An assisted positioning method, apparatus, device, and medium

Abstract

The application discloses an auxiliary positioning method, device, equipment and medium. The method comprises the following steps: simulating a decision mechanism of a tamarin when a satellite signal is lost; a leader predicts a target area where a vehicle is located according to real-time feedback information of the vehicle; and a follower densely searches in the target area until a target position of the vehicle in the target area is determined or the satellite signal is recovered. The embodiment of the application can realize accurate positioning in the case of lost satellite signal.

Description

Technical Field

[0001] This invention relates to the field of vehicle positioning technology, and in particular to an auxiliary positioning method, device, equipment and medium. Background Technology

[0002] Currently, in complex environments such as densely populated urban areas with high-rise buildings, tunnels, and underground parking lots where GPS signals are limited or completely blocked, traditional positioning technologies often struggle to guarantee high-precision positioning results and may even fail to locate due to signal loss.

[0003] Traditional positioning technologies often rely on external GPS signals or fixed infrastructure, making it difficult to cope with complex and ever-changing driving environments. In particular, their positioning performance can be severely affected in extreme weather or signal interference conditions. Summary of the Invention

[0004] This invention provides an auxiliary positioning method, device, equipment, and medium to achieve accurate positioning in the event of lost satellite signals.

[0005] According to one aspect of the present invention, an assisted positioning method is provided, comprising:

[0006] When satellite signals are lost, the decision-making mechanism of capuchin monkeys is simulated, and the leader predicts the target area where the vehicle is located based on the real-time feedback information from the vehicle.

[0007] The follower conducts an intensive search in the target area until the vehicle's target location in the target area is determined or the satellite signal is restored.

[0008] According to another aspect of the present invention, an auxiliary positioning device is provided, comprising:

[0009] The search start module is used to simulate the decision-making mechanism of capuchin monkeys when satellite signals are lost, and the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle.

[0010] The search termination module is used by the follower to conduct an intensive search in the target area until the target location of the vehicle in the target area is determined or the satellite signal is restored.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the assisted positioning method according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the assisted positioning method according to any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the assisted positioning method according to any embodiment of the present invention.

[0014] This invention introduces a capuchin monkey algorithm to simulate the efficient path selection behavior of capuchin monkeys during foraging. In vehicle positioning, when GPS signals are obstructed by complex environments such as tunnels and underground parking lots, this algorithm can integrate data from multiple vehicle sensors, and through intelligent analysis and optimization, reduce the impact of signal loss on vehicle positioning, thereby achieving more accurate and stable vehicle positioning.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of an auxiliary positioning method provided according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart of an auxiliary positioning method provided according to another embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of an auxiliary positioning method provided according to another embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Figure 1 This is a flowchart illustrating an assisted positioning method according to an embodiment of the present invention. This embodiment is applicable to situations where a vehicle cannot accurately locate itself due to satellite signal loss during movement. The method can be executed by an assisted positioning device, which can be implemented in hardware and / or software. This device can be configured in an electronic device with corresponding data processing capabilities, such as an in-vehicle positioning system. Figure 1 As shown, the method includes:

[0024] S110. When satellite signals are lost, the decision-making mechanism of capuchin monkeys is simulated, and the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle.

[0025] S120, The follower performs an intensive search in the target area until the target location of the vehicle in the target area is determined or the satellite signal is restored.

[0026] The capuchin search algorithm (CapSA) is a novel intelligent optimization algorithm. By modeling the behavior of capuchin monkeys wandering and foraging in the forest, the algorithm's basic optimization characteristics are designed, exhibiting strong optimization ability and fast convergence speed. In this algorithm, the capuchin monkey population includes two roles: the leader, which is the individual with the best fitness value in the current population, representing the best solution found so far and guiding followers to search in a better direction; and the followers, which are the other individuals besides the leader, adjusting their positions according to the leader's guidance in order to find a better solution. Real-time feedback information is used to characterize the vehicle's state changes over continuous time.

[0027] Specifically, the vehicle's positioning is based primarily on the received satellite signals during its movement. When the satellite signal is lost due to the vehicle entering a tunnel, underground parking garage, or encountering extreme weather, the real-time feedback information of the vehicle at the time of the lost satellite signal is obtained. The leader then uses this real-time feedback information to predict the target area where the vehicle is located at that moment.

[0028] The follower performs an intensive search in the target area. If the candidate location found meets the output requirements (mainly the positioning accuracy requirements), the candidate location is output as the final target location of the vehicle in the target area. If the candidate location does not meet the output requirements, it is fed back to the leader, who is required to provide a new target area. The follower then performs an intensive search again in the new target area.

[0029] This iterative process will be repeated many times until the number of iterations reaches the upper limit. The candidate position obtained in the last iteration will be determined as the target position. If the candidate position meets the output requirements, the candidate position that meets the output requirements will be determined as the target position, or the satellite signal will be recovered and the positioning will rely on the satellite signal again.

