A positioning method and electronic device in the absence of GPS signal

Abstract

The present application provides a positioning method and electronic device in the case of GPS signal loss, wherein the method includes: when it is determined that the received initial GPS signal is missing, obtaining GPS data, inertial navigation data and pedestrian dead reckoning PDR data; performing a first fusion process on the GPS data and the inertial navigation data to obtain first fusion data; performing a second fusion process on the GPS data and the PDR data to obtain second fusion data; performing a third fusion process on the first fusion data and the second fusion data to obtain a prediction result and a positioning result. The present application solves the problems of unstable positioning and missing positioning caused by occlusion, electromagnetic signal interference, etc. by using a method of high-precision satellite positioning and trajectory prediction and estimation to ensure the accuracy and stability of positioning information.

Description

Technical Field

[0001] The present application relates to the field of positioning, and in particular to a positioning method and electronic equipment in the absence of GPS signals. Background Art

[0002] Among the current personnel positioning methods, positioning is mainly carried out through satellite positioning technology GPS. When the GPS signal is missing, for example, when personnel are working in the power system environment, the presence of a large amount of metal, strong electromagnetic fields, etc. in the environment will interfere with the received signal and cause inaccurate positioning. If there are a lot of obstructions in the environment, the positioning will also be unstable. The lack of GPS signal will cause inaccurate positioning. Summary of the invention

[0003] In view of this, the purpose of the present application is to propose a positioning method and electronic device in the absence of GPS signals to solve or partially solve the above technical problems.

[0004] Based on the above purpose, the present application provides a positioning method in the absence of GPS signal, including:

[0005] When it is determined that the initial GPS signal received is missing, GPS data, inertial navigation data, and pedestrian dead reckoning PDR data are obtained;

[0006] Performing a first fusion process on the GPS data and the inertial navigation data to obtain first fusion data;

[0007] Performing a second fusion process on the GPS data and the PDR data to obtain second fusion data;

[0008] The first fusion data and the second fusion data are fused for the third time to obtain a prediction result and a positioning result.

[0009] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the program.

[0010] As can be seen from the above, the positioning method provided by the present application in the case of lack of GPS signal, in the case of lack of GPS signal, uses inertial navigation data and GPS data to fuse, can realize the calibration of GPS positioning data. In order to ensure the stability of positioning, PDR data is once again fused with GPS data, GPS data is calibrated again, and the positioning result obtained by fusing the two fused data for the third time has higher accuracy, and solves the defects of inertial navigation in long-term navigation. In the case of lack of GPS signal, the problem of position misjudgment caused by inaccurate and unstable positioning can be effectively solved, and the accurate collection of the movement position of personnel can be ensured, thereby ensuring the reliability of safety monitoring of personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a flowchart of a positioning method in the absence of a GPS signal according to an embodiment of the present application;

[0013] Figure 2 This is a structural block diagram of a positioning device in the case of a GPS signal loss in an embodiment of the present application;

[0014] Figure 3 A structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0016] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] The inertial navigation system in the related art is an autonomous system that does not rely on any external information and does not radiate energy to the outside, so it has good concealment and is not affected by external electromagnetic interference. It can work in the air, on the surface of the earth and even underwater all day and all the time, and the navigation information it generates has good continuity and low noise. It has a high data update rate and good short-term accuracy and stability. However, since the navigation information is generated through integration, the positioning error increases with time, the long-term accuracy is poor, and time information cannot be given.

[0018] Pedestrian Dead Reckoning (PDR) mainly uses the Inertial Measurement Unit (IMU) to sense the acceleration, angular velocity, magnetism, pressure and other data of people in the process of walking in a beacon-free environment, and uses these data to calculate the step length and direction of the walking person, so as to achieve the purpose of positioning and tracking the person.

[0019] Based on the above situation, the present application proposes a positioning method and electronic device in the absence of GPS signals.

[0020] like Figure 1 As shown, the method provided in the embodiment of the present application comprises the following steps:

[0021] Step 110, when it is determined that the received initial GPS signal is missing, obtaining GPS data, inertial navigation data, and pedestrian dead reckoning PDR data;

[0022] Step 120, performing a first fusion process on the GPS data and the inertial navigation data to obtain first fusion data;

[0023] Step 130, performing a second fusion process on the GPS data and the PDR data to obtain second fusion data;

[0024] Step 140: Perform a third fusion process on the first fused data and the second fused data to obtain third fused data.

[0025] Step 150: output prediction results and positioning results.

[0026] For step 110, the data to be acquired include: GPS data, inertial navigation data and pedestrian dead reckoning PDR data. When it is determined that the GPS signal is missing, using GPS alone for positioning will cause inaccurate positioning, so it is necessary to collect inertial navigation data and PDR data for positioning calibration.

[0027] Among them, for obtaining GPS data, the longitude and latitude information and the first speed data of GPS positioning are received. The longitude and latitude information is first transformed into coordinate values ​​under the geocentric rectangular coordinates, and then the coordinate values ​​under the geocentric rectangular coordinates are converted into coordinate values ​​under the northeast celestial coordinate system. Considering that the eccentricity of the earth is very small, certain approximate formula calculations can also be made to directly convert the longitude and latitude information into coordinate values ​​under the northeast celestial coordinate system. The coordinate value is used as the first position, and the first position and the first speed data are used as GPS data.

[0028] For acquiring inertial navigation data, based on Newton's laws of mechanics, a navigation coordinate system is formed using a gyroscope, a yaw angle is measured using a magnetometer, and acceleration is measured using an accelerometer; the acceleration is integrated with respect to time and transformed into the navigation coordinate system to obtain a second speed, the second speed is integrated with respect to time again to obtain a second position, and the second speed, yaw angle, and second position obtained in the navigation coordinate system are used as inertial navigation data.

[0029] For obtaining PDR data, the acceleration, angular rate and geomagnetic heading are obtained by combining the accelerometer, gyroscope and magnetometer; the step length, direction and third position calculated by the pedestrian dead reckoning algorithm according to the acceleration, angular rate and geomagnetic heading are used as PDR data.

[0030] There are usually two models for estimating step length: static model and dynamic model. The static model assumes that any valid step length is a constant value, and the dynamic model assumes that any valid step has a different step length. Using the dynamic model to calculate the step length, it is assumed that the change in acceleration during walking is proportional to the step length. In a cycle, the change in acceleration can be expressed as the difference between the peak and the valley.

[0031] The step length at the current moment can be calculated based on the difference between the peak value and the valley value.

[0032] The angle between the projection of the terminal's Y axis onto the horizontal plane and the magnetic North Pole is used as the direction angle.

[0033] For the current position, the step size at the previous moment is multiplied by the cosine or sine value of the direction angle at the previous moment, and then the product is added to the position at the previous moment to obtain the current position.

[0034] Specifically, step 120 includes:

[0035] 1201, constructing a first state equation using the inertial navigation data as a state variable and obtaining a first state matrix;

[0036] 1202, using the GPS data and the inertial navigation data as first observation values;

[0037] 1203, initialize the first state matrix, calculate the first state matrix at the current moment according to the first state matrix at the previous moment to obtain a first a priori state estimate value and calculate the first a priori estimate covariance;

[0038] 1204. Adjust the weights of the first priori state estimate and the first observation value according to the first priori estimate covariance to determine first fused data.

[0039] Since inertial navigation does not rely on external signals and has strong anti-interference capabilities, when there are a lot of interference factors such as metal and strong electromagnetic fields in the environment, the positioning data of inertial navigation will not be disturbed, and the accuracy of positioning can be guaranteed. In addition, the sampling rate of inertial sensors is high, the working speed is fast, and objects with good mobility can be accurately located. Through the first fusion of GPS data and inertial navigation data, the calibration of inertial navigation data to GPS data is realized, which improves the accuracy of positioning data.

[0040] The Kalman filter algorithm used not only filters out the noise of the measurement signal, but also combines previous estimates. Kalman filtering has been proven to be the optimal estimate in linear problems. Using Kalman filtering for fusion processing can improve the positioning accuracy and stability of mobile terminals.

