CN109855621B - Combined indoor pedestrian navigation system and method based on UWB and SINS - Google Patents
Combined indoor pedestrian navigation system and method based on UWB and SINS Download PDFInfo
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Abstract
The invention discloses a combined indoor pedestrian navigation system and method based on UWB and SINS, comprising an inertial measurement unit, a navigation resolving module, a zero-speed detection unit, a UWB wireless positioning unit, a magnetometer and a federal kalman filtering unit, wherein the federal kalman filtering unit performs data fusion on output signals of the navigation resolving module, the zero-speed detection unit, the UWB wireless positioning unit and the magnetometer, obtains error signals of human motion acceleration, course angle and position information, and sends the error signals to the inertial measurement unit for correction. According to the invention, a zero-speed correction technology is introduced on a traditional strapdown algorithm for detecting zero-speed moment, a position course angle is provided by utilizing ultra-wideband when entering an ultra-wideband signal area, a course angle is provided by utilizing magnetometer in a non-ultra-wideband area, and zero-speed, position and course angle information is fused through federal kalman filtering, so that the speed, position and course of a navigation system are corrected, and the positioning of indoor personnel is realized.
Description
Technical Field
The invention belongs to the field, and particularly relates to a combined indoor pedestrian navigation system and method based on UWB and SINS.
Background
Pedestrian navigation began in the eighth and nineties of the 20 th century, developed later and at a slower rate, and was not developed until after the U.S. federal communications commission published the positioning needs of E-911 in 1996. The pedestrian navigation system is mainly used for tracking the position information of the individual in real time and providing position service, can provide service for soldiers and rescue personnel, and provides higher-level security for high-risk staff.
The GPS/LBS positioning technology is mature and is successfully applied to vehicle-mounted equipment, mobile phones and other equipment. However, in indoor or signal severely blocked locations, the Global Positioning System (GPS) or the spatial location service (LBS) cannot effectively locate, and for navigation devices that rely heavily on both navigation techniques, it cannot continue to be used. With the development of computer and communication technologies, wireless Radio Frequency Identification (RFID), wireless fidelity (WIFI), ultra Wideband (UWB) technology and the like are widely applied to the indoor positioning field. The ultra-wideband technology uses pulses with the width of nanosecond level as communication signals, has very high time resolution, can obtain positioning accuracy of centimeter level, has certain multipath resistance and penetrability, and is suitable for high-accuracy indoor navigation positioning service. However, UWB positioning is accomplished through ranging or direction finding, and in a non-line-of-sight environment, due to shielding by an obstacle, pulse signals are reflected, which can reduce the ranging accuracy of UWB and greatly affect the positioning accuracy, and the analysis is as follows:
ultra wideband technology is a new type of wireless communication technology. The signal has bandwidth of GHz magnitude by directly modulating impulse with steep rising and falling time. The positioning is performed by using a time flight method, the distance is calculated by measuring the pulse flight time difference, and when a shielding object appears, the pulse propagation speed is influenced; let the pulse flight distance be S
S＝c·Δt
Where c is the speed of light and Δt is time; since the time Δt is in error, c is extremely large, and S is seriously affected. Therefore, the wild value often appears in the ranging process, and the ranging error is caused. Therefore, the inertial navigation system and the ultra-wideband positioning system cannot be independently used as a positioning system.
The Micro-Electro-Mechanical System (MEMS) based strapdown inertial navigation system (strap-down inertial navigation System) can provide effective supplement for UWB positioning, has the functions of short-time precision, high anti-interference performance and short-time autonomous positioning, is based on the navigation system of an integral working mode, can rapidly accumulate positioning errors along with the time, can not be used even if the positioning errors are too large, and cannot become a true navigation system, and is specifically analyzed as follows:
the inertial navigation basic formula:
wherein ,vn For the speed of the carrier body,
differential of speed, ++>
Converting matrix for carrier coordinate system (b system) to navigation coordinate system (n system), f b Projection of acceleration in b-series,>
is God's acceleration>
Is the centripetal acceleration g n Gravitational acceleration. Considering the practical situation of pedestrian navigation, the influence of the God acceleration and the centripetal acceleration is small, and is approximately as follows:
the actual output of the accelerometer is designed as
Is error, gyro actual output is +.>
wherein />
Is the theoretical output of the gyroscope epsilon b For a constant error of the gyro, the speed and position are given by the following formulas:
in the formula ,
speed at present moment->
Is the position at the current moment, t is the time; the error of the addition table is contained in->
Wherein the gyro error is contained in->
In (a), over time, is at a premium>
And epsilon b The constant calculation errors are accumulated at the growth rate of the power of 2, and the long-time positioning errors are too large to use.
Disclosure of Invention
Aiming at the problems, the invention provides a combined indoor pedestrian navigation system and method based on UWB and SINS, which introduces a zero-speed correction technology on the traditional strapdown algorithm for detecting zero-speed moment, provides a position course angle by utilizing ultra-wideband when entering an ultra-wideband signal area, corrects the speed, position and course of the navigation system by fusion of the zero-speed, position and course angle through Federal kalman filtering, and realizes the indoor personnel positioning function.
The technical aim is achieved, and the technical effects are achieved by the following technical scheme:
in a first aspect, the present invention provides a combined indoor pedestrian navigation system based on UWB and SINS, comprising:
the SINS comprises an inertial measurement unit and a navigation resolving module which are connected, wherein the inertial measurement unit is used for acquiring the motion acceleration and the rotation angular velocity of a human body; the navigation resolving module receives and calculates course angle and position information based on the human body movement acceleration and the rotation angular velocity;
the input end of the zero-speed detection unit is connected with the output end of the inertial measurement unit and is used for receiving the human body motion acceleration and the rotation angular velocity output by the inertial measurement unit, and performing zero-speed judgment based on the human body motion acceleration and the rotation angular velocity to obtain a zero-speed vector;
the UWB wireless positioning unit is used for outputting course angle and position information;
a magnetometer for outputting a heading angle;
the input end of the Federal kalman filtering unit is respectively connected with the output ends of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer, and carries out data fusion on the output signals of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer to obtain error signals of human body movement speed, course angle and position information, and the error signals are fed back to the navigation resolving module to carry out speed, course and position correction.
Preferably, the zero-speed detection unit comprises a motion mode judgment module, a generalized likelihood ratio detection module and a zero-speed judgment module;
the motion mode judging module is used for judging the motion mode according to the human body motion acceleration output by the inertia measuring unit;
the generalized likelihood ratio checking module is used for calculating generalized likelihood ratio based on human motion acceleration and rotation angular velocity output by the inertia measuring unit;
the zero speed judging module is used for carrying out zero speed judgment according to the generalized likelihood ratio output by the generalized likelihood ratio checking module and the experience threshold value under each motion mode to obtain a zero speed vector.
Preferably, the calculation formula of the generalized likelihood ratio is specifically:
wherein ,
human motion acceleration representing accelerometer output in inertial measurement unit, +.>
The rotation angular velocity of the gyroscope output in the inertial measurement unit is represented, and I.I. I. I represents the modulus value of the vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope in inertial measurement unit, respectively, +.>
Expressed in window period omega n The average of the internal accelerometer data, N, represents the window length and g represents the local gravitational acceleration vector.
Preferably, the zero speed judgment is performed according to the generalized likelihood ratio output by the generalized likelihood ratio detection module and the empirical threshold under each motion mode by adopting a judgment formula as follows:
Th i for the empirical threshold in motion pattern i, zero_flag represents a zero speed flag, a value of 1 represents a speed of zero speed, and a value of 0 represents a non-zero speed.
Preferably, the federal kalman filter comprises a speed sub-filter, a course sub-filter, a gesture sub-filter and a main filter, wherein the data transmission ends of the speed sub-filter, the course sub-filter and the gesture sub-filter are all connected with the data transmission end of the main filter, the input end of the speed sub-filter is respectively connected with the output ends of the navigation resolving module and the zero speed detecting unit, the input end of the course sub-filter is respectively connected with the output ends of the navigation resolving module and the UWB wireless positioning unit, and the input end of the gesture sub-filter is respectively connected with the output ends of the navigation resolving module and the magnetometer.
In a second aspect, the present invention provides a combined indoor pedestrian navigation method based on UWB and SINS, comprising the steps of:
(1) Acquiring human body movement acceleration and rotation angular velocity by using an inertial measurement unit, and calculating course angle and position information based on the human body movement acceleration and rotation angular velocity by using a navigation resolving module;
(2) The zero-speed detection unit is utilized to carry out zero-speed judgment based on the human motion acceleration and the rotation angular velocity output by the inertia measurement unit, and a zero-speed vector is obtained;
(3) Outputting human body position information and course angle by using the UWB wireless positioning unit;
(4) And carrying out data fusion on the output signals of the navigation resolving module, the zero-speed detection unit, the UWB wireless positioning unit and the magnetometer by utilizing a federal Kalman filter to obtain error signals of the human body movement speed, the course angle and the position information, and feeding back the error signals to the navigation resolving module to correct the speed, the course and the position.
Preferably, the zero speed detection unit is configured to perform zero speed judgment based on the human motion acceleration and the rotation angular velocity output by the inertial measurement unit, and obtain a zero speed vector, and specifically includes the following steps:
judging a movement mode according to the human movement acceleration output by the inertia measurement unit;
calculating a generalized likelihood ratio based on the human motion acceleration and the rotation angular velocity output by the inertia measurement unit;
and carrying out zero speed judgment according to the generalized likelihood ratio and the empirical threshold under each motion mode to obtain a zero speed vector.
Preferably, the calculation formula of the generalized likelihood ratio is specifically:
wherein ,
human motion acceleration representing accelerometer output in inertial measurement unit, +.>
The rotation angular velocity of the gyroscope output in the inertial measurement unit is represented, and I.I. I. I represents the modulus value of the vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope in inertial measurement unit, respectively, +.>
Expressed in window period omega n An average value of the internal accelerometer data, N represents a window length, g represents a local gravitational acceleration vector;
the judging formula adopted by the zero speed judgment is as follows:
Th i for the empirical threshold in motion pattern i, zero_flag represents a zero speed flag, a value of 1 represents a speed of zero speed, and a value of 0 represents a non-zero speed.
Preferably, the step (4) comprises the following sub-steps:
and (4.1) carrying out speed fusion on the zero-speed signal obtained by the zero-speed detection unit and the navigation resolving speed by using a speed sub-filter:
(4.2) utilizing a course sub-filter to fuse the navigation resolving module with the position and course measurement output by the UWB wireless positioning unit;
(4.3) carrying out course fusion on the course angle output by the navigation resolving module and the magnetometer by utilizing a gesture sub-filter;
(4.4) initial estimation covariance matrix P of velocity sub-filter, heading sub-filter, attitude sub-filter and main filter using upper bound variance technique i,0 Process noise covariance matrix Q i,0 Setting an initial estimated covariance matrix P of a Federal kalman filter g,0 Sum process noise covariance matrix Q g,0 A kind of electronic device
Multiple times, ensuring that each sub-filter is not related to each other, beta i The principle of conservation of information is satisfied;
(4.5) respectively and independently updating the speed sub-filter, the heading sub-filter and the attitude sub-filter in time;
(4.6) respectively measuring and updating the speed sub-filter, the heading sub-filter and the attitude sub-filter;
(4.7) updating the time of the main filter;
(4.8) fusing the estimation information of each sub-filter and the main filter into new global state estimation information;
(4.9) New global State estimation information to be obtained, error covariance matrix P g Public system noise matrix Q g And resetting each sub-filter and the main filter according to a set rule, and simultaneously transmitting the obtained new global state estimation information to an inertial measurement unit for correcting the speed, the course angle and the position.
Preferably, the set rule is:
where k represents the k time, i represents the ith filter,
represents the state estimate of the public system at time k, < >>
Representing the state estimate, P, of the ith filter g,k 、Q g,k Process covariance matrix and noise covariance matrix representing k-moment public system, P i,k 、Q i,k Representing the ith filter process covariance matrix and the noise covariance matrix at time k.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the indoor pedestrian navigation positioning requirement independent of GPS or LBS, the invention discloses an indoor pedestrian navigation device and method based on the combination of an Inertial Measurement Unit (IMU) and Ultra Wideband (UWB). The system consists of an MEMS inertial measurement unit and an ultra wideband positioning system, wherein the MEMS is fixedly connected with the vamp of a pedestrian, and the UWB is fixed at the shoulder position; the indoor pedestrian navigation algorithm introduces a zero-speed correction technology on the traditional strapdown algorithm for detecting zero-speed moment, provides a position course angle by utilizing ultra-wideband when entering an ultra-wideband signal area, provides a course angle by utilizing magnetometer in a non-ultra-wideband area, corrects system speed, position and course by integrating zero-speed, position and course angle through federal kalman filtering, and realizes the indoor personnel positioning function.
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FIG. 1 is a schematic diagram of an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following 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 principle of application of the invention is described in detail below with reference to the accompanying drawings.
Example 1
The embodiment of the invention provides a combined indoor pedestrian navigation device based on UWB and SINS, as shown in figures 1-2, which specifically comprises:
the SINS comprises an inertial measurement unit and a navigation resolving module which are connected, wherein the inertial measurement unit is used for acquiring the motion acceleration and the rotation angular velocity of a human body; the navigation resolving module receives and calculates course angle and position information based on the human body movement acceleration and the rotation angular velocity;
the input end of the zero-speed detection unit is connected with the output end of the inertial measurement unit and is used for receiving the human body movement acceleration and the rotation angular velocity output by the inertial measurement unit and carrying out zero-speed judgment based on the human body movement acceleration and the rotation angular velocity; in a specific embodiment of the present invention, the zero-speed detection unit includes a motion mode judgment module, a generalized likelihood ratio detection module, and a zero-speed judgment module, where:
the motion mode judging module is used for judging the motion mode according to the human body motion acceleration output by the inertia measuring unit; the working process of the motion mode judging module is as follows:
the x-axis of the inertial measurement unit in the embodiment of the invention is taken as the forward axis of the walking direction of the human body, the speed of the human body movement is judged by calculating the average acceleration of the x-axis based on a time window, and the average acceleration of the x-axis at the k moment is set as follows:
setting two threshold values Thv according to empirical values 1 ,Thv 2 Divided into three motion speed modes mod 1 ～mod 3 Representing slow, medium and fast respectively; the specific partitioning method is as follows:
mod i representing the movement pattern i.
The generalized likelihood ratio test module comprises the following working processes:
establishing generalized likelihood ratio test model
Defining the output of the inertia measurement unit at the moment k as:
wherein ,
representing the triaxial output of the accelerometer in the inertial measurement unit at time k>
Three-axis output of gyroscope in inertial measurement unit at k moment is expressed in m/s 2 、rad/s。
Setting the output quantity of the inertial measurement unit to continuously acquire and output N times (window period) from the moment N to obtain a group of continuous measurement values { y } k |k∈[n,n-N+1]And k is a positive integer. The zero-speed detection method based on the generalized likelihood ratio statistics constructs generalized likelihood ratio statistics values by outputting data to an inertial measurement unit with the length of N, and defines a generalized likelihood ratio statistical model as follows:
wherein ,
representing accelerometer output values, +.>
Representing a gyro output value, wherein I I.I represents a modulus value of a vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope, respectively,/>
Expressed in window period omega n The average of the internal accelerometer data, N, represents the window length and g represents the local gravitational acceleration vector.
Th i For the empirical threshold value in the motion mode i, the zero_flag is a zero speed flag, the value of zero_flag is 1, the speed is zero, the value of zero_flag is 0, the speed is non-zero, and the zero speed can be judged more accurately by setting different thresholds according to different motion speeds.
The UWB wireless positioning unit is used for outputting human body heading information and position information; the UWB wireless positioning unit comprises a base station and a tag, and only plane conditions are considered for convenience of installation; the UWB wireless positioning unit in the embodiment of the invention comprises three base stations and a tag; when the UWB wireless positioning unit works, the tag can output the distance from the tag to each base station, and when the position of each base station is known, the positioning work can be completed by utilizing a three-point positioning mode; let the positions of the three base stations A, B, C be (x) a ,y a ),(x b ,y b ),(x c ,y c ) The tag is output to three base stations at the position d a ,d b ,d c The coordinates of the tag may be expressed as:
in the ultra-wideband signal, the position provided by the UWB wireless positioning unit is relatively accurate, the output course angle is more stable relative to the magnetometer, the course angle is determined by utilizing a mode of determining a straight line by two points, and the calculation equation of the course angle is shown as a formula (7):
Y n represents the vertical axis coordinate, X, at time n n A horizontal axis coordinate at the time n;
the magnetometer is used for outputting heading information;
the input end of the Federal kalman filtering unit is respectively connected with the output ends of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer, and the output signals of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer are subjected to data fusion to obtain error signals of human body movement speed, course angle and position information, and the error signals are fed back to the navigation resolving module to correct the speed, the course and the position; specifically, the federal kalman filter comprises a speed sub-filter, a course sub-filter, a gesture sub-filter and a main filter, wherein the data transmission ends of the speed sub-filter, the course sub-filter and the gesture sub-filter are all connected with the data transmission end of the main filter, the input end of the speed sub-filter is respectively connected with the output ends of the navigation resolving module and the zero speed detecting unit, the input end of the course sub-filter is respectively connected with the output ends of the navigation resolving module and the UWB wireless positioning unit, and the input end of the gesture sub-filter is respectively connected with the output ends of the navigation resolving module and the magnetometer.
The working process of the Federal kalman filtering unit specifically comprises the following steps:
the zero-speed signal obtained by the zero-speed detection unit and the resolving speed of the inertial measurement unit are subjected to speed fusion by utilizing a speed sub-filter, and the method specifically comprises the following steps:
for the velocity sub-filter, the velocity error, position error and attitude error are selected as the state quantities thereof without considering the inertial measurement unit error, i.e
wherein />
Representing attitude misalignment angle δv n Representing speed error δp n Representing a position error; the state equation is expressed as:
wherein ,
state estimator representing time k-1, < +.>
Representation->
One-step predictive value of W k-1 and Vk All considered as zero-mean white noise sequences uncorrelated with each other; state transition matrix F from time k to time k-1 k|k-1 And noise input matrix G k|k-1 The score is determined according to the following formula.
The speed sub-filter is utilized to carry out speed fusion, the difference between the zero speed signal obtained by the zero speed detection unit and the calculated speed of the navigation calculation module is used as an observation value, the acceleration signal obtained in the navigation calculation module is corrected through the observation value, and the observation vector and the observation matrix are respectively as follows:
wherein Z＝03×1 -v n 0 in (0) 3×1 Representing zero velocity vector, v, obtained by zero velocity detection n Representing the velocity vector obtained by the navigation solution module solution.
The navigation resolving module is fused with the position and the course measurement output by the UWB wireless positioning unit by using the course sub-filter, and the method specifically comprises the following steps:
for the heading sub-filter, the state equation is the same as the speed sub-filter, namely the state equation of the heading sub-filter is also formula (8), but the observation equation is different; the course sub-filter is used for fusing the position and the course, taking the difference between the position and the course measurement output by the navigation resolving module and the UWB wireless positioning unit as an observation value, correcting the position information and the course angle in the navigation resolving module through the observation value, and respectively obtaining an observation vector and an observation matrix as follows:
in the formula ,Hs Is the course angle calculated by the navigation calculation module, H u Is the course angle, P, calculated by the UWB wireless positioning unit s Is the position calculated by the navigation calculation module, P u Is the position that the UWB wireless location unit has resolved.
The navigation resolving module and the course angle output by the magnetometer are fused by utilizing the gesture sub-filter, and the method specifically comprises the following steps:
for the attitude sub-filter, the state equation is the same as that of the speed sub-filter, namely, the state equation of the attitude sub-filter is also formula (8), the attitude sub-filter is used for carrying out course fusion, and the difference between the course angle output by the navigation resolving module and the magnetometer is used as an observation value. Correcting the course angle in the navigation resolving module through the observed value, wherein the observed vector and the observed matrix are respectively as follows:
in the formula ,Hs Is the course angle calculated by the navigation calculation module, H c Is the heading angle calculated by the magnetometer.
Setting an initial estimation covariance matrix and a process noise covariance matrix of the velocity sub-filter, the heading sub-filter, the attitude sub-filter and the main filter as a federal kalman filter by using an upper bound variance technology
Multiple times, ensuring that each sub-filter is not related to each other, beta i Meets the principle of conservation of information, and has the following specific formula;
wherein 
Wherein i represents an i-th sub-filter; p (P) g,0 Process covariance matrix representing 0 moment public system, Q g,0 Representing a system noise covariance matrix at the moment 0;
the speed sub-filter, the heading sub-filter and the attitude sub-filter are respectively and independently updated in time, and the updating formula is as follows:
in the formula ,
representing the state estimate at time k-1 of the ith sub-filter, <>
Representation->
Current prediction vector of F i,k|k-1 、G i,k|k-1 Representing the state transition matrix and noise input matrix, P, at times k-1 to k of the ith sub-filter i,k-1 、P i,k|k-1 Representation->
Covariance matrix of Q i,k-1 The system process covariance matrix at time k-1 is shown.
The speed sub-filter, the heading sub-filter and the attitude sub-filter are respectively measured and updated (the measurement is observed quantity), and the update formula is as follows:
in the formula ,Ki,k Represents the i-th filter filtering gain matrix at k moment, H i,k Represents the observation matrix at time k, R i,k The variance matrix of the noise at the k time is represented, and I represents the identity matrix.
And (3) updating the time of the main filter, wherein the state equation of the main filter is also formula (8), and the updating formula is as follows:
the estimation information of each sub-filter and the main filter is fused into new global state estimation information, specifically:
global state estimation to be obtained
Error covariance matrix P g Public system noise matrix Q g Resetting each sub-filter and the main filter as follows, while the +.A obtained by step (4.8)>
And sending the corrected speed, course angle and position to a navigation resolving module, wherein the formula (19) is specifically as follows:
example 2
The embodiment of the invention provides a combined indoor pedestrian navigation method based on UWB and SINS, which comprises the following steps:
the method comprises the following steps that (1) an inertial measurement unit is used for collecting human body movement acceleration and rotation angular velocity, and a navigation resolving module is used for calculating course angle and position information based on the human body movement acceleration and rotation angular velocity; the course angle is obtained by integrating a rotation angular velocity signal, and the attitude signal comprises a pitch angle, a roll angle and a course angle, wherein the course angle is the course angle; the calculation process of the position information is also in the prior art, so that excessive details are not needed in the embodiment of the invention;
step (2) utilizing a zero-speed detection unit to judge zero speed based on the human body movement acceleration and the rotation angular velocity output by the inertia measurement unit;
the zero-speed detection is a precondition of zero-speed correction, and the existing zero-speed detection algorithm is mainly based on accelerometer and gyroscope output signals and mainly comprises generalized likelihood ratio detection (Generalized Likelihood Ratio Test, GLRT), accelerometer measurement variance detection (Accelerometer Measurements Variance Test, MV), acceleration measurement amplitude detection (Accelerometer Measurements Magnitude Test, MAG) and angular velocity measurement energy detection (Angular Rate Measurement Energy Test, ARE), wherein the generalized likelihood ratio detection effect is obviously superior to that of other methods, and in one specific implementation of the embodiment of the invention, the zero-speed detection adopts generalized likelihood ratio detection, and gait with different speeds is adapted by judging the motion mode and setting different thresholds;
(2.1) judging a movement pattern
The x-axis of the inertial measurement unit in the embodiment of the invention is taken as the forward axis of the walking direction of the human body, the speed of the human body movement is judged by calculating the average acceleration of the x-axis based on a time window, and the average acceleration of the x-axis at the k moment is set as follows:
setting two threshold values Thv according to empirical values 1 ,Thv 2 Divided into three motion speed modes mod 1 ～mod 3 Representing slow, medium and fast respectively; the specific partitioning method is as follows:
mod i representing the movement pattern i.
(2.2) establishing a generalized likelihood ratio test model
Defining the output of the inertia measurement unit at the moment k as:
wherein ,
representing the triaxial output of the accelerometer in the inertial measurement unit at time k>
Indicating the gyroscope in the inertial measurement unit at time kTriaxial outputs of the screw instrument in m/s respectively 2 、rad/s。
Setting the output quantity of the inertial measurement unit to continuously acquire and output N times (window period) from the moment N to obtain a group of continuous measurement values { y } k |k∈[n,n-N+1]And k is a positive integer. The zero-speed detection method based on the generalized likelihood ratio statistics constructs generalized likelihood ratio statistics values by outputting data to an inertial measurement unit with the length of N, and defines a generalized likelihood ratio statistical model as follows:
wherein ,
representing accelerometer output values, +.>
Representing a gyro output value, wherein I I.I represents a modulus value of a vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope, respectively,/>
Expressed in window period omega n The average of the internal accelerometer data, N, represents the window length and g represents the local gravitational acceleration vector. />
Th i For the empirical threshold value in the motion mode i, the zero_flag is a zero speed flag, the value of zero_flag is 1, the speed is zero, the value of zero_flag is 0, the speed is non-zero, and the zero speed can be judged more accurately by setting different thresholds according to different motion speeds.
Step (3), outputting human body position information and course angle by using the UWB wireless positioning unit;
the UWB wireless positioning unit comprises a base station and a tag, and only plane conditions are considered for convenience of installation; the UWB wireless positioning unit in the embodiment of the invention comprises three base stations and a tag; when the UWB wireless positioning unit works, the tag can output the distance from the tag to each base station, and when the position of each base station is known, the positioning work can be completed by utilizing a three-point positioning mode; let the positions of the three base stations A, B, C be (x) a ,y a ),(x b ,y b ),(x c ,y c ) The tag is output to three base stations at the position d a ,d b ,d c The coordinates of the tag may be expressed as:
in the ultra-wideband signal, the position provided by the UWB wireless positioning unit is relatively accurate, the output course angle is more stable relative to the magnetometer, the course angle is determined by utilizing a mode of determining a straight line by two points, and the calculation equation of the course angle is shown as a formula (7):
Y n represents the vertical axis coordinate, X, at time n n The abscissa at time n is indicated.
Step (4) carrying out data fusion processing on signals output by the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer by utilizing a Federal Kalman filter;
as shown in fig. 2, the federal kalman filter includes a velocity sub-filter, a heading sub-filter, a posture sub-filter, and a main filter;
the data transmission ends of the speed sub-filter, the heading sub-filter and the gesture sub-filter are all connected with the speed sub-filter and the gesture sub-filterThe input end of the speed sub-filter is connected with the output ends of the navigation resolving module and the zero speed detection unit respectively, the input end of the heading sub-filter is connected with the output ends of the navigation resolving module and the UWB wireless positioning unit respectively, and the input end of the gesture sub-filter is connected with the output ends of the navigation resolving module and the magnetometer respectively, wherein X in FIG. 2 i 、P i Representing the local state estimation value and covariance matrix of each filter, X m 、P m Respectively representing the state estimation value and covariance matrix of the main filter, X g 、P g Respectively representing a state estimation value and a covariance matrix of the public system; beta i Representing the coefficients.
In a specific implementation of the embodiment of the present invention, the step (4) includes the following substeps:
step (4.1): the zero-speed signal obtained by the zero-speed detection unit is subjected to speed fusion with the navigation resolving speed by utilizing a speed sub-filter, and the method specifically comprises the following steps:
for the velocity sub-filter, the velocity error, the position error and the attitude error are selected as the state quantities thereof without considering the navigation resolving module error, namely
wherein />
Representing attitude misalignment angle δv n Representing speed error δp n Representing a position error; the state equation is expressed as: />
wherein ,
state estimator representing time k-1, < +.>
Representation->
One-step predictive value of W k-1 and Vk All considered as zero-mean white noise sequences uncorrelated with each other; state transition matrix F from time k to time k-1 k|k-1 And noise input matrix G k|k-1 The components are determined according to the following formula, H represents the observation matrix, Z k The observation vector at time k is shown.
The speed sub-filter is utilized to carry out speed fusion, the difference between the zero speed signal obtained by the zero speed detection unit and the calculated speed of the navigation calculation module is used as an observation value, the acceleration signal obtained in the navigation calculation module is corrected through the observation value, and the observation vector and the observation matrix are respectively as follows:
wherein Z＝03×1 -v n 0 in (0) 3×1 Representing zero velocity vector, v, obtained by zero velocity detection n Representing the velocity vector obtained by inertial navigation solution.
Step (4.2): the navigation resolving module is fused with the position and the course measurement output by the UWB wireless positioning unit by using the course sub-filter, and the method specifically comprises the following steps:
for the heading sub-filter, the state equation is the same as the speed sub-filter, namely the state equation of the heading sub-filter is also formula (8), but the observation equation is different; the course sub-filter is used for fusing the position and the course, taking the difference between the position and the course measurement output by the navigation resolving module and the UWB wireless positioning unit as an observation value, correcting the position information and the course angle in the navigation resolving module through the observation value, and respectively obtaining an observation vector and an observation matrix as follows:
in the formula ,Hs Is the course angle calculated by the navigation calculation module, H u Is the course angle, P, calculated by the UWB wireless positioning unit s Is the position calculated by the navigation calculation module, P u Is the position that the UWB wireless location unit has resolved.
Step (4.3): the navigation resolving module and the course angle output by the magnetometer are fused by utilizing the gesture sub-filter, and the method specifically comprises the following steps:
for the attitude sub-filter, the state equation is the same as that of the speed sub-filter, namely, the state equation of the attitude sub-filter is also formula (8), the attitude sub-filter is used for carrying out course fusion, and the difference between the course angle output by the navigation resolving module and the magnetometer is used as an observation value. Correcting the course angle in the navigation resolving module through the observed value, wherein the observed vector and the observed matrix are respectively as follows:
in the formula ,Hs Is the course angle calculated by the navigation calculation module, H c Is the heading angle calculated by the magnetometer.
Step (4.4) setting an initial estimation covariance matrix and a process noise covariance matrix of the velocity sub-filter, the heading sub-filter, the attitude sub-filter and the main filter as a federal kalman filter by using an upper variance technology
Multiple times, ensuring that each sub-filter is not related to each other, beta i Meets the principle of conservation of information, and is specifically shown in the following formula；
wherein 
Wherein i represents an i-th sub-filter; p (P) g,0 Process covariance matrix representing 0 moment public system, Q g,0 Representing a system noise covariance matrix at the moment 0;
and (4.5) respectively and independently updating the speed sub-filter, the heading sub-filter and the attitude sub-filter in time, wherein the updating formula is as follows:
in the formula ,
representing the state estimate at time k-1 of the ith sub-filter, <>
Representation->
Current prediction vector of F i,k|k-1 、G i,k|k-1 Representing the state transition matrix and noise input matrix, P, at times k-1 to k of the ith sub-filter i,k-1 、P i,k|k-1 Representation->
Covariance matrix of Q i,k-1 The system process covariance matrix at time k-1 is shown.
And (4.6) respectively measuring and updating the speed sub-filter, the heading sub-filter and the attitude sub-filter (the measurement is an observed quantity), wherein the updating formula is as follows:
in the formula ,Ki,k Represents the i-th filter filtering gain matrix at k moment, H i,k Represents the observation matrix at time k, R i,k The variance matrix of the noise at the k time is represented, and I represents the identity matrix.
And (4.7) updating the time of the main filter, wherein the state equation of the main filter is also formula (8), and the updating formula is as follows:
and (4.8) fusing estimation information of each sub-filter and the main filter into new global state estimation information, wherein the method specifically comprises the following steps:
step (4.9) global state estimation to be obtained
Error covariance matrix P g Public system noise matrix Q g Resetting each sub-filter and the main filter as follows, while the +.A obtained by step (4.8)>
And sending the corrected speed, course angle and position to a navigation resolving module, wherein the formula (19) is specifically as follows:
the foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A combined indoor pedestrian navigation system based on UWB and SINS, comprising:
the SINS comprises an inertial measurement unit and a navigation resolving module which are connected, wherein the inertial measurement unit is used for acquiring the motion acceleration and the rotation angular velocity of a human body; the navigation resolving module receives and calculates course angle and position information based on the human body movement acceleration and the rotation angular velocity;
the input end of the zero-speed detection unit is connected with the output end of the inertial measurement unit and is used for receiving the human body motion acceleration and the rotation angular velocity output by the inertial measurement unit, and performing zero-speed judgment based on the human body motion acceleration and the rotation angular velocity to obtain a zero-speed vector;
the UWB wireless positioning unit is used for outputting course angle and position information;
a magnetometer for outputting a heading angle;
the input end of the Federal kalman filtering unit is respectively connected with the output ends of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer, and carries out data fusion on the output signals of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer to obtain error signals of the movement speed, the course angle and the position information of the human body, and the error signals are fed back to the navigation resolving module to carry out speed, course and position correction;
providing a position course angle by utilizing an ultra-wideband when entering an ultra-wideband signal area, and providing a course angle by utilizing a magnetometer in a non-ultra-wideband area;
the Federal kalman filter comprises a speed sub-filter, a course sub-filter, a gesture sub-filter and a main filter, wherein the data transmission ends of the speed sub-filter, the course sub-filter and the gesture sub-filter are all connected with the data transmission end of the main filter, the input end of the speed sub-filter is respectively connected with the output ends of the navigation resolving module and the zero speed detecting unit, the input end of the course sub-filter is respectively connected with the output ends of the navigation resolving module and the UWB wireless positioning unit, and the input end of the gesture sub-filter is respectively connected with the output ends of the navigation resolving module and the magnetometer;
the speed sub-filter carries out speed fusion on the zero-speed signal obtained by the zero-speed detection unit and the calculated speed of the navigation calculation module;
the course sub-filter fuses the navigation resolving module with the position and course measurement output by the UWB wireless positioning unit;
the gesture sub-filter carries out course fusion on the course angle output by the navigation resolving module and the magnetometer;
using upper bound variance technique to make initial estimation covariance matrix P of speed sub-filter, course sub-filter, attitude sub-filter and main filter i,0 Process noise covariance matrix Q i,0 Setting an initial estimated covariance matrix P of a Federal kalman filter g,0 Sum process noise covariance matrix Q g,0 Beta of (2) i -1 Multiple times, ensuring that each sub-filter is not related to each other, beta i The principle of conservation of information is satisfied;
the speed sub-filter, the heading sub-filter and the gesture sub-filter are respectively and independently updated in time;
respectively measuring and updating the speed sub-filter, the heading sub-filter and the gesture sub-filter;
updating the time of the main filter;
fusing the estimation information of each sub-filter and the main filter into new global state estimation information;
new global state estimation information and error covariance matrix P to be obtained g Public system noise matrix Q g Resetting each according to a set ruleThe sub-filter and the main filter, and new global state estimation information obtained simultaneously are sent to an inertial measurement unit for correction of speed, course angle and position.
2. The combined indoor pedestrian navigation system based on UWB and SINS of claim 1 wherein: the zero-speed detection unit comprises a motion mode judgment module, a generalized likelihood ratio detection module and a zero-speed judgment module;
the motion mode judging module is used for judging the motion mode according to the human body motion acceleration output by the inertia measuring unit;
the generalized likelihood ratio checking module is used for calculating generalized likelihood ratio based on human motion acceleration and rotation angular velocity output by the inertia measuring unit;
the zero speed judging module is used for carrying out zero speed judgment according to the generalized likelihood ratio output by the generalized likelihood ratio checking module and the experience threshold value under each motion mode to obtain a zero speed vector.
3. The combined indoor pedestrian navigation system based on UWB and SINS of claim 2 wherein: the generalized likelihood ratio has a calculation formula specifically as follows:
wherein ,
human motion acceleration representing accelerometer output in inertial measurement unit, +.>
The rotation angular velocity of the gyroscope output in the inertial measurement unit is represented, and I.I. I. I represents the modulus value of the vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope in inertial measurement unit, respectively, +.>
Expressed in window period omega n The average of the internal accelerometer data, N, represents the window length and g represents the local gravitational acceleration vector.
4. A combined indoor pedestrian navigation system based on UWB and SINS as in claim 3, wherein: the judgment formula adopted for zero speed judgment according to the generalized likelihood ratio output by the generalized likelihood ratio detection module and the experience threshold value under each motion mode is as follows:
Th i for the empirical threshold in motion pattern i, zero_flag represents a zero speed flag, a value of 1 represents a speed of zero speed, and a value of 0 represents a non-zero speed.
5. The combined indoor pedestrian navigation method based on UWB and SINS is characterized by comprising the following steps:
(1) Acquiring human body movement acceleration and rotation angular velocity by using an inertial measurement unit, and calculating course angle and position information based on the human body movement acceleration and rotation angular velocity by using a navigation resolving module;
(2) The zero-speed detection unit is utilized to carry out zero-speed judgment based on the human motion acceleration and the rotation angular velocity output by the inertia measurement unit, and a zero-speed vector is obtained;
(3) Outputting human body position information and course angle by using the UWB wireless positioning unit;
(4) The output signals of the navigation resolving module, the zero-speed detecting unit, the UWB wireless positioning unit and the magnetometer are subjected to data fusion by utilizing a federal Kalman filter, error signals of human body movement speed, course angle and position information are obtained, and the error signals are fed back to the navigation resolving module to correct the speed, the course and the position; the magnetometer is used for outputting a course angle;
providing a position course angle by utilizing an ultra-wideband when entering an ultra-wideband signal area, and providing a course angle by utilizing a magnetometer in a non-ultra-wideband area;
the Federal kalman filter comprises a speed sub-filter, a course sub-filter, a gesture sub-filter and a main filter, wherein the data transmission ends of the speed sub-filter, the course sub-filter and the gesture sub-filter are all connected with the data transmission end of the main filter, the input end of the speed sub-filter is respectively connected with the output ends of the navigation resolving module and the zero speed detecting unit, the input end of the course sub-filter is respectively connected with the output ends of the navigation resolving module and the UWB wireless positioning unit, and the input end of the gesture sub-filter is respectively connected with the output ends of the navigation resolving module and the magnetometer;
said step (4) comprises the sub-steps of:
(4.1) carrying out speed fusion on the zero-speed signal obtained by the zero-speed detection unit and the calculated speed of the navigation calculation module by utilizing a speed sub-filter;
(4.2) utilizing a course sub-filter to fuse the navigation resolving module with the position and course measurement output by the UWB wireless positioning unit;
(4.3) carrying out course fusion on the course angle output by the navigation resolving module and the magnetometer by utilizing a gesture sub-filter;
(4.4) initial estimation covariance matrix P of velocity sub-filter, heading sub-filter, attitude sub-filter and main filter using upper bound variance technique i,0 Process noise covariance matrix Q i,0 Setting an initial estimated covariance matrix P of a Federal kalman filter g,0 Sum process noise covariance matrix Q g,0 Beta of (2) i -1 Multiple times, ensuring that each sub-filter is not related to each other, beta i The principle of conservation of information is satisfied;
(4.5) respectively and independently updating the speed sub-filter, the heading sub-filter and the attitude sub-filter in time;
(4.6) respectively measuring and updating the speed sub-filter, the heading sub-filter and the attitude sub-filter;
(4.7) updating the time of the main filter;
(4.8) fusing the estimation information of each sub-filter and the main filter into new global state estimation information;
(4.9) New global State estimation information to be obtained, error covariance matrix P g Public system noise matrix Q g And resetting each sub-filter and the main filter according to a set rule, and simultaneously transmitting the obtained new global state estimation information to an inertial measurement unit for correcting the speed, the course angle and the position.
6. The combined indoor pedestrian navigation method based on UWB and SINS according to claim 5, wherein the method comprises the following steps: the zero speed detection unit is used for carrying out zero speed judgment based on the human body motion acceleration and the rotation angular velocity output by the inertia measurement unit, and obtaining a zero speed vector, and specifically comprises the following steps of:
judging a movement mode according to the human movement acceleration output by the inertia measurement unit;
calculating a generalized likelihood ratio based on the human motion acceleration and the rotation angular velocity output by the inertia measurement unit;
and carrying out zero speed judgment according to the generalized likelihood ratio and the empirical threshold under each motion mode to obtain a zero speed vector.
7. The combined indoor pedestrian navigation method based on UWB and SINS according to claim 6, wherein the method comprises the following steps: the generalized likelihood ratio has a calculation formula specifically as follows:
wherein ,
human motion acceleration representing accelerometer output in inertial measurement unit, +.>
The rotation angular velocity of the gyroscope output in the inertial measurement unit is represented, and I.I. I. I represents the modulus value of the vector, and omega n Measuring a window period of the time series for the inertial measurement unit,/->
Observed noise variance of accelerometer and gyroscope in inertial measurement unit, respectively, +.>
Expressed in window period omega n An average value of the internal accelerometer data, N represents a window length, g represents a local gravitational acceleration vector;
the judging formula adopted by the zero speed judgment is as follows:
Th i for the empirical threshold in motion pattern i, zero_flag represents a zero speed flag, a value of 1 represents a speed of zero speed, and a value of 0 represents a non-zero speed.
8. The combined indoor pedestrian navigation method based on UWB and SINS according to claim 5, wherein the method comprises the following steps: the setting rule is as follows:
where k represents the k time, i represents the ith filter,
represents the state estimate of the public system at time k, < >>
Representing the state estimate, P, of the ith filter g,k 、Q g,k Process covariance matrix and noise covariance matrix representing k-moment public system, P i,k 、Q i,k Representing the ith filter process covariance matrix and the noise covariance matrix at time k. />
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