Positioning Method, Device, Equipment and Product for Indoor Target Vehicle

Abstract

The present invention discloses a positioning method, device, equipment and product for an indoor target vehicle. By establishing a measurement model and a missed detection model, local estimation value models of two sensors are obtained. Further, according to the local estimation value models of the two sensors and set preset constraint conditions, an estimated value model of the indoor target vehicle positioning information is obtained and the indoor target vehicle is positioned by using the estimated value model. The present invention discloses a positioning method, device, equipment and product for an indoor target vehicle, which has the characteristics of high positioning accuracy, high reliability and small calculation amount for the indoor target vehicle.

Description

Technical Field

[0001] The present invention belongs to the field of target positioning, and in particular, relates to a positioning method, device, equipment, and product for an indoor target vehicle. Background Art

[0002] The positioning of indoor unmanned ground vehicles is widely applied in fields such as environmental monitoring, environmental search, intelligent logistics, etc. Reliable and accurate positioning information ensures the safety and intelligence of the target vehicle. Due to different positioning principles, the positioning effects of different types of sensors are also different. For example, in harsh lighting conditions, low-quality visual information is obtained, reducing the positioning performance of camera sensors; due to the influence of measurement noise, the positioning of ultra-wideband (UWB) sensors is prone to generating non-smooth positioning trajectories; an inertial measurement unit (IMU) can obtain acceleration and angular velocity, but there are problems of cumulative and offset errors in target positioning. Therefore, when using a single sensor for indoor target vehicle positioning, there are problems of poor positioning accuracy and weak environmental adaptability. Summary of the Invention

[0003] In view of this, the present invention aims to overcome the defects in the prior art and proposes a positioning method, device, equipment, and product for an indoor target vehicle.

[0004] To achieve the above object, the technical solution of the present invention is realized as follows:

[0005] In a first aspect, the present invention discloses a positioning method for an indoor target vehicle, including:

[0006] According to the two-dimensional motion models corresponding to the first sensor and the second sensor, the measurement models of the first sensor and the second sensor are respectively established. The two-dimensional motion model is used to characterize the change relationship between the position and speed of the target vehicle; the measurement model is used to characterize the measurement value of the corresponding sensor for indoor target vehicle positioning.

[0007] The undetected models of the first sensor and the second sensor are respectively established. The undetected model is used to characterize whether the corresponding sensor outputs measurement information.

[0008] According to the two-dimensional motion models, measurement models, and undetected models corresponding to the first sensor and the second sensor, the local estimator models of the first sensor and the second sensor are respectively established. The local estimator model is used to characterize the estimated value of the corresponding sensor for indoor target vehicle positioning.

[0009] According to the preset constraint conditions and the two local estimator models, an estimator model capable of obtaining the positioning information of the indoor target vehicle based on the measurement information of the first sensor and the second sensor is obtained, and the indoor target vehicle is positioned using the estimator model.

[0010] In one embodiment of the present invention, local estimator models of the first sensor and the second sensor are respectively established according to the two-dimensional motion models, measurement models, and missed detection models corresponding to the first sensor and the second sensor, including: aligning the estimated values output by the two local estimator models in sampling time.

[0011] In one embodiment of the present invention, the preset constraint conditions include: the distance between the coordinate origin of the first sensor and the coordinate origin of the second sensor is fixed, and the speeds of the first sensor and the second sensor for measuring the target vehicle are the same.

[0012] In one embodiment of the present invention, the missed detection model is a binary function.

[0013] In one embodiment of the present invention, when the value of the binary function is 1, it indicates that the corresponding sensor has output measurement information; when the value of the binary function is 0, it indicates that the corresponding sensor does not have output measurement information.

[0014] In one embodiment of the present invention, an estimated value model for obtaining the positioning information of the indoor target vehicle is obtained and the indoor target vehicle is positioned by using the estimated value model, including: obtaining the estimated value and covariance of the positioning of the indoor target vehicle by using the estimated value model.

[0015] In a second aspect, the present invention discloses a positioning device for an indoor target vehicle, and the device includes:

[0016] A measurement model generation module, configured to respectively establish measurement models of the first sensor and the second sensor according to the two-dimensional motion models corresponding to the first sensor and the second sensor, where the two-dimensional motion model is used to characterize the change relationship between the position and speed of the target vehicle; the measurement model is used to characterize the measurement value of the corresponding sensor for positioning the indoor target vehicle;

[0017] A missed detection model generation module, configured to respectively establish missed detection models of the first sensor and the second sensor, where the missed detection model is used to characterize whether the corresponding sensor outputs measurement information;

[0018] A local estimator model generation module, configured to respectively establish local estimator models of the first sensor and the second sensor according to the two-dimensional motion models, measurement models, and missed detection models corresponding to the first sensor and the second sensor, where the local estimator model is used to characterize the estimated value of the corresponding sensor for positioning the indoor target vehicle;

[0019] A positioning module, configured to obtain an estimated value model capable of obtaining the positioning information of the indoor target vehicle according to the preset constraint conditions and the two local estimator models, and use the estimated value model to position the indoor target vehicle according to the measurement information of the first sensor and the second sensor.

[0020] In a third aspect, the present invention discloses an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above method.

[0021] In a fourth aspect, the present invention discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0022] In a fifth aspect, a computer program product comprises a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] The present invention discloses a positioning method, device, equipment and product for an indoor target vehicle. By establishing a measurement model and a missed detection model, local estimator models of two sensors are obtained. Further, according to the local estimator models of the two sensors and set preset constraint conditions, an estimated value model of the indoor target vehicle positioning information is obtained, and the indoor target vehicle is positioned by using the estimated value model. The present invention discloses a positioning method, device, equipment and product for an indoor target vehicle, which has the characteristics of high positioning accuracy, high reliability and small calculation amount for the indoor target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0026] In the drawings:

[0027] Figure 1 is a schematic diagram of an application scenario of a positioning method for an indoor target vehicle according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of a positioning method for an indoor target vehicle according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of the principle of a positioning method for an indoor target vehicle according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of a positioning device for an indoor target vehicle according to an embodiment of the present invention;

[0031] Figure 5 is a schematic diagram of a positioning electronic device for an indoor target vehicle according to an embodiment of the present invention;

[0032] Figure 6Schematic diagram of the position trajectory of a positioning method for an indoor target vehicle according to an embodiment of the present invention;

[0033] Figure 7 Schematic diagram of the position error covariance of a positioning method for an indoor target vehicle according to an embodiment of the present invention;

[0034] Figure 8 Schematic diagram of the speed error covariance of a positioning method for an indoor target vehicle according to an embodiment of the present invention. Detailed implementation manners

[0035] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0036] In the description of the present invention, it should be further noted that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0037] The application scenarios of a positioning method, device, equipment and product for an indoor target vehicle disclosed by the present invention are as Figure 1 shown. Under the existing technical conditions, due to different positioning principles, the positioning effects of different types of sensors are different. For example, in harsh lighting conditions, the obtained low-quality visual information reduces the positioning performance of the camera sensor; due to the influence of measurement noise, the positioning of the ultra-wideband UWB sensor is prone to generate an uneven positioning trajectory; the inertial measurement unit IMU can obtain acceleration and angular velocity, but there are problems of cumulative and offset errors in target positioning. Therefore, when a single sensor is used for positioning an indoor target vehicle, there are problems of poor positioning accuracy and weak environmental adaptability. A positioning method, device, equipment and product for an indoor target vehicle disclosed by the present invention obtains local estimator models of two sensors by establishing a measurement model and a missed detection model, and further obtains an estimated value model of the positioning information of the indoor target vehicle according to the local estimator models of the two sensors and the set preset constraint conditions, and uses the estimated value model to position the indoor target vehicle, which has the characteristics of high positioning accuracy, high reliability and small calculation amount for the indoor target vehicle.

[0038] The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0039] In an embodiment of the present invention, as Figure 2 shown, a positioning method for an indoor target vehicle includes:

[0040] Step S201: According to the two-dimensional motion models corresponding to the first sensor and the second sensor, establish the measurement models of the first sensor and the second sensor respectively. The two-dimensional motion model is used to characterize the change relationship between the position and speed of the target vehicle; the measurement model is used to characterize the measurement value of the corresponding sensor for locating the indoor target vehicle.

[0041] Exemplarily, the first sensor is a camera sensor, and the second sensor is an ultra-wideband UWB sensor. The two-dimensional motion model is expressed as follows:

[0042]

[0043] Where represents the camera sensor, represents the ultra-wideband UWB sensor, is the target state, and are respectively the position and speed in the longitudinal direction, and are respectively the position and speed in the transverse direction, and are respectively the state update times of the camera sensor and the ultra-wideband UWB sensor. In this embodiment, represents the sampling period of the camera sensor, represents the sampling period of the ultra-wideband UWB sensor; represents the process noise, which is Gaussian white noise with zero mean; the covariance is known and expressed as where , is a 4×4 identity matrix;

[0044] Exemplarily, in this embodiment, s and s;

[0045] The measurement model is expressed as :

[0046]

[0047] represents the camera sensor, represents the ultra-wideband UWB sensor; and are respectively white noises with zero mean and covariances of and ; , ; I 2 is a 2×2 identity matrix.

[0048] Step S202: Establish the undetected models of the first sensor and the second sensor respectively. The undetected model is used to characterize whether the corresponding sensor outputs measurement information.

[0049] In this embodiment, the undetected model is a binary function.

[0050] In this embodiment, when the value of the binary function is 1, it indicates that the corresponding sensor has output measurement information; when the value of the binary function is 0, it indicates that the corresponding sensor does not have output measurement information, that is, undetected.

[0051] Exemplarily, the binary function is expressed as ;

[0052]

[0053] Step S203: As Figure 2 and Figure 3 shown, according to the two-dimensional motion models, measurement models and undetected models corresponding to the first sensor and the second sensor, establish the local estimator models of the first sensor and the second sensor respectively. The local estimator model is used to characterize the estimated value of the corresponding sensor for the positioning of the indoor target vehicle.

[0054] Exemplarily, the estimated value of the local estimator model is expressed as , the covariance is expressed as , and the local estimator model is expressed as follows:

[0055]

[0056] Where and are the predicted estimated value and its covariance respectively, and are the innovation and the covariance of the innovation respectively, is the estimator gain, is the identity matrix.

[0057] Step S204: As Figure 2 and Figure 3 shown, according to the preset constraint conditions and the two local estimator models, obtain an estimated value model that can obtain the positioning information of the indoor target vehicle based on the measurement information of the first sensor and the second sensor, and use the estimated value model to position the indoor target vehicle.

[0058] In this embodiment, obtaining an estimated value model for the positioning information of the indoor target vehicle and using the estimated value model to position the indoor target vehicle includes: using the estimated value model to obtain the estimated value and covariance of the positioning of the indoor target vehicle.

[0059] In this embodiment, the estimated value of the estimated value model is expressed as , the covariance is expressed as :

[0060]

[0061]

[0062]

[0063]

[0064] Wherein, is the covariance of the estimated value of the local estimator model of the ultra-wideband UWB sensor, is the covariance of the estimated value of the local estimator model of the camera sensor, is the cross-covariance of the two; and are respectively the longitudinal distance and the lateral distance from the origin of the coordinate system in the camera sensor to the origin of the coordinate system in the ultra-wideband UWB sensor; I 4 is a 4×4 identity matrix.

[0065] Based on the previous embodiment, in another embodiment of the present invention, according to the two-dimensional motion model, measurement model and missed detection model corresponding to the first sensor and the second sensor, local estimator models of the first sensor and the second sensor are respectively established, including: aligning the estimated values output by the two local estimator models in sampling time.

[0066] Exemplarily, since the sampling periods of the camera sensor and the ultra-wideband UWB sensor are inconsistent, and the sampling frequency of the camera sensor is high, the estimated value of the ultra-wideband UWB sensor is predicted according to the motion model during the time period, and aligned with the estimated value result of the local estimator model of the camera sensor in time, then the estimated value of the ultra-wideband UWB sensor at moment is:

[0067]

[0068]

[0069] In this embodiment, aligning in sampling time can effectively improve the fusion effect of the first sensor and the second sensor and improve the final positioning accuracy of the target vehicle.

[0070] Based on the previous embodiment, in another embodiment of the present invention, the preset constraint conditions include: the distance between the coordinate origin of the first sensor and the coordinate origin of the second sensor is fixed, and the speeds of the first sensor and the second sensor for measuring the target vehicle are the same.

[0071] Exemplarily, and are respectively the longitudinal distance and the lateral distance from the origin of the coordinate system in the camera sensor to the origin of the coordinate system in the ultra-wideband (UWB) sensor. Since the picture tags to be recognized by the camera and the positions of the ultra-wideband (UWB) sensors are both known and fixed a priori, the distance constraint conditions are as follows:

[0072]

[0073] where the position coordinate is the position in the coordinate system with the camera sensor as the origin of the coordinate, and the position coordinate is the position in the coordinate system with the ultra-wideband (UWB) sensor as the origin of the coordinate.

[0074] Exemplarily, the speeds of the same indoor target vehicle measured by the camera sensor and the ultra-wideband (UWB) sensor should be the same. Therefore, the speed constraint conditions are as follows:

[0075]

[0076] is the longitudinal speed of the target vehicle measured by the ultra-wideband (UWB) sensor, is the longitudinal speed of the target vehicle measured by the camera sensor, is the lateral speed of the target vehicle measured by the ultra-wideband (UWB) sensor, is the longitudinal speed of the target vehicle measured by the camera sensor.