[0041] For step 1201, the second speed, yaw angle and second position included in the inertial navigation data are used as state variables, and a first state equation is constructed according to the motion equation and the physical relationship:

[0042] X k1 =Φ1X k1-1 +Γ k1 / k1-1 W k1-1

[0043] Among them, X k1 represents the first state matrix at time k; Φ1 represents the first system structure parameter; X k1-1 represents the first state matrix at time k-1; Γ k1 / k1-1 represents the first state transfer matrix; W k1-1represents the first system noise.

[0044] For step 1202, the GPS data and the inertial navigation data are used as first observation values ​​to construct a first observation equation:

[0045] Z k1 =H k1 X k1 +V k1

[0046] Among them, Z k1 represents the first observation vector at time k; H k1 represents the first transformation matrix; V k1 represents the first observation noise.

[0047] The first state equation and the first observation equation constitute a first linear stochastic system model.

[0048] For step 1203, the state prediction equation is used to predict the state at the next moment:

[0049]

[0050] in, represents the first prior state estimate at time k; Represents the first a posteriori state estimate at time k-1.

[0051] The state is further predicted using the mean square error equation to calculate the first prior estimate covariance at time k:

[0052]

[0053] Among them, P k1 / k1-1 represents the first prior estimate covariance at time k; P k1-1 represents the first a posteriori estimated covariance at time k-1; Q k1-1 represents the first process excitation noise covariance.

[0054] For step 1204, the state estimation equation is used to calculate the state at the current moment:

[0055]

[0056] in, K represents the first a posteriori state estimate at time k; k1 represents the first filter gain matrix.

[0057] Income The first fusion data includes: a first fusion speed, a first fusion position, and a first fusion yaw angle.

[0058] Among them, the first filter gain matrix is ​​calculated using the filter gain equation:

[0059]

[0060] Among them, R k1 represents the first measurement noise covariance.

[0061] The first a posteriori estimated covariance at time k is calculated using the state estimation mean square error equation:

[0062] P k1 =(IK k1 H k1 ) k1 / k1-1

[0063] Among them, P k1 represents the first a posteriori estimated covariance at time k.

[0064] Specifically, step 130 includes:

[0065] 1301, constructing a second state equation using the PDR data as a state variable and obtaining a second state matrix;

[0066] 1302, the GPS data and the PDR data are used as second observation values;

[0067] 1303, initializing the second state matrix, calculating the second state matrix at the current moment according to the second state matrix at the previous moment to obtain a second priori state estimate value and calculating a second priori estimate covariance;

[0068] 1304. Adjust the weights of the second priori state estimate and the second observation value according to the second priori estimate covariance to determine second fused data.

[0069] Since inertial navigation has the defect that the positioning error gradually increases with the positioning time, in an environment where the GPS signal is interfered for a long time, using only inertial navigation to calibrate the GPS cannot maintain long-term positioning accuracy. Therefore, the GPS data and PDR data are fused for the second time, and the PDR data is used to calibrate the GPS data to ensure the stability of positioning.

[0070] For step 1301, the step length, direction and third position included in the PDR data are used as state variables, and a second state equation is constructed according to the motion equation and the physical relationship:

[0071] X k2 =Φ2X k2-1 +Γ k2 / k2-1 W k2-1

[0072] Among them, Xk2 represents the second state matrix at time k; Φ2 represents the second system structure parameter; X k2-1 represents the second state matrix at time k-1; Γ k2 / k2-1 represents the second state transfer matrix; W k2-1 represents the second system noise.

[0073] For step 1302, the GPS data and the PDR data are used as second observation values ​​to construct a second observation equation:

[0074] Z k2 =H k2 X k2 +V k2

[0075] Among them, Z k2 represents the second observation vector at time k; H k2 represents the second transformation matrix; V k2 represents the second observation noise.

[0076] The second state equation and the second measurement equation constitute a second linear stochastic system model.

[0077] For step 1203, the state prediction equation is used to predict the state at the next moment:

[0078]

[0079] in, represents the second prior state estimate at time k; Represents the second a posteriori state estimate at time k-1.

[0080] The state is further predicted using the mean square error equation to calculate the second prior estimate covariance at time k:

[0081]

[0082] Among them, P k2 / k2-1 represents the second prior estimate covariance at time k; P k2-1 represents the second posterior estimated covariance at time k-1; Q k2-1 represents the second process excitation noise covariance.

[0083] For step 1304, the state estimation equation is used to calculate the state at the current moment:

[0084]

[0085] in, K represents the second a posteriori state estimate at time k; k2 represents the second filter gain matrix.

[0086] Income The second fusion data includes: a second fusion position, a second fusion speed, a first fusion step length, and a first fusion direction.

[0087] Among them, the second filter gain matrix is ​​calculated using the filter gain equation:

[0088]

[0089] Among them, R k2 represents the second measurement noise covariance.

[0090] The second a posteriori estimated covariance at time k is calculated using the state estimation mean square error equation:

[0091] P k2 =(IK k2 H k2 ) k2 / k2-1

[0092] Among them, P k2 represents the second a posteriori estimated covariance at time k.

[0093] Specifically, step 140 includes:

[0094] 1401, constructing a third state equation using the first fused data as a state variable and obtaining a third state matrix;

[0095] 1402, the first fused data and the second fused data are used as third observation values;

[0096] 1403, initializing the third state matrix, calculating the third state matrix at the current moment according to the third state matrix at the previous moment to obtain a third priori state estimate and calculating the third priori estimate covariance;

[0097] 1404. Adjust the weights of the third priori state estimate and the third observation value according to the third priori estimate covariance to determine third fused data.

[0098] The third fusion processing of the first fusion data and the second fusion data further improves the accuracy of positioning and maintains the stability of positioning. Inertial navigation and PDR calibrate GPS respectively, and the two calibration results are fused to reduce the error of the final positioning result. The positioning obtained by this method not only greatly improves the accuracy of positioning, but also continuously ensures stable high-precision positioning, and can be used for a long time in an environment where the GPS signal is interfered.

[0099] For step 1401, the first fused data is used as the state variable, and the third state equation is constructed according to the motion equation and the physical relationship:

[0100] X k3 =Φ3X k3-1 +Γ k3 / k3-1 W k3-1

[0101] Among them, X k3 represents the third state matrix at time k; Φ3 represents the third system structure parameter; X k3-1 represents the third state matrix at time k-1; Γ k3 / k3-1 represents the third state transfer matrix; W k3-1 Represents the third system noise.

[0102] For step 1402, the first fused data and the second fused data are used as third observation values ​​to construct a third observation equation:

[0103] Z k3 =H k3 X k3 +V k3

[0104] Among them, Z k3 represents the third observation vector at time k; H k3 represents the third transformation matrix; V k3 represents the third observation noise.

[0105] The third state equation and the third measurement equation constitute a third linear stochastic system model.

[0106] For step 1403, the state prediction equation is used to predict the state at the next moment:

[0107]

[0108] in, represents the third prior state estimate at time k; Represents the third a posteriori state estimate at time k-1.

[0109] The state is further predicted using the mean square error equation to calculate the third prior estimate covariance at time k:

[0110]

[0111] Among them, P k3 / k3-1 represents the third prior estimate covariance at time k; P k3-1 represents the third posterior estimated covariance at time k-1; Q k3-1 represents the third process excitation noise covariance.

[0112] For step 1404, the state estimation equation is used to calculate the state at the current moment:

[0113]

[0114] Income The third fusion data includes: a third fusion position, a third fusion speed, a second fusion direction, a second fusion step length, and a second fusion yaw angle.

[0115] in, K represents the third a posteriori state estimate at time k; k3 represents the third filter gain matrix.

[0116] Among them, the third filter gain matrix is ​​calculated using the filter gain equation:

[0117]

[0118] Among them, R k3 represents the third measurement noise covariance.

[0119] The third a posteriori estimated covariance at time k is calculated using the state estimation mean square error equation:

[0120] P k3 =(IK k3 H k3 ) k3 / k3-1

[0121] Among them, P k3 Represents the third a posteriori estimated covariance at time k.

[0122] For step 150, the third priori state estimation value is used as a prediction result, including: predicted speed, predicted position, predicted step length, predicted direction and predicted yaw angle, and the prediction result is output in the form of an image.

[0123] The third fusion data, as a positioning result, includes a third fusion position, a third fusion speed, a second fusion direction, a second fusion step length, and a second fusion yaw angle, and the positioning result is output in the form of an image.

[0124] The positioning results are output in the form of images, which can intuitively display the location of the personnel and facilitate location query.

[0125] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0126] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides a positioning device in the absence of a GPS signal.

[0128] refer to Figure 2 , the positioning device in the case of GPS signal loss includes:

[0129] The acquisition module 210 is configured to acquire GPS data, inertial navigation data, and pedestrian dead reckoning PDR data when it is determined that the received initial GPS signal is missing;

[0130] The fusion processing module 220 is configured to perform a first fusion process on the GPS data and the inertial navigation data to obtain first fusion data; perform a second fusion process on the GPS data and the PDR data to obtain second fusion data; and perform a third fusion process on the first fusion data and the second fusion data to obtain third fusion data;

[0131] The output module 230 is configured to output prediction results and positioning results.

[0132] The acquisition module 210 includes:

[0133] The GPS data acquisition unit is configured to receive the longitude and latitude information and the first speed data of the GPS positioning; convert the longitude and latitude information into coordinate values ​​corresponding to the northeast sky coordinate system, and use the coordinate values ​​as the first position; use the first position and the first speed data as GPS data;

[0134] an inertial navigation data acquisition unit, configured to measure the yaw angle and acceleration in combination with a magnetometer, a gyroscope and an accelerometer; integrate the acceleration with respect to time, transform the calculation result into a navigation coordinate system, and obtain a second speed, a yaw angle and a second position in the navigation coordinate system as inertial navigation data;

[0135] The PDR data acquisition unit is configured to acquire acceleration, angular rate and geomagnetic heading in combination with an accelerometer, a gyroscope and a magnetometer; and calculate the step length, direction and third position as PDR data using a pedestrian dead reckoning algorithm according to the acceleration, angular rate and geomagnetic heading.

[0136] The fusion processing module 220 includes:

[0137] The first fusion processing unit is configured to construct a first state equation using the inertial navigation data as a state variable and obtain a first state matrix; use the GPS data and the inertial navigation data as first observation values; initialize the first state matrix, calculate the first state matrix at the current moment according to the first state matrix at the previous moment to obtain a first priori state estimate and calculate a first priori estimate covariance; adjust the weights of the first priori state estimate and the first observation value according to the first priori estimate covariance to determine the first fusion data;

[0138] The second fusion processing unit is configured to construct a second state equation with the PDR data as a state variable and obtain a second state matrix; the GPS data and the PDR data are used as second observation values; the second state matrix is ​​initialized, and the second state matrix at the current moment is calculated according to the second state matrix at the previous moment to obtain a second priori state estimate and calculate a second priori estimate covariance; according to the second priori estimate covariance, the weights of the second priori state estimate and the second observation value are adjusted to determine the second fusion data;

[0139] The third fusion processing unit is configured to construct a third state equation with the first fusion data as a state variable and obtain a third state matrix; the first fusion data and the second fusion data are used as third observation values; the third state matrix is ​​initialized, and the third state matrix at the current moment is calculated according to the third state matrix at the previous moment to obtain a third priori state estimate and calculate a third priori estimate covariance; the weights of the third priori state estimate and the third observation value are adjusted according to the third priori estimate covariance to determine the third fusion data.

[0140] The output module 230 includes:

[0141] A prediction result output unit, configured to output the predicted position information and the predicted speed information contained in the third priori state estimation value in the form of an image to obtain a prediction result;

[0142] The positioning result output unit is configured to output the real-time position information and real-time speed information contained in the third fusion data in the form of an image to obtain a positioning result.

[0143] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0144] The device of the above embodiment is used to implement the corresponding beneficial effects of the positioning method embodiment in the absence of GPS signals in any of the above embodiments, and will not be described in detail here.

[0145] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, positioning in the absence of GPS signals as described in any of the above embodiments is achieved.

[0146] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 310, a memory 320, an input / output interface 330, a communication interface 340, and a bus 350. The processor 310, the memory 320, the input / output interface 330, and the communication interface 340 are connected to each other in communication within the device through the bus 350.

[0147] The processor 310 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0148] The memory 320 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 320 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 320 and are called and executed by the processor 310.

[0149] The input / output interface 330 is used to connect the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0150] The communication interface 340 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0151] The bus 350 comprises a pathway that transmits information between the various components of the device (eg, the processor 310 , the memory 320 , the input / output interface 330 , and the communication interface 340 ).

[0152] It should be noted that, although the above device only shows the processor 310, the memory 320, the input / output interface 330, the communication interface 340 and the bus 350, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0153] The electronic device of the above embodiment is used to implement the positioning method in the absence of the corresponding GPS signal in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0154] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the positioning method in the absence of GPS signals as described in any of the above embodiments.

[0155] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic 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.

[0156] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the positioning method in the absence of GPS signals as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0157] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0158] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0159] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0160] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A positioning method in the absence of GPS signals, characterized in that: include: When it is determined that the initial GPS signal received is missing, GPS data, inertial navigation data, and pedestrian dead reckoning PDR data are obtained; Performing a first fusion process on the GPS data and the inertial navigation data to obtain first fusion data; Performing a second fusion process on the GPS data and the PDR data to obtain second fusion data, so as to calibrate the GPS data; The first fused data and the second fused data are subjected to a third fusion process to obtain a prediction result and a positioning result; The specific steps of performing a second fusion process on the GPS data and the PDR data to obtain the second fusion data are as follows: Using the PDR data as a state variable, construct a second state equation and obtain a second state matrix; The GPS data and the PDR data are used as second observation values; Initializing the second state matrix, calculating the second state matrix at the current moment according to the second state matrix at the previous moment to obtain a second a priori state estimate and calculating the second a priori estimate covariance; Adjust the weights of the second priori state estimate and the second observation value according to the second priori estimate covariance to determine the second fused data; The specific steps of performing a third fusion process on the first fusion data and the second fusion data to obtain a prediction result and a positioning result are: Using the first fused data as a state variable, constructing a third state equation and obtaining a third state matrix; The first fused data and the second fused data are used as third observation values; Initializing the third state matrix, calculating the third state matrix at the current moment according to the third state matrix at the previous moment to obtain a third priori state estimate and calculating the third priori estimate covariance; Adjust the weights of the third priori state estimate and the third observation value according to the third priori estimate covariance to determine third fused data; A prediction result is output according to the third priori state estimation value, and a positioning result is output according to the third fusion data.

2. The method according to claim 1, characterized in that The specific steps of obtaining GPS data are: Receiving the latitude and longitude information and first speed data of GPS positioning; Convert the longitude and latitude information into coordinate values ​​corresponding to the northeast celestial coordinate system, and use the coordinate values ​​as the first position; The first position and the first speed data are used as GPS data.

3. The method according to claim 1, characterized in that The specific steps of obtaining inertial navigation data are: Combine magnetometer, gyroscope and accelerometer to measure yaw angle and acceleration; The acceleration is integrated with respect to time, and the calculation result is transformed into a navigation coordinate system, and the obtained second speed, yaw angle and second position in the navigation coordinate system are used as inertial navigation data.

4. The method according to claim 1, characterized in that The specific steps of obtaining PDR data are: Combine accelerometer, gyroscope and magnetometer to obtain acceleration, angular rate and geomagnetic heading; The step length, direction and third position calculated by a pedestrian dead reckoning algorithm according to the acceleration, angular rate and geomagnetic heading are used as PDR data.

5. The method according to claim 1, characterized in that The first fusion processing of the GPS data and the inertial navigation data to obtain the first fusion data comprises the following specific steps: Using the inertial navigation data as a state variable, constructing a first state equation and obtaining a first state matrix; Using the GPS data and the inertial navigation data as first observation values; Initializing the first state matrix, calculating the first state matrix at the current moment according to the first state matrix at the previous moment to obtain a first a priori state estimate and calculating the first a priori estimate covariance; The first fusion data is determined by adjusting the weights of the first priori state estimate and the first observation value according to the first priori estimate covariance.

6. The method according to claim 1, characterized in that The predicted position information and the predicted speed information included in the third priori state estimation value are output in the form of an image to obtain a prediction result.

7. The method according to claim 1, characterized in that The real-time position information and real-time speed information contained in the third fusion data are output in the form of an image to obtain a positioning result.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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