A novel navigation method, device, electronic device and storage medium

Abstract

The present invention discloses a novel navigation method, device, electronic device and storage medium. The method includes determining the first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; calculating the second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; subtracting the first position coordinate from the second position coordinate, and processing the difference through an extended Kalman filter to obtain an optimal deviation estimate; and correcting the positioning error of the first position coordinate using the optimal deviation estimate to obtain updated position information. The solution provided by the present invention combines the inertial navigation positioning technology based on self-sensor positioning with the RFID positioning technology, which can improve positioning accuracy and can be applied to complex indoor environments.

Description

Technical Field

[0001] The present invention relates to the field of navigation and positioning technology, and in particular to a novel navigation method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of smart logistics and intelligent manufacturing, the indoor autonomous positioning and navigation technology of mobile robots has made significant progress. Indoor positioning systems can locate objects in closed spaces such as office buildings, hospitals, stores, factories and warehouses, while satellite positioning systems such as GPS and Beidou usually cannot work properly in the above scenarios.

[0003] Nowadays, the more common indoor positioning and navigation technologies mainly include Bluetooth technology, Wi-Fi technology, ultra-wideband (UWB) technology, radio frequency identification (RFID) positioning technology, laser radar technology, inertial navigation (IMU) technology, etc. These commonly used positioning technologies have the problem of weak independence and need to be assisted by external equipment. When the external environment cannot meet the conditions, positioning will fail. In addition, when the above positioning technologies are used alone, the positioning accuracy that can be achieved is not high, and it is difficult to meet the actual application needs in a specific environment. Summary of the invention

[0004] In order to solve the technical problem of poor positioning accuracy of a single sensor, an embodiment of the present invention provides a new navigation method, device, electronic device and storage medium.

[0005] The technical solution of the embodiment of the present invention is achieved as follows:

[0006] An embodiment of the present invention provides a novel navigation method, which is applied to an electronic device, wherein the electronic device is equipped with a radio frequency identification reader and an inertial measurement unit, wherein the radio frequency identification reader is used to receive a radio frequency signal emitted by at least one radio frequency identification tag, and the inertial measurement unit is used to measure the heading angle of the electronic device, wherein the setting position of the radio frequency identification tag is parallel to the movement path of the electronic device with a preset projection and interval, and the radio frequency identification tag is set on the ground and is located on one side of the radio frequency identification reader; the method comprises: determining a first position coordinate of the radio frequency identification reader according to the position of the radio frequency identification tag and the power value of the radio frequency signal; calculating a second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; subtracting the first position coordinate from the second position coordinate, and obtaining an optimal deviation estimate after the difference is processed by an extended Kalman filter; and correcting the positioning error of the first position coordinate using the optimal deviation estimate to obtain updated position information.

[0007] In one embodiment, determining the first position coordinates of the RFID reader according to the position of the RFID tag and the power value of the RFID signal includes: determining a path segment in which the RFID reader is located relative to the RFID tag according to the position of the RFID tag and the power value of the RFID signal; estimating the distance range between the RFID reader and the RFID tag in the path segment; updating the estimated value by using a Kalman filter, and calculating an angular power loss by using the updated estimated value; recalculating the distance range between the RFID reader and the RFID tag according to the angular power loss; and determining the first position coordinates of the RFID reader according to the recalculated distance range between the RFID reader and the RFID tag.

[0008] In one embodiment, determining the path segment where the RFID reader is located relative to the RFID tag based on the position of the RFID tag and the power value of the RFID signal includes: selecting two RFID signals with the largest power values ​​from the RFID signals; determining whether the power difference between the two RFID signals is greater than a preset threshold; and determining that the RFID reader is located in the path segment corresponding to the two RFID signals when the power difference between the two RFID signals is greater than the preset threshold.

[0009] In one embodiment, in the path segmentation, estimating the distance range between the RFID reader and the RFID tag includes: establishing an RSSI measurement and error model; determining a model expression of the RSSI measurement and error model in an initial state; obtaining a mathematical expression of the distance range between the RFID reader and the RFID tag based on the model expression of the RSSI measurement and error model in the initial state; and calculating the distance range between the RFID reader and the RFID tag according to the mathematical expression of the distance range between the RFID reader and the RFID tag; wherein the RSSI measurement and error model is:

[0010] Calculation formula (1)

[0011] in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission. Indicates the power loss value as the angle changes;

[0012] The model expression of the RSSI measurement and error model in the initial state is:

[0013] Calculation formula (2)

[0014] in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. represents the path loss exponent; is an estimate of the distance d, Indicates the close reference distance, is the corresponding distance estimation error, is a zero-mean Gaussian random variable;

[0015] The mathematical expression of the distance range between the RFID reader and the RFID tag is:

[0016] Calculation formula (3)

[0017] in, is an estimate of the distance d, Indicates the close reference distance, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. Represents the radio frequency signal, is a zero-mean Gaussian random variable, Represents the path loss exponent.

[0018] In one embodiment, the second position coordinates of the electronic device are calculated using the heading angle measured by the inertial measurement unit, including: performing coordinate conversion on the heading angle measured by the inertial measurement unit to obtain a posture matrix; based on the quaternion state equation measured by a gyroscope, converting the posture matrix into a posture quaternion expression; solving the posture quaternion expression to determine the posture angle information of the electronic device; and calculating the second position coordinates of the electronic device using the posture angle information.

[0019] In one embodiment, the posture matrix is:

[0020] Calculation formula (4)

[0021] in, is the posture matrix, , , Indicates the heading angle, pitch angle and roll angle of the electronic equipment;

[0022] The attitude quaternion expression is:

[0023] Calculation formula (5)

[0024] in, is the posture matrix, , , , They are all quaternions from the navigation coordinate system to the carrier coordinate system; , , , , , , , It is the projection component from each direction of the navigation coordinate system to each direction of the carrier coordinate system;

[0025] The attitude angle information is:

[0026] Calculation formula (6)

[0027] in, , , Indicates the heading angle, pitch angle and roll angle of the electronic device. , , , , It is the projection component from each direction of the navigation coordinate system to each direction of the carrier coordinate system.

[0028] In one embodiment, using the optimal deviation estimate to correct the positioning error of the first position coordinate to obtain updated position information includes: using the optimal deviation estimate to correct the positioning error of the first position coordinate by the following calculation formula to obtain updated position information:

[0029] Calculation formula (7)

[0030] in, is the updated location information. and is the optimal deviation estimate, and is the first position coordinate.

[0031] The embodiment of the present invention further provides a novel navigation device, which is applied to an electronic device, wherein the electronic device is equipped with an RFID reader and an inertial measurement unit, wherein the RFID reader is used to receive a radio frequency signal emitted by at least one RFID tag, and the inertial measurement unit is used to measure the heading angle of the electronic device, wherein the RFID tag is set at a position parallel to the movement path of the electronic device with a preset projection and interval, and the RFID tag is set on the ground and is located on one side of the RFID reader; the novel navigation device includes: a first position coordinate module, which is used to determine the first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; a second position coordinate module, which is used to calculate the second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; an optimal deviation estimation module, which is used to make a difference between the first position coordinate and the second position coordinate, and obtain an optimal deviation estimate after the difference is processed by an extended Kalman filter; and an updating module, which is used to correct the positioning error of the first position coordinate using the optimal deviation estimate to obtain updated position information.

[0032] An embodiment of the present invention further provides an electronic device, comprising an RFID reader, an inertial measurement unit and a processor; the RFID reader is used to receive a radio frequency signal emitted by at least one RFID tag; the RFID tags are set at positions parallel to the movement path of the electronic device with a preset projection and interval, and the RFID tags are set on the ground and are all located on one side of the RFID reader; the inertial measurement unit is used to measure the heading angle of the electronic device; and the processor is used to execute the steps of the above-mentioned method.

[0033] An embodiment of the present invention further provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0034] The new navigation method, device, electronic device and storage medium provided by the embodiment of the present invention determine the first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; calculate the second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; make a difference between the first position coordinate and the second position coordinate, and obtain the optimal deviation estimate after processing the difference through an extended Kalman filter; use the optimal deviation estimate to correct the positioning error of the first position coordinate to obtain updated position information. The solution provided by the present invention combines the inertial navigation positioning technology based on self-sensor positioning with the RFID positioning technology, which can improve the positioning accuracy and can be applied to complex indoor environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a flow chart of a novel navigation method according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the data processing flow of an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the RFID geometric constraint principle according to an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of an experimental device according to an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of an experimental scenario of an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of the structure of a novel navigation device according to an embodiment of the present invention;

[0041] Figure 7 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0043] An embodiment of the present invention provides a novel navigation method, which is applied to an electronic device, wherein the electronic device is equipped with a radio frequency identification reader and an inertial measurement unit, wherein the radio frequency identification reader is used to receive a radio frequency signal emitted by at least one radio frequency identification tag, and the inertial measurement unit is used to measure the heading angle of the electronic device, wherein the setting position of the radio frequency identification tag is parallel to the movement path of the electronic device with a preset projection and interval, and the radio frequency identification tag is set on the ground and is located on one side of the radio frequency identification reader; Figure 1 As shown, the method includes:

[0044] Step 101: determining the first position coordinates of the RFID reader according to the position of the RFID tag and the power value of the RFID signal;

[0045] Step 102: Calculating the second position coordinates of the electronic device using the heading angle measured by the inertial measurement unit;

[0046] Step 103: Subtract the first position coordinate from the second position coordinate, and process the difference through an extended Kalman filter to obtain an optimal deviation estimate;

[0047] Step 104: Correct the positioning error of the first position coordinates using the optimal deviation estimate to obtain updated position information.

[0048] The electronic device in this embodiment can be a wheeled robot. This embodiment can be used for positioning and tracking wheeled robots in indoor environments and complex environments. This embodiment adopts a combined navigation method of multi-sensor fusion to solve the problem of poor positioning accuracy of a single sensor and easy zero drift and dynamic error.

[0049] Specifically, this embodiment will first use the position of the radio frequency identification tag (i.e., RFID tag) and the power value of the reverse radio frequency signal transmitted to the radio frequency identification reader (i.e., RFID reader) to estimate the position coordinates of the RFID reader; then use the heading angle measured by the inertial measurement unit (i.e., IMU) to calculate the current positioning position coordinates; then make a difference between the two obtained position coordinates, and pass the difference through EKF to obtain the optimal deviation estimate; finally, use the optimal deviation estimate to correct the RFID positioning error and update the position information. See Figure 2 In this embodiment, the RFID information is first used to estimate the RFID position through the RSSI model distance, and the IMU information is used to calculate the IMU position through the quaternion posture solution. Then, the RFID position and the IMU position are corrected through EKF and the position is updated. This embodiment implements the main navigation function through RFID, and integrates the IMU measurement information to correct the position error, which solves the problem of cumulative error in relying solely on IMU positioning and improves the positioning accuracy. This navigation method is highly applicable in the field of autonomous movement and navigation of robots in modern logistics warehouses.

[0050] RFID (Radio Frequency Identification) can identify and locate objects through radio signals. The RFID system consists of RFID tags, RFID readers, and RFID data processing systems. RFID readers communicate with RFID tags by sending radio signals, reading information from the tags and obtaining the distance between the tags and the reader, thereby locating and tracking the target.

[0051] Furthermore, step 101 in this embodiment may specifically include the following steps:

[0052] S201, determining a path segment of the RFID reader relative to the RFID tag according to the position of the RFID tag and the power value of the RFID signal;

[0053] S202, estimating the distance range between the RFID reader and the RFID tag in the path segment;

[0054] S203, using Kalman filtering to update the estimated value, and using the updated estimated value to calculate the angle power loss;

[0055] S204, recalculating the distance range between the RFID reader and the RFID tag according to the angular power loss;

[0056] S205: Determine a first position coordinate of the RFID reader according to the recalculated distance range between the RFID reader and the RFID tag.

[0057] Since the present embodiment is to install an RFID reader on the wheeled robot to receive the radio frequency signal sent by the RFID tag. Figure 3 , Figure 4 and Figure 5 , assuming that a series of passive RFID tags are parallel to the path of the wheeled robot with a fixed projection and known intervals. The tags are attached to the ground and placed on one side of the RFID reader. Assume that there are geometric constraints between the RFID reader and the RFID tags: (1) The preset trajectory and the RFID tags are in the same plane, and the line connecting tags n and n+1 is parallel to the corresponding path, that is, the shortest distance between the lines connecting adjacent tags on the path is a known constant (2) The RFID reader on the wheeled robot and the two tags closest to it are in the same plane.

[0058] S201 is specifically: select two RSSI values ​​that are larger and the difference between them exceeds The segment corresponding to the label is recorded as , , assuming that the distance between two adjacent labels is ,in , express Projection on the line segment connecting the two tags. That is, select the two RF signals with the largest power values ​​from the RF signals; determine whether the power difference between the two RF signals is greater than a preset threshold (for example ); when the power difference between the two radio frequency signals is greater than the preset threshold, determining that the radio frequency identification reader is located in the path segment corresponding to the two radio frequency signals.

[0059] In addition, in actual applications, there are several special cases: when the two largest RSSI values ​​are not significantly different from other values, consider using three maximum RSSI values ​​and assume that these values ​​correspond to three consecutive labels n-1, n and n+1. When , the estimated value of the reader position is the projection of tag n on the reader path. or When , the reader position estimate is the projection of the midpoint between tags n-1 and n or between tags n and n+1 on the reader path, where It is a smaller parameter obtained by experiment.

[0060] S202 is specifically as follows: Range estimation is to preliminarily estimate the distance d between the reader and the corresponding segment tag, and to establish a RSSI measurement and error model. Due to geometric constraints, only the RSSI nominal value and the direction angle are considered. The relevant angle loss model can be expressed as:

[0061]

[0062] in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest signal transmission. represents the power loss value as the angle changes. The measured signal level model used in the present invention is completely based on the received average signal, which decreases logarithmically with the distance and the average value related to the path loss distance follows a normal distribution. Therefore, the signal level model is:

[0063]

[0064] in, , Indicates the transmit power of the tag, represents the average large-scale path loss, Indicates the close reference distance, represents the path loss exponent, is a zero-mean Gaussian random variable, d Represents the distance. So its measurement model can be expressed as:

[0065]

[0066] In the initial state, the angle is ignored. and The distance estimation model can be simplified as follows:

[0067]

[0068] in, is an estimate of the distance d, is the corresponding distance estimation error. So the estimated value of the distance can be obtained as:

[0069]

[0070] According to this formula, the estimated distance when reaching a certain location for the kth time can be calculated by the given exponential power. . And because , we can get the estimated distance projection value . Reuse geometric relations , the angle estimate can be obtained .in, , It indicates the fixed distance between the reader path and the tag track when laying the tag. It is assumed here that the two are parallel and therefore the distance is a constant.

[0071] S203 is specifically as follows: after obtaining the distance and the projection distance estimation, using the Kalman filter to obtain the updated estimation value and , thereby reducing the error in reader position estimation. The specific steps of Kalman filtering include:

[0072] In order to obtain the effect of angle loss on distance estimation, it is assumed that each position of the moving carrier is fixed and independent, that is, , so the state equation of the estimated value is:

[0073]

[0074] in, , represents noise, so the covariance of the noise is:

[0075]

[0076] Taking into account the projection error, the measurement equation for the estimated value is:

[0077]

[0078] The quantity measured is ,Measurement Matrix , Represents the measurement error, .

[0079] in, , represents the distance between two consecutive labels.

[0080] The recursive formula of the corresponding Kalman filter is as follows. Assuming that the initial position of the motion carrier is the nth label point, the initial state of the projection estimate is:

[0081]

[0082] set up and Represent the system's prior state estimate and prior state error covariance matrix respectively. Then when k>0:

[0083]

[0084] in, is the error covariance matrix at time k-1, is the covariance matrix of the noise, Kalman gain The calculation formula is:

[0085]

[0086] in, represents the measurement noise covariance matrix.

[0087] Assumptions represents the updated state estimate. Since the state transformation matrix A is the identity matrix, we have . Therefore, the updated state estimate is:

[0088]

[0089] in, is the measured value.

[0090] The covariance matrix of the updated estimated state error is:

[0091]

[0092] in, is the identity matrix.

[0093] Get the updated value of the estimated distance value through Kalman filtering and angle update value , .

[0094] S204 is specifically: Estimation Assume that there is an angle threshold between the reader and the tag ,when When , that is, ignoring the angle loss; when , the updated angle is used Estimated angular loss , .

[0095] Here, the formula for estimating the angle loss by the updated angle is:

[0096]

[0097] in, It is a constant set according to different environments.

[0098] After the angle loss is estimated, re-estimate the , thereby obtaining a distance estimate that takes into account angle-related losses and improving positioning accuracy.

[0099] S205 specifically includes: obtaining a location point according to the latest distance estimation value , and then project the point to the path of the wheeled robot to get the current position of the wheeled robot , and store the positioning location information.

[0100] The advantage of IMU inertial navigation positioning is that it does not rely on external information, and can solve the position information only through the sensor's own measurement value. This excellent independence enables it to play a greater role in positioning in complex indoor environments.

[0101] Furthermore, step 102 in this embodiment may specifically include the following steps:

[0102] S301, performing coordinate transformation on the heading angle measured by the inertial measurement unit to obtain an attitude matrix;

[0103] S302, based on the quaternion state equation measured by the gyroscope, converting the attitude matrix into an attitude quaternion expression; solving the attitude quaternion expression to determine the attitude angle information of the electronic device;

[0104] S303, calculating the second position coordinates of the electronic device using the attitude angle information;

[0105] S304: Store the second position coordinates.

[0106] Specifically, S301 is: converting the carrier coordinate system to the navigation coordinate system. Generally, before performing inertial navigation solution, the output data in the carrier coordinate system (n system) must be converted to the navigation coordinate system (b system). Assume , , The heading angle, pitch angle and roll angle of the moving carrier are represented in turn. The conversion relationship between the two coordinate systems is as follows, and the attitude matrix is:

[0107]

[0108] S302 is specifically: the quaternion state equation based on gyroscope measurement is:

[0109]

[0110]

[0111] in, Represents the quaternion from the navigation coordinate system to the carrier coordinate system, Indicates the angular velocity of the navigation coordinate system relative to the carrier coordinate system. , Represents the measurement value of the gyroscope in each direction. The attitude matrix is ​​expressed as a quaternion:

[0112]

[0113] , , , They are all quaternions from the navigation coordinate system to the carrier coordinate system; , , , , , , , It is the projection component from each direction of the navigation coordinate system to each direction of the carrier coordinate system.

[0114] Substituting the quaternion value obtained by solving the differential equation, the attitude matrix can be calculated. The attitude matrix update calculation is completed by the above formula, and the attitude angle information of the carrier is obtained as follows:

[0115]

[0116] S303 is specifically as follows: the direction of motion of the carrier is obtained by solving the attitude angle information. According to the geometric constraints, it is assumed that the motion of the wheeled robot is on a 2-D plane. Therefore, only the heading angle obtained from the IMU is considered. to navigate.

[0117] Assume that the coordinates of the label point n are , the coordinates of the label n+1 point are IMU sensor every The heading angle is measured once, so there are j heading angle measurements from tag n to n+1, recorded as Then the distance between two adjacent labels can be divided into j-1 segments, and the distance between each segment is , can be obtained by integrating the acceleration obtained by IMU. From the geometric relationship between the heading angle and the tag point:

[0118]

[0119] in, , we can find the moving distance of the carrier on the x and y coordinate axes. Finally, we can calculate the estimated position of the moving carrier at the instant of sampling time: .

[0120] S304 is specifically: storing the position coordinates of the moving carrier estimated by the IMU sensor .

[0121] Furthermore, step 103 in this embodiment may specifically include the following steps:

[0122] When only a single RFID sensor is used for positioning, it is usually assumed that the line between consecutive tags is parallel to the trajectory of the wheeled robot. However, when the density of the tags is not large enough or the trajectory is bent at a large angle, this assumption will lead to positioning errors, causing the wheeled robot to yaw or derail. Therefore, the position information provided by the IMU is used for error correction. And the extended Kalman filter method is used to find its optimal deviation, thereby improving the accuracy of error correction. This fusion process effectively reduces the uncertainty of positioning. The specific steps are:

[0123] Assume that the motion of the wheeled robot on the predetermined track is uniformly accelerated linear motion, and establish the state equation with the two sensor positioning deviations as state variables as follows:

[0124]

[0125] The motion equation can be established based on the motion characteristics of uniformly accelerated linear motion:

[0126]

[0127] in, is the data collection time interval of IMU, and represents the position difference of the two sensor positioning systems in the x and y coordinate directions of the navigation coordinate system at time k, and represents the speed difference of the two sensor positioning systems in the x and y coordinate axes at time k, and and The acceleration in the x and y coordinate axes is directly obtained by the accelerometer in the IMU sensor. Therefore, in the navigation state equation, represents the state vector of the wheeled robot at time k. The state matrix can be obtained from the motion equation:

[0128]

[0129] The standard measurement equation in the EKF can be expressed as:

[0130]

[0131] The positioning coordinate deviation of the two sensor systems in the corresponding system is:

[0132]

[0133] For the measurement equation, the measurement variable at time k is , represents the measurement noise and obeys the normal distribution. The measurement matrix is .

[0134] The specific steps of using the above positioning deviation model to obtain the optimal deviation estimation through extended Karl filtering are as follows:

[0135] (1) The prior state estimation equation of EKF is:

[0136]

[0137] in, is the known input vector of the system, is the posterior estimator at the previous moment.

[0138] (2) The prior covariance estimation equation is:

[0139]

[0140] Among them, F is the Jacobian matrix representing the partial derivative of the state update function with respect to the posterior estimate of the previous moment, Represents the covariance matrix of the process noise.

[0141] (3) The Kalman gain calculation formula is:

[0142]

[0143] in, is the partial derivative of the observation function with respect to the observed quantity, Represents the covariance matrix of the measurement noise.

[0144] (4) The update equation of the posterior estimated covariance matrix is:

[0145]

[0146] (5) The posterior estimation formula is:

[0147]

[0148] The optimal deviation estimates in the x and y coordinate axes are obtained respectively, denoted as and .

[0149] Furthermore, step 104 in this embodiment may specifically include the following steps:

[0150] Correction of RFID positioning errors:

[0151]

[0152] The positioning coordinates after updating the location information are .

[0153] This embodiment aims at the complex scenes in modern logistics warehouses, and combines the inertial navigation positioning technology based on its own sensor positioning with the radio frequency identification (RFID) positioning technology to overcome the low positioning accuracy of traditional methods. This new combined navigation method can give full play to the advantages of the two positioning technologies and achieve more accurate and reliable navigation in complex environments.

[0154] This embodiment realizes the main navigation function through RFID and integrates IMU measurement information to correct the position error, which solves the problem of cumulative error in relying solely on IMU positioning and improves positioning accuracy. This navigation method is highly applicable in the field of autonomous movement and navigation of robots in modern logistics warehouses.

[0155] The novel navigation method provided by the embodiment of the present invention determines the first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; calculates the second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; subtracts the first position coordinate from the second position coordinate, and processes the difference through an extended Kalman filter to obtain an optimal deviation estimate; and uses the optimal deviation estimate to correct the positioning error of the first position coordinate to obtain updated position information. The solution provided by the present invention combines the inertial navigation positioning technology based on self-sensor positioning with the RFID positioning technology, which can improve positioning accuracy and can be applied to complex indoor environments.

[0156] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides a novel navigation device, which is applied to an electronic device, wherein the electronic device is configured with an RFID reader and an inertial measurement unit, wherein the RFID reader is used to receive a RFID signal emitted by at least one RFID tag, and the inertial measurement unit is used to measure the heading angle of the electronic device, wherein the RFID tags are arranged at positions parallel to the movement path of the electronic device with a preset projection and interval, and wherein the RFID tags are arranged on the ground and are all located on one side of the RFID reader; Figure 6 As shown, the novel navigation device 600 comprises:

[0157] A first position coordinate module 601, configured to determine a first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal;

[0158] A second position coordinate module 602, configured to calculate a second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit;

[0159] The optimal deviation estimation module 603 is used to make a difference between the first position coordinate and the second position coordinate, and process the difference through an extended Kalman filter to obtain an optimal deviation estimate;

[0160] The updating module 604 is used to correct the positioning error of the first position coordinates by using the optimal deviation estimation to obtain updated position information.

[0161] In actual application, the above modules can all be realized by the processor in the new navigation device.

[0162] It should be noted that: when the above-mentioned device provided in the above-mentioned embodiment is executed, only the division of the above-mentioned program modules is used as an example. In actual application, the above-mentioned processing can be assigned to different program modules as needed, that is, the internal structure of the terminal is divided into different program modules to complete all or part of the above-mentioned processing. In addition, the above-mentioned device provided in the above-mentioned embodiment and the above-mentioned method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0163] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the above method.

[0164] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides an electronic device (computer device). Specifically, in one embodiment, the computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0165] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0166] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0167] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0168] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0169] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0171] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0172] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0173] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0174] It can be understood that the memory of the embodiment of the present invention can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0175] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0176] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A new navigation method, characterized in that: Applied to an electronic device, the electronic device is equipped with a radio frequency identification reader and an inertial measurement unit, the radio frequency identification reader is used to receive a radio frequency signal emitted by at least one radio frequency identification tag, the inertial measurement unit is used to measure the heading angle of the electronic device, the radio frequency identification tag is set at a position parallel to the movement path of the electronic device with a preset projection and interval, the radio frequency identification tags are set on the ground and are all located on one side of the radio frequency identification reader; the method comprises: Determining a first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; Calculating a second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; Subtracting the first position coordinate from the second position coordinate and processing the difference through an extended Kalman filter to obtain an optimal deviation estimate; Correcting the positioning error of the first position coordinates using the optimal deviation estimate to obtain updated position information; Wherein, the determining the first position coordinates of the RFID reader according to the position of the RFID tag and the power value of the RFID signal comprises: determining the path segment where the RFID reader is located relative to the RFID tag according to the position of the RFID tag and the power value of the RFID signal; estimating the distance range between the RFID reader and the RFID tag in the path segment; updating the estimated value by using Kalman filtering, and calculating the angular power loss by using the updated estimated value; recalculating the distance range between the RFID reader and the RFID tag according to the angular power loss; and determining the first position coordinates of the RFID reader according to the recalculated distance range between the RFID reader and the RFID tag; Among them, in the path segmentation, estimating the distance range between the RFID reader and the RFID tag includes: establishing an RSSI measurement and error model; determining a model expression of the RSSI measurement and error model in an initial state; obtaining a mathematical expression of the distance range between the RFID reader and the RFID tag based on the model expression of the RSSI measurement and error model in the initial state; calculating the distance range between the RFID reader and the RFID tag according to the mathematical expression of the distance range between the RFID reader and the RFID tag; Wherein, the RSSI measurement and error model is: Calculation formula (1) in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission. Indicates the power loss value as the angle changes; The model expression of the RSSI measurement and error model in the initial state is: Calculation formula (2) in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. represents the path loss exponent; is the estimated value of the distance d, calculated as , Indicates the close reference distance, is the corresponding distance estimation error, is a zero-mean Gaussian random variable; The mathematical expression of the distance range between the RFID reader and the RFID tag is: Calculation formula (3) in, is an estimate of the distance d, Indicates the close reference distance, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. Represents the radio frequency signal, is a zero-mean Gaussian random variable, represents the path loss exponent; Among them, the Kalman filter is used to update the estimated value, including: Assume that each location where the electronic device operates is fixed and independent, that is, , so the state equation of the estimated value is: in, , represents noise, so the covariance of the noise is: Taking into account the projection error, the measurement equation for the estimated value is: The quantity measured is ,Measurement Matrix , Represents the measurement error, , , represents the distance between two consecutive labels; Assuming that the initial position of the motion carrier is the nth label point, the initial state of the projection estimate is: set up and Represent the system's prior state estimate and prior state error covariance matrix respectively; then when k>0: in, is the error covariance matrix at time k-1, is the covariance matrix of the noise, , Kalman gain The calculation formula is: in, represents the measurement noise covariance matrix; Assumptions Represents the updated state estimate. Since the state transformation matrix A is the identity matrix, we have , so the updated state estimate is: in, is the measured value; The covariance matrix of the updated estimated state error is: in, is the identity matrix; Get the updated value of the estimated distance value through Kalman filtering and angle update value , ; Wherein, calculating the angular power loss using the updated estimated value includes; Assume there is an angle threshold between the reader and the tag ,when When , that is, ignoring the angle loss; when , the updated angle is used Estimated angular loss , ; Here, the formula for estimating the angle loss by the updated angle is: in, It is a constant set according to different environments; Wherein, calculating the distance range between the RFID reader and the RFID tag according to the mathematical expression of the distance range between the RFID reader and the RFID tag comprises: After estimating the angle loss, re-estimate based on the angle loss , based on the recalculation results, recalculate the distance estimate.

2. The novel navigation method according to claim 1, characterized in that: The determining the path segment of the RFID reader relative to the RFID tag according to the position of the RFID tag and the power value of the RFID signal comprises: Select two radio frequency signals with the largest power values ​​from the radio frequency signals; Determining whether a power difference between the two radio frequency signals is greater than a preset threshold; When the power difference between the two radio frequency signals is greater than the preset threshold, it is determined that the radio frequency identification reader is located in the path segment corresponding to the two radio frequency signals.

3. The novel navigation method according to claim 1, characterized in that: Calculating the second position coordinates of the electronic device using the heading angle measured by the inertial measurement unit includes: Performing coordinate transformation on the heading angle measured by the inertial measurement unit to obtain an attitude matrix; Based on the quaternion state equation measured by the gyroscope, the attitude matrix is ​​converted into an attitude quaternion expression; the attitude quaternion expression is solved to determine the attitude angle information of the electronic device; The second position coordinates of the electronic device are calculated using the attitude angle information.

4. The novel navigation method according to claim 3 is characterized in that: The posture matrix is: Calculation formula (4) in, is the attitude matrix, , , Indicates the heading angle, pitch angle and roll angle of the electronic equipment; The attitude quaternion expression is: Calculation formula (5) in, is the posture matrix, , , , They are all quaternions from the navigation coordinate system to the carrier coordinate system; , , , , , , , It is the projection component from each direction of the navigation coordinate system to each direction of the carrier coordinate system; The attitude angle information is: Calculation formula (6) in, , , Indicates the heading angle, pitch angle and roll angle of the electronic device. , , , , It is the projection component from each direction of the navigation coordinate system to each direction of the carrier coordinate system.

5. The novel navigation method according to claim 1, characterized in that: Correcting the positioning error of the first position coordinates by using the optimal deviation estimate to obtain updated position information includes: The optimal deviation estimate is used to correct the positioning error of the first position coordinates through the following calculation formula to obtain updated position information: Calculation formula (7) in, is the updated location information. and is the optimal deviation estimate, and is the first position coordinate.

6. A new navigation device, characterized in that: Applied to an electronic device, the electronic device is equipped with a radio frequency identification reader and an inertial measurement unit, the radio frequency identification reader is used to receive a radio frequency signal emitted by at least one radio frequency identification tag, the inertial measurement unit is used to measure the heading angle of the electronic device, the radio frequency identification tag is set at a position parallel to the movement path of the electronic device with a preset projection and interval, the radio frequency identification tags are set on the ground and are all located on one side of the radio frequency identification reader; the novel navigation device comprises: A first position coordinate module, used to determine a first position coordinate of the RFID reader according to the position of the RFID tag and the power value of the RFID signal; A second position coordinate module, used to calculate the second position coordinate of the electronic device using the heading angle measured by the inertial measurement unit; An optimal deviation estimation module, used for subtracting the first position coordinate from the second position coordinate and processing the difference through an extended Kalman filter to obtain an optimal deviation estimate; An updating module, configured to correct a positioning error of the first position coordinate using the optimal deviation estimate to obtain updated position information; The first position coordinate module is further specifically used to determine the path segment where the RFID reader is located relative to the RFID tag according to the position of the RFID tag and the power value of the RFID signal; in the path segment, estimate the distance range between the RFID reader and the RFID tag; update the estimated value by using Kalman filtering, and calculate the angular power loss by using the updated estimated value; recalculate the distance range between the RFID reader and the RFID tag according to the angular power loss; determine the first position coordinate of the RFID reader according to the recalculated distance range between the RFID reader and the RFID tag; Among them, in the path segmentation, estimating the distance range between the RFID reader and the RFID tag includes: establishing an RSSI measurement and error model; determining a model expression of the RSSI measurement and error model in an initial state; obtaining a mathematical expression of the distance range between the RFID reader and the RFID tag based on the model expression of the RSSI measurement and error model in the initial state; calculating the distance range between the RFID reader and the RFID tag according to the mathematical expression of the distance range between the RFID reader and the RFID tag; Wherein, the RSSI measurement and error model is: Calculation formula (1) in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission. Indicates the power loss value as the angle changes; The model expression of the RSSI measurement and error model in the initial state is: Calculation formula (2) in, Represents the radio frequency signal, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. represents the path loss exponent; is the estimated value of the distance d, calculated as , Indicates the close reference distance, is the corresponding distance estimation error, is a zero-mean Gaussian random variable; The mathematical expression of the distance range between the RFID reader and the RFID tag is: Calculation formula (3) in, is an estimate of the distance d, Indicates the close reference distance, Indicates the received power in the direction of the strongest RF signal transmission in the initial state. Represents the radio frequency signal, is a zero-mean Gaussian random variable, represents the path loss exponent; Among them, the Kalman filter is used to update the estimated value, including: Assume that each location where the electronic device operates is fixed and independent, that is, , so the state equation of the estimated value is: in, , represents noise, so the covariance of the noise is: Taking into account the projection error, the measurement equation for the estimated value is: The quantity measured is ,Measurement Matrix , Represents the measurement error, , , represents the distance between two consecutive labels; Assuming that the initial position of the motion carrier is the nth label point, the initial state of the projection estimate is: set up and Represent the system's prior state estimate and prior state error covariance matrix respectively; then when k>0: in, is the error covariance matrix at time k-1, is the covariance matrix of the noise, , Kalman gain The calculation formula is: in, represents the measurement noise covariance matrix; Assumptions Represents the updated state estimate. Since the state transformation matrix A is the identity matrix, we have , so the updated state estimate is: in, is the measured value; The covariance matrix of the updated estimated state error is: in, is the identity matrix; Get the updated value of the estimated distance value through Kalman filtering and angle update value , ; Wherein, calculating the angular power loss using the updated estimated value includes; Assume there is an angle threshold between the reader and the tag ,when When , that is, ignoring the angle loss; when , the updated angle is used Estimated angular loss , ; Here, the formula for estimating the angle loss by the updated angle is: in, It is a constant set according to different environments; Wherein, calculating the distance range between the RFID reader and the RFID tag according to the mathematical expression of the distance range between the RFID reader and the RFID tag comprises: After estimating the angle loss, re-estimate based on the angle loss , based on the recalculation results, recalculate the distance estimate.

7. An electronic device, characterized in that: The electronic device includes a radio frequency identification reader, an inertial measurement unit, and a processor; The RFID reader is used to receive a radio frequency signal emitted by at least one RFID tag; the RFID tags are arranged parallel to the movement path of the electronic device with a preset projection and interval, and the RFID tags are arranged on the ground and are all located on one side of the RFID reader; The inertial measurement unit is used to measure the heading angle of the electronic device; The processor is used to execute the steps of the method according to any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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