[0030] This invention introduces a capuchin monkey algorithm to simulate the efficient path selection behavior of capuchin monkeys during foraging. In vehicle positioning, when GPS signals are obstructed by complex environments such as tunnels and underground parking lots, this algorithm can integrate data from multiple vehicle sensors, and through intelligent analysis and optimization, reduce the impact of signal loss on vehicle positioning, thereby achieving more accurate and stable vehicle positioning.

[0031] Based on the above embodiments, optionally, when the satellite signal is not lost, a capuchin monkey decision-making mechanism is simulated, in which the leader predicts the target area where the vehicle is located based on the vehicle's historical data and global information; the global information includes the satellite signal; and the followers conduct intensive searches in the target area to determine the target position of the vehicle in the target area.

[0032] Specifically, when satellite signals are not lost, a capuchin monkey decision-making mechanism can be simulated. The leader uses historical vehicle data and global information to conduct a large-scale search, assess the positioning accuracy of different areas, and determine the target area, which is the area most likely where the vehicle is located. Followers perform a local search within the target area, quickly converging to the optimal solution and determining the vehicle's target position within the target area. Compared to the capuchin monkey decision-making mechanism when satellite signals are lost, the leader now has the added positioning reference data of satellite signals. Simulating the capuchin monkey decision-making mechanism when satellite signals are not lost eliminates the need to reload the mechanism from scratch when satellite signals are still available, thus improving positioning efficiency.

[0033] Figure 2 This is a flowchart illustrating an auxiliary positioning method according to another embodiment of the present invention. This embodiment is an optimization and improvement upon the above embodiment. Figure 2 As shown, the method includes:

[0034] S210. Construct a historical dataset based on the vehicle's motion information and environmental information; based on the historical dataset, simulate the decision-making process and search process of capuchin monkeys when faced with multiple choices to obtain a first machine learning model and a second machine learning model, respectively.

[0035] S220. The first machine learning model is identified as the leader in the capuchin monkey decision-making mechanism, and the second machine learning model is identified as the follower in the capuchin monkey decision-making mechanism.

[0036] Specifically, in the initial data collection phase, vehicle motion and environmental information are acquired to construct a complete historical dataset. This historical dataset is then preprocessed to facilitate the subsequent extraction of key features such as road type and environmental changes from the processed data.

[0037] Data related to the leader's decision-making tasks is extracted from historical datasets, and a base machine learning model is trained based on this data to obtain a first machine learning model representing the leader. Similarly, data related to the followers' search tasks is extracted from historical datasets, and a second machine learning model is trained based on this data to obtain a second machine learning model representing the followers. Since the tasks of the leader and followers are relatively fixed, neural network models can be trained to simulate the decision-making process of capuchin monkeys when faced with multiple choices, learning how to choose the optimal path in complex environments.

[0038] S230. When satellite signals are lost, simulate the decision-making mechanism of capuchin monkeys. The leader determines the possible location and trajectory of the vehicle based on the real-time feedback information of the vehicle. The leader predicts the target area where the vehicle is located based on the possible location and trajectory.

[0039] Specifically, the leader uses real-time feedback to recursively determine the vehicle's possible location and trajectory at the current moment. Based on the vehicle's possible location and trajectory at the current moment, the leader sets a range (within xx square meters) and defines it as the vehicle's possible movement area in the next time period, i.e., the target area where the vehicle is located.

[0040] S240. The follower performs a dense search in the target area to obtain candidate locations;

[0041] S250. If the candidate location meets the preset positioning accuracy, the candidate location is determined as the target location of the vehicle in the prediction area; if the candidate location does not meet the preset positioning accuracy, the search results of the dense search are added to the real-time feedback information, and the execution leader is returned to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle, until the candidate location meets the preset positioning accuracy or the satellite signal is restored.

[0042] Specifically, the follower conducts an intensive search within the target area determined by the leader. This target area can be further subdivided using a grid-based method, gradually narrowing the search range and focusing on precise positioning within a smaller area. When the follower finds a vehicle location within the target area that meets a preset positioning accuracy (e.g., less than xx meters), this candidate location is determined as the vehicle's target location within the predicted area.

[0043] If the follower does not find a candidate location with the preset expected positioning accuracy within the current area, it will supplement the real-time feedback information with the search results of this intensive search (including candidate locations and search parameters) to provide feedback to the leader, requesting the leader to search for a new target area.

[0044] Furthermore, during the adjustment and optimization process, both the leader and the supervisor can continuously adjust the search strategy, such as adjusting the search step size, adjusting the search direction, or changing the precision of the grid division area, in order to adapt to environmental changes and improve the accuracy of positioning.

[0045] Based on the above embodiments, optionally, the historical data includes vehicle motion information, environmental information, and user vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent the changes in the vehicle's motion parameters in space.

[0046] Specifically, historical data includes vehicle motion information (such as position, speed, acceleration, and direction of travel), environmental information (road type, surrounding building information, obstacle information, satellite signal strength, etc.), and the owner's vehicle usage habits (such as preferred routes and frequently visited areas). Vehicle dynamic models can be constructed based on vehicle kinematics to represent the changes in the vehicle's position, speed, acceleration, and other motion parameters in space over time, thus providing a basis for predicting vehicