate position
Distance therebetween, and Δ θ to calculate transition probability
For example, the calculation may be performed according to the following formula (8):
where f (Δ θ) may be a continuously decreasing function of Δ θ, for example, f (Δ θ) may be modeled as a gaussian function of Δ θ.
In this embodiment, as shown in fig. 7, the method further includes:
In step 703 of the present embodiment, since the attitude of the mobile terminal changes during the fifth predetermined period of time, the estimated position is not calculated using the heading calculated based on the detection signal output from the inertial sensor, but is calculated using the predetermined candidate moving direction selected in step 502. For example, the estimated position may be calculated using the following equation (9) based on the PDR algorithm:
wherein, theta i Is the ith candidate moving directionDirection information, 1 ≦ i ≦ N1, i and N1 both being natural numbers, N1 being the number of predetermined candidate movement directions selected in step 502,
is the estimated position corresponding to the ith candidate direction of movement.
In step 704, various estimated locations may be selected
And m-th candidate position
Nearest estimated position
According to the estimated position and the m-th candidate position
Calculating transition probability of distance between
For example, the calculation can be performed using the following formula (10):
the expression after the above expression (10) is normalized is the following expression (11):
in the present embodiment, the length of time of the fifth predetermined period of time may be equal to the length of time of the second predetermined period of time, and thus the determination result as to whether the posture of the movable terminal has changed within the fifth predetermined period of time may be the same as the determination result as to whether the posture of the movable terminal has changed within the second predetermined period of time. Further, the time length of the fifth predetermined period of time may not be equal to the time length of the second predetermined period of time, whereby it can be judged whether or not the posture of the movable terminal has changed within the fifth predetermined period of time based on the same manner as in step 503.
In step 603 of the present embodiment, the (t- τ) th time period within the fifth predetermined time period 1 + k) time, transition probability matrix Q between candidate locations t-τ1+k Can be represented by the following formula (12):
in step 604 of this embodiment, the initial probability vector P obtained in steps 601, 602, and 603 may be used based on a continuous time hidden markov model (CHMM) t-τ1 Probability vector P at each time t-τ1+k And a transition probability matrix Q at each time instant t-τ1+k Calculating the posterior probability vector P of each candidate position at the current moment t For example, the calculation may be performed according to the following equation (13):
wherein, the first and the second end of the pipe are connected with each other,
P t ＝[P t 1 ，P t 2 ，...，P t N ]，P t 1 ，P t 2 ，...P t N representing the posterior probability of each candidate position, x and ii both represent the multiplication of the elements of two 1 x N vectors to form the corresponding 1 x N vector.
In step 104 of this embodiment, the position of the object to be located at the current time may be calculated according to the posterior probabilities of the candidate positions, for example, the posterior probability cases may be sorted from large to small, and the candidate positions corresponding to the top T posterior probabilities of the sorting may be weighted to obtain the position of the object to be located at the current time T, for example,the position of the object to be positioned at the current time t can be calculated according to the following equation (14)
Wherein r is more than or equal to 1 and less than or equal to T, r and T are natural numbers,
is the r-th a-posteriori probability of the first T a-posteriori probabilities,
is a posterior probability
Corresponding candidate position, w r Is a candidate position
The corresponding weight.
In addition, the embodiment may not be limited to this, and step 104 may also use other manners to calculate the position of the object to be located at the current time.
According to the embodiment, the posterior probability of each candidate position is calculated based on the continuous time hidden Markov model, so that the calculation can be performed by referring to the probability of the time before the current time, thereby improving the accuracy of the posterior probability calculation, and in the course of calculating the course and the transfer vector, under the condition that the posture of the movable terminal is changed, the calculation is not performed by using the detection signal output by the inertial sensor of the movable terminal, thereby reducing the calculation error; therefore, the embodiment can keep higher positioning precision under the condition of not limiting the posture of the movable terminal, and improves the use convenience of the movable terminal.
Example 2
Embodiment 2 of the present application provides a positioning apparatus, which corresponds to the positioning method of embodiment 1.
Fig. 8 is a schematic diagram of the positioning apparatus of the embodiment, and as shown in fig. 8, the positioning apparatus 800 may include:
a first determination unit 801 for determining a set of candidate positions of an object to be positioned at the current time;
a first calculation unit 802 for calculating a heading and a step length of the object to be positioned;
a second calculating unit 803, configured to calculate, based on a hidden markov model, a posterior probability corresponding to each candidate position in the set of candidate positions according to the heading and the step length; and
and a third calculating unit 804 for calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position.
Fig. 9 is a schematic diagram of the first determination section of the present embodiment, and as shown in fig. 9, the first determination section 801 may include:
a second determining part 901, configured to determine a distribution area of the candidate positions according to the position of the object to be positioned estimated based on the wireless signal within a first predetermined time period before the current time, or according to the position of the object to be positioned calculated at a first predetermined time before the current time;
a third determining section 902 for determining each of the candidate positions in the distribution area.
As shown in fig. 9, the first determination unit 801 further includes:
and a first correcting unit 903 for correcting the distribution area of the candidate position in conjunction with the map information.
Fig. 10 is a schematic diagram of the first calculating part of the present embodiment, and as shown in fig. 10, the first calculating part 802 includes a fourth calculating part 1001 for calculating a step length and a fifth calculating part 1002 for calculating a heading, wherein the fifth calculating part 1002 includes:
a sixth calculation unit 1003, configured to calculate, when the posture of the mobile terminal provided to the object to be positioned is not changed within a second predetermined time period before the current time, a heading change amount of the object to be positioned within the predetermined time period based on a detection signal output by an inertial sensor in the mobile terminal, and calculate, based on the heading change amount, a heading of the object to be positioned in combination with a wireless signal and/or map information;
a seventh calculation part 1004 that adopts a predetermined candidate moving direction as the heading of the object to be positioned in a case where the attitude of the movable terminal has changed within the second predetermined period of time.
Fig. 11 is a schematic diagram of the second calculation section of the present embodiment, and as shown in fig. 11, the second calculation section 803 includes:
an eighth calculation unit 1101 configured to calculate an initial probability of each candidate position in a fifth predetermined time period before the current time to obtain an initial probability vector;
a ninth calculation unit 1102 configured to calculate a probability of each candidate position at each time point within the fifth predetermined time period, to obtain a probability vector;
a tenth calculating unit 1103, configured to calculate transition probabilities between candidate positions at respective times within the fifth predetermined time period, so as to obtain a candidate position transition matrix;
an eleventh calculating unit 1104 that calculates a posterior probability of each candidate position at the current time based on a continuous-time hidden markov model from the initial probability vector, the probability vector, and the candidate position transition matrix.
In the present embodiment, the ninth calculation unit 1102 calculates the probability of each candidate position at each time based on a probability model of the distance between the position of the object to be positioned and each candidate position calculated based on the wireless signal.
Fig. 12 is a schematic diagram of a tenth calculation unit of the present embodiment, in which the tenth calculation unit 1103 includes:
a twelfth calculating part 1201 that calculates an estimated position starting from the nth candidate position, based on the heading and the step length of the object to be positioned, when the posture of the mobile terminal is not changed within the fifth predetermined period of time;
a thirteenth calculating unit 1202 for calculating the transition probability according to the distance between the estimated position and the mth candidate position and the included angle between the direction connecting the nth candidate position and the mth candidate position and the heading of the object to be positioned;
as shown in fig. 12, the tenth calculation unit 1103 further includes:
a fourteenth calculation unit 1203 that calculates, when the posture of the positioning terminal has changed in the fifth predetermined period of time, estimated positions corresponding to the predetermined candidate movement directions from the nth candidate position as a starting point, based on the predetermined candidate movement directions and the step length;
a fifteenth calculating section 1204 that calculates a transition probability based on the minimum distance between each of the estimated positions and the mth candidate position.
In this embodiment, the third calculation unit 804 weights the candidate positions corresponding to a predetermined number of posterior probabilities to obtain the position of the object to be positioned at the current time.
In this embodiment, for the detailed description of the above units, reference may be made to the description of the corresponding steps in embodiment 1, and the description of this embodiment will not be repeated.
According to the embodiment, the posterior probability of each candidate position is calculated based on the continuous time hidden Markov model, so that the calculation can be performed by referring to the probability of the time before the current time, thereby improving the accuracy of the posterior probability calculation, and in the course of calculating the course and the transfer vector, under the condition that the posture of the movable terminal is changed, the calculation is not performed by using the detection signal output by the inertial sensor of the movable terminal, thereby reducing the calculation error; therefore, the embodiment can keep higher positioning precision under the condition of not limiting the posture of the movable terminal, and improves the use convenience of the movable terminal.
Example 3
An embodiment 3 of the present application provides an electronic device, including: the positioning device as described in embodiment 2.
Fig. 13 is a schematic diagram of a configuration of an electronic device according to embodiment 3 of the present application. As shown in fig. 13, electronic device 1300 may include: a Central Processing Unit (CPU)1301 and a memory 1302; the memory 1302 is coupled to the central processor 1301. Wherein the memory 1302 can store various data; a program for positioning is also stored and executed under the control of the central processor 1301.
In one embodiment, the functions in the positioning device may be integrated into the central processor 1301.
The central processor 1301 may be configured to:
determining a set of candidate positions of an object to be positioned at the current moment;
calculating the course and the step length of the object to be positioned;
calculating the posterior probability corresponding to each candidate position in the set of candidate positions based on a continuous time hidden Markov model according to the course and the step length; and
and calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position.
In this embodiment, the central processor 1301 may be further configured to:
determining a distribution area of the candidate position according to the position of the object to be positioned estimated based on the wireless signal in a first predetermined time period before the current time, or according to the position of the object to be positioned calculated at a first predetermined time before the current time; determining each of the candidate locations in the distribution area.
In this embodiment, the central processor 1301 may be further configured to:
and correcting the distribution area of the candidate position by combining the map information.
In this embodiment, the central processor 1301 may be further configured to:
in a second preset time period before the current time, under the condition that the posture of a movable terminal arranged on the object to be positioned is not changed, calculating the course variation of the object to be positioned in the preset time period according to a detection signal output by an inertial sensor in the movable terminal, and calculating the course of the object to be positioned according to the course variation and by combining a wireless signal and/or map information;
and adopting a preset candidate moving direction as the course of the object to be positioned under the condition that the posture of the movable terminal is changed in the second preset time period.
In this embodiment, the central processor 1301 may be further configured to:
calculating the initial probability of each candidate position in a fifth preset time period before the current moment to obtain an initial probability vector;
calculating the probability of each candidate position at each moment in the fifth preset time period to obtain a probability vector;
calculating the transition probability among the candidate positions at each moment in the fifth preset time period to obtain a candidate position transition matrix;
and calculating the posterior probability of each candidate position at the current moment based on a continuous time hidden Markov model according to the initial probability vector, the probability vector and the candidate position transfer matrix.
In this embodiment, the central processor 1301 may be further configured to:
the probability of each candidate position at each time is calculated based on a probability model of the distance between the position of the object to be positioned and each candidate position calculated based on the wireless signal.