[0077] In this embodiment, the setting of the constraint conditions can improve the accuracy of the estimation value model and enhance the final positioning accuracy of the target vehicle.

[0078] As Figure 6 shown, this method is smoother and has less noise than the data of the single camera sensor and the data of the single ultra-wideband (UWB) sensor.

[0079] As Figure 7 shown, the estimated error covariance of this method decreases in terms of position, reflecting that the positioning accuracy of the fused target is higher than that of a single sensor.

[0080] As Figure 8 shown, the estimated error covariance of this method decreases in terms of speed, reflecting that the positioning accuracy of the fused target is higher than that of a single sensor.

[0081] As Figure 4 shown, the present invention also discloses a positioning device for an indoor target vehicle, including:

[0082] The measurement model generation module 401 is configured to establish measurement models for the first sensor and the second sensor respectively according to the two-dimensional motion models corresponding to the first sensor and the second sensor, where the two-dimensional motion model is used to characterize the variation relationship between the position and speed of the target vehicle; the measurement model is used to characterize the measurement values of the corresponding sensor for indoor target vehicle positioning.

[0083] The missed detection model generation module 402 is configured to establish missed detection models for the first sensor and the second sensor respectively, where the missed detection model is used to characterize whether the corresponding sensor outputs measurement information.

[0084] The local estimator model generation module 403 is configured to establish local estimator models for the first sensor and the second sensor respectively according to the two-dimensional motion models, measurement models and missed detection models corresponding to the first sensor and the second sensor, where the local estimator model is used to characterize the estimated values of the corresponding sensor for indoor target vehicle positioning.

[0085] The positioning module 404 is configured to obtain an estimated value model capable of obtaining indoor target vehicle positioning information according to the measurement information of the first sensor and the second sensor based on preset constraint conditions and two local estimator models, and use the estimated value model to perform positioning on the indoor target vehicle.

[0086] The present invention also discloses an electronic device, as Figure 5 shown, a block diagram of an embodiment of an electronic device applicable to the positioning of the above indoor target vehicle is disclosed.

[0087] The electronic device 50 in this embodiment includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the ROM 502 or the program loaded from the storage section 508 into the RAM 503. The processor 501 may include, for example, a general microprocessor, an instruction set processor, and / or a related chipset and / or a dedicated microprocessor, etc. The processor 501 may also include on-board memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiments of the present invention.

[0088] In the RAM 503, various programs and data required for the operation of the electronic device 50 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504, and the processor 501 performs various operations of the method flow according to the embodiments of the present invention by executing the programs in the ROM 502 and / or the RAM 503. It should be noted that the program may also be stored in one or more memories other than the ROM 502 and the RAM 503, and the processor 501 may also perform various operations of the method flow according to the embodiments of the present invention by executing the programs stored in one or more memories.

[0089] According to an embodiment of the present invention, the electronic device 50 may further include an I / O interface 505, and the I / O interface 505 is also connected to the bus 504. The electronic device 50 may further include one or more of the following components connected to the I / O interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube, a liquid crystal display, a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. The drive 5010 is also connected to the I / O interface 505 as needed. A removable medium 5011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 5010 as needed so that a computer program read from it is installed into the storage portion 508 as needed.

[0090] The present invention also provides a computer-readable storage medium.

[0091] The computer-readable storage medium may be included in the electronic device / device system described in the above embodiments; or it may exist separately without being assembled into the electronic device / device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present invention is implemented.

[0092] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory RAM, a read-only memory ROM, an erasable programmable read-only memory EPROM or a flash memory, a portable compact disk read-only memory CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0093] The embodiments of the present invention further include a computer program product.

[0094] The computer program product includes a computer program, and the computer program contains program codes for executing the method provided by the embodiments of the present invention. When the computer program product runs on an electronic device, the program codes are used to cause the electronic device to implement the method provided by the embodiments of the present invention.

[0095] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium. The program code included in the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0096] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiments of the present invention may be written in any combination of one or more programming languages. Specifically, these computing programs may be implemented using high-level procedures and / or object-oriented programming languages. Programming languages include but are not limited to, for example, Java, C++, Python, C language, or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device may be connected to the user's computing device through any type of network, including a local area network or a wide area network, or may be connected to an external computing device.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions. Those skilled in the art can understand that the features described in the various embodiments and / or claims of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments and / or claims of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0098] The embodiments of the present invention have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present invention is defined by the appended claims and their equivalents, and without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. A method for positioning a target vehicle indoors, characterized in that: include: According to the two-dimensional motion models corresponding to the first sensor and the second sensor, respectively establish the measurement models of the first sensor and the second sensor, wherein the two-dimensional motion model is used to characterize the changing relationship between the position and speed of the target vehicle; and the measurement model is used to characterize the measurement value of the corresponding sensor for positioning the indoor target vehicle; Establishing missed detection models of the first sensor and the second sensor respectively, wherein the missed detection models are used to characterize whether the corresponding sensors output measurement information; According to the two-dimensional motion model, the measurement model and the missed detection model corresponding to the first sensor and the second sensor, respectively establish a local estimator model of the first sensor and the second sensor, wherein the local estimator model is used to characterize an estimation value of the corresponding sensor for positioning the indoor target vehicle; According to the preset constraint conditions and the two local estimator models, an estimation value model capable of obtaining indoor target vehicle positioning information according to the measurement information of the first sensor and the second sensor is obtained, and the indoor target vehicle is positioned using the estimation value model; The estimated value of the local estimator model is expressed as , Indicates new information; ； ； ； in: ； ； represents the estimated value of the forecast, Indicates the missed detection value corresponding to the missed detection model, is the estimator gain, is the measurement value of the measurement model, represents the first sensor, represents the second sensor, Indicates the sampling period of the corresponding sensor, Indicates the status update time of the corresponding sensor; Align the estimated values ​​output by the two local estimator models at sampling time, indicating , the estimated value of the estimated value model is expressed as ; ； ； ； ； in, is the estimate of the local estimator model of the first sensor, is the estimated value covariance of the local estimator model of the first sensor, is the estimated value covariance of the local estimator model of the second sensor, is the cross covariance between the two, and are respectively the longitudinal distance and the lateral distance from the origin of the coordinate system in the first sensor to the origin of the coordinate system in the second sensor.

2. A method for positioning an indoor target vehicle according to claim 1, characterized in that: The preset constraint conditions include: a distance between a coordinate origin of the first sensor and a coordinate origin of the second sensor is fixed, and the speeds of the target vehicle measured by the first sensor and the second sensor are the same.

3. A method for positioning an indoor target vehicle according to claim 1, characterized in that: The missed detection model is a binary function.

4. A method for positioning an indoor target vehicle according to claim 3, characterized in that: When the value of the binary function is 1, it indicates that the corresponding sensor has output measurement information; when the value of the binary function is 0, it indicates that the corresponding sensor does not have output measurement information.

5. The method for positioning an indoor target vehicle according to claim 1, characterized in that: The method of obtaining an estimated value model for indoor target vehicle positioning information and using the estimated value model to locate the indoor target vehicle includes: using the estimated value model to obtain an estimated value and covariance for indoor target vehicle positioning.

6. A positioning device for an indoor target vehicle, characterized in that: The device comprises: A measurement model generation module, used to establish measurement models of the first sensor and the second sensor respectively according to the two-dimensional motion models corresponding to the first sensor and the second sensor, wherein the two-dimensional motion model is used to characterize the changing relationship between the position and speed of the target vehicle; and the measurement model is used to characterize the measurement value of the corresponding sensor for positioning the indoor target vehicle; A missed detection model generation module, used to respectively establish missed detection models of the first sensor and the second sensor, wherein the missed detection models are used to characterize whether the corresponding sensors output measurement information; A local estimator model generation module, used to establish local estimator models of the first sensor and the second sensor respectively according to the two-dimensional motion model, the measurement model and the missed detection model corresponding to the first sensor and the second sensor, wherein the local estimator model is used to characterize the estimation value of the corresponding sensor for the indoor target vehicle positioning; A positioning module, used to obtain an estimation value model capable of obtaining indoor target vehicle positioning information according to the measurement information of the first sensor and the second sensor according to preset constraints and the two local estimator models, and to locate the indoor target vehicle using the estimation value model; The estimated value of the local estimator model is expressed as , Indicates new information; ； ； ； in: ； ； represents the estimated value of the forecast, Indicates the missed detection value corresponding to the missed detection model, is the estimator gain, is the measurement value of the measurement model, represents the first sensor, represents the second sensor, Indicates the sampling period of the corresponding sensor, Indicates the status update time of the corresponding sensor; Align the estimated values ​​output by the two local estimator models at sampling time, indicating , the estimated value of the estimated value model is expressed as ; ； ； ； ； in, is the estimate of the local estimator model of the first sensor, is the estimated value covariance of the local estimator model of the first sensor, is the estimated value covariance of the local estimator model of the second sensor, is the cross covariance between the two, and are respectively the longitudinal distance and the lateral distance from the origin of the coordinate system in the first sensor to the origin of the coordinate system in the second sensor.

7. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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