In this embodiment, the central processor 1301 may be further configured to:
under the condition that the posture of the mobile terminal is not changed within the fifth preset time period, calculating an estimated position taking the nth candidate position as a starting point according to the course and the step length of the object to be positioned;
calculating the transition probability according to the distance between the estimated position and the mth candidate position and the included angle between the connecting line direction of the nth candidate position and the mth candidate position and the course of the object to be positioned;
under the condition that the posture of the positioning terminal is changed in the fifth preset time period, calculating estimated positions which respectively correspond to the preset candidate moving directions and take the nth candidate position as a starting point according to the preset candidate moving directions and the step length;
and calculating the transition probability according to the minimum distance between each estimated position and the mth candidate position.
In this embodiment, the central processor 1301 may be further configured to:
and weighting the candidate positions corresponding to the posterior probabilities of the front preset number to obtain the position of the object to be positioned at the current moment.
Further, as shown in fig. 13, the electronic device 1300 may further include: an input-output unit 1303, a display unit 1304, and the like; the functions of the above components are similar to those of the prior art, and are not described in detail here. It is worthy to note that electronic device 1300 also need not include all of the components shown in FIG. 13; furthermore, the electronic device 1300 may also include components not shown in fig. 13, which may be referred to in the prior art.
Embodiments of the present application further provide a computer-readable program, where when the program is executed in a positioning apparatus or an electronic device, the program causes the positioning apparatus or the electronic device to execute the positioning method described in embodiment 1.
An embodiment of the present application further provides a storage medium storing a computer-readable program, where the storage medium stores the computer-readable program, and the computer-readable program enables a positioning apparatus or an electronic device to execute the positioning method described in embodiment 2.
The measurement devices described in connection with the embodiments of the invention may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. For example, one or more of the functional block diagrams and/or one or more combinations of the functional block diagrams illustrated in fig. 8, 9, 10, 11, and 12 may correspond to respective software modules of a computer program flow or may correspond to respective hardware modules. These software modules may correspond to the respective steps shown in embodiment 1. These hardware modules may be implemented, for example, by solidifying these software modules using a Field Programmable Gate Array (FPGA).
A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium; or the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The software module may be stored in the memory of the mobile terminal or in a memory card that is insertable into the mobile terminal. For example, if the electronic device employs a MEGA-SIM card with a larger capacity or a flash memory device with a larger capacity, the software module may be stored in the MEGA-SIM card or the flash memory device with a larger capacity.
One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 8, 9, 10, 11, 12 may be implemented as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any suitable combination thereof, for performing the functions described herein. One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 8, 9, 10, 11, 12 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.
The present application has been described in conjunction with specific embodiments, but it should be understood that these descriptions are exemplary and not intended to limit the scope of the present application. Various modifications and adaptations of the present application may occur to those skilled in the art based on the teachings herein and are within the scope of the present application.
With respect to the embodiments including the above embodiments, the following remarks are also disclosed:
1. a positioning device, comprising:
a first determination unit configured to determine a set of candidate positions of an object to be positioned at a current time;
a first calculation part for calculating the course and step length of the object to be positioned;
a second calculating part, which is used for calculating the posterior probability corresponding to each candidate position in the candidate position set based on a continuous time hidden Markov model according to the course and the step length; and
and a third calculation unit that calculates the position of the object to be positioned based on the posterior probabilities corresponding to the respective candidate positions.
2. The apparatus according to supplementary note 1, wherein the first determination section includes:
a second determination section configured to determine a distribution area of the candidate positions from a position of the object to be positioned estimated based on the wireless signal within a first predetermined time period before a current time or from a position of the object to be positioned calculated at a first predetermined time before the current time;
a third determination section for determining each of the candidate positions in the distribution area.
3. The apparatus according to supplementary note 2, wherein the first determination section further includes:
and a first correcting unit for correcting the distribution area of the candidate position in association with map information.
4. The apparatus according to supplementary note 1, wherein the first calculation section includes a fourth calculation section for calculating the step length and a fifth calculation section for calculating the heading, wherein the fifth calculation section includes:
a sixth calculation unit that calculates a course variation of the object to be positioned within a second predetermined time period before the current time, based on a detection signal output from an inertial sensor in the mobile terminal, when the posture of the mobile terminal provided to the object to be positioned is not changed, and calculates the course of the object to be positioned based on the course variation in combination with a wireless signal and/or map information;
a seventh calculation section that adopts a predetermined candidate movement direction as the heading of the object to be positioned, in a case where the attitude of the movable terminal has changed within the second predetermined period of time.
5. The apparatus according to supplementary note 1, wherein the second calculation section includes:
an eighth calculating part, configured to calculate an initial probability of each candidate position in a fifth predetermined time period before the current time to obtain an initial probability vector;
a ninth calculation unit configured to calculate a probability of each candidate position at each time in the fifth predetermined time period, and obtain a probability vector;
a tenth calculation unit configured to calculate transition probabilities between the candidate positions at each time within the fifth predetermined time period, and obtain a candidate position transition matrix;
an eleventh calculating unit that calculates a posterior probability of each candidate position at the current time based on a continuous-time hidden markov model based on the initial probability vector, the probability vector, and the candidate position transition matrix.
6. The apparatus according to supplementary note 5, wherein,
the ninth calculation unit calculates the probability of each candidate position at each time based on a probability model of the distance between the position of the object to be positioned and each candidate position calculated based on the wireless signal.
7. The apparatus according to supplementary note 5, wherein the tenth calculation section includes:
a twelfth calculating section that calculates an estimated position starting from the nth candidate position, based on the heading and the step length of the object to be positioned, when the posture of the mobile terminal has not changed within the fifth predetermined period of time;
a thirteenth calculating part, which calculates the transition probability according to the distance between the estimated position and the mth candidate position and the included angle between the connecting line direction of the nth candidate position and the mth candidate position and the heading of the object to be positioned;
8. the apparatus according to supplementary note 5, wherein the tenth calculation section includes:
a fourteenth calculation unit that calculates, when the orientation of the positioning terminal has changed within the fifth predetermined period of time, estimated positions corresponding to the predetermined candidate movement directions from the nth candidate position as a starting point, based on the predetermined candidate movement directions and the step length;
a fifteenth calculating section that calculates a transition probability based on a minimum distance between each of the estimated positions and the mth candidate position.
9. The apparatus according to supplementary note 1, wherein the third calculation unit obtains the position of the object to be positioned at the current time by weighting candidate positions corresponding to a predetermined number of posterior probabilities.
10. An electronic apparatus having the positioning device according to any one of supplementary notes 1 to 9.
11. A method of positioning, comprising:
determining a set of candidate positions of an object to be positioned at the current moment;
calculating the course and the step length of the object to be positioned;
calculating the posterior probability corresponding to each candidate position in the set of candidate positions based on a continuous time hidden Markov model according to the course and the step length; and
and calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position.
12. The method of supplementary note 11, wherein determining the set of candidate positions of the object to be located at the current time comprises:
determining a distribution area of the candidate position according to the position of the object to be positioned estimated based on the wireless signal in a first predetermined time period before the current time, or according to the position of the object to be positioned calculated at a first predetermined time before the current time;
determining each of the candidate locations in the distribution area.
13. The method of supplementary note 12, wherein determining the set of candidate positions of the object to be located at the current time further comprises:
and correcting the distribution area of the candidate position by combining the map information.
14. The method of supplementary note 12, wherein calculating the heading of the object to be located includes:
in the case that the posture of the movable terminal arranged on the object to be positioned is not changed in a second preset time period before the current time,
calculating the course variation of the object to be positioned in the preset time period according to the detection signal output by the inertial sensor in the movable terminal, and calculating the course of the object to be positioned according to the course variation and by combining a wireless signal and/or map information;
in the case where the posture of the movable terminal is changed within the second predetermined period of time,
and adopting a preset candidate moving direction as the course of the object to be positioned.
15. The method of supplementary note 11, wherein calculating a posterior probability for each candidate location comprises:
calculating the initial probability of each candidate position in a fifth preset time period before the current moment to obtain an initial probability vector;
calculating the probability of each candidate position at each moment in the fifth preset time period to obtain a probability vector;
calculating the transition probability among the candidate positions at each moment in the fifth preset time period to obtain a candidate position transition matrix;
and calculating the posterior probability of each candidate position at the current moment based on a continuous time hidden Markov model according to the initial probability vector, the probability vector and the candidate position transition matrix.
16. The method of supplementary note 15, wherein the calculating of the probability of each candidate position at each time comprises:
the probability of each candidate position at each time is calculated based on a probability model of the distance between the position of the object to be positioned and each candidate position calculated based on the wireless signal.
17. The method of supplementary note 15, wherein calculating transition probabilities between the candidate positions at respective times within the fifth predetermined period of time includes:
under the condition that the posture of the mobile terminal is not changed within the fifth preset time period, calculating an estimated position taking the nth candidate position as a starting point according to the course and the step length of the object to be positioned;
calculating the transition probability according to the distance between the estimated position and the mth candidate position and the included angle between the connecting line direction of the nth candidate position and the mth candidate position and the course of the object to be positioned;
under the condition that the posture of the positioning terminal is changed in the fifth preset time period, calculating estimated positions which respectively correspond to the preset candidate moving directions and take the nth candidate position as a starting point according to the preset candidate moving directions and the step length;
and calculating the transition probability according to the minimum distance between each estimated position and the mth candidate position.
18. The method according to supplementary note 11, wherein calculating the position of the object to be located based on the posterior probability of each candidate state comprises:
and weighting the candidate positions corresponding to the posterior probabilities of the front preset number to obtain the position of the object to be positioned at the current moment.
Claims (9)
1. A positioning device, comprising:
a first determination unit configured to determine a set of candidate positions of an object to be positioned at a current time;
a first calculation part for calculating the course and step length of the object to be positioned;
a second calculating part, which is used for calculating the posterior probability corresponding to each candidate position in the candidate position set based on a continuous time hidden Markov model according to the course and the step length; and
a third calculating unit that calculates the position of the object to be positioned based on the posterior probability corresponding to each candidate position,
wherein the first calculation part includes a fourth calculation part for calculating the step length and a fifth calculation part for calculating the heading, wherein the fifth calculation part includes:
a sixth calculation unit that calculates a course variation of the object to be positioned within a second predetermined time period before the current time, based on a detection signal output from an inertial sensor in the mobile terminal, when the posture of the mobile terminal provided to the object to be positioned is not changed, and calculates the course of the object to be positioned based on the course variation in combination with a wireless signal and/or map information;
a seventh calculation section that adopts a predetermined candidate movement direction as the heading of the object to be positioned in a case where the attitude of the movable terminal has changed within the second predetermined period of time.
2. The apparatus of claim 1, wherein the first determining portion comprises:
a second determination section for determining a distribution area of the candidate positions from a position of the object to be positioned estimated based on the wireless signal within a first predetermined time period before a current time or from a position of the object to be positioned calculated at a first predetermined time before the current time;
a third determining section for determining each of the candidate positions in the distribution area.
3. The apparatus of claim 2, wherein the first determination section further comprises:
and a first correcting unit for correcting the distribution area of the candidate position in association with map information.
4. The apparatus as set forth in claim 1, wherein the second calculation section includes:
an eighth calculating part, configured to calculate an initial probability of each candidate position in a fifth predetermined time period before the current time to obtain an initial probability vector;
a ninth calculation unit configured to calculate a probability of each candidate position at each time in the fifth predetermined time period, and obtain a probability vector;
a tenth calculation unit configured to calculate transition probabilities between the candidate positions at each time within the fifth predetermined time period, and obtain a candidate position transition matrix;
an eleventh calculating unit that calculates a posterior probability of each candidate position at the current time based on a continuous-time hidden markov model from the initial probability vector, the probability vector, and the candidate position transition matrix.
5. The apparatus of claim 4, wherein,
the ninth calculation unit calculates the probability of each candidate position at each time based on a probability model of the distance between the position of the object to be positioned and each candidate position calculated based on the wireless signal.
6. The apparatus according to claim 4, wherein the tenth calculation section includes:
a twelfth calculation unit that calculates an estimated position starting from the nth candidate position, based on the heading and the step length of the object to be positioned, when the posture of the mobile terminal provided to the object to be positioned is not changed within the fifth predetermined period of time;
a thirteenth calculating part, which calculates the transition probability according to the distance between the estimated position and the mth candidate position and the included angle between the connecting line direction of the nth candidate position and the mth candidate position and the heading of the object to be positioned;
a fourteenth calculation unit that calculates, when the orientation of the positioning terminal has changed within the fifth predetermined period of time, estimated positions corresponding to the predetermined candidate movement directions from the nth candidate position as a starting point, based on the predetermined candidate movement directions and the step length;
a fifteenth calculation unit that calculates a transition probability based on a minimum distance between each of the estimated positions and the mth candidate position.
7. The apparatus as claimed in claim 1, wherein the third calculating portion weights the candidate positions corresponding to a predetermined number of posterior probabilities to obtain the position of the object to be located at the current time.
8. An electronic device having a positioning apparatus as claimed in any one of claims 1-7.
9. A method of positioning, comprising:
determining a set of candidate positions of an object to be positioned at the current moment;
calculating the course and the step length of the object to be positioned;
calculating the posterior probability corresponding to each candidate position in the set of candidate positions based on a continuous time hidden Markov model according to the course and the step length; and
calculating the position of the object to be positioned based on the posterior probability corresponding to each candidate position,
wherein calculating the heading comprises:
in a second preset time period before the current time, under the condition that the posture of a movable terminal arranged on the object to be positioned is not changed, calculating the course variation of the object to be positioned in the preset time period according to a detection signal output by an inertial sensor in the movable terminal, and calculating the course of the object to be positioned according to the course variation and by combining a wireless signal and/or map information;
and adopting a preset candidate moving direction as the course of the object to be positioned under the condition that the posture of the movable terminal is changed in the second preset time period.
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| US7970575B2 (en) * | 2007-04-20 | 2011-06-28 | Industrial Technology Research Institute | Method and apparatus for determining accuracy of the estimated location for a target in a wireless system |
| WO2011091549A1 (en) * | 2010-01-27 | 2011-08-04 | Motorola Mobility, Inc. | Real-time location estimation of a mobile station in a wireless communication system |
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| JP2012008659A (en) * | 2010-06-22 | 2012-01-12 | Sony Corp | Data processing device, data processing method, and program |
| US8457880B1 (en) * | 2012-11-28 | 2013-06-04 | Cambridge Mobile Telematics | Telematics using personal mobile devices |
| US20150198443A1 (en) * | 2014-01-10 | 2015-07-16 | Alcatel-Lucent Usa Inc. | Localization activity classification systems and methods |
| EP3108207B1 (en) * | 2014-02-17 | 2020-06-24 | Oxford University Innovation Limited | Determining the position of a mobile device in a geographical area |
| CN104900059B (en) * | 2015-05-26 | 2017-04-12 | 大连理工大学 | Method for enhancing cell phone base station positioning precision |
| JP6597797B2 (en) * | 2015-12-24 | 2019-10-30 | 富士通株式会社 | Information processing system, information processing program, and information processing method |
| CN106028450B (en) * | 2016-08-05 | 2019-12-31 | 襄阳翠鸟视图科技有限公司 | Indoor positioning method and equipment |
| CN106595680A (en) * | 2016-12-15 | 2017-04-26 | 福州大学 | Vehicle GPS data map matching method based on hidden markov model |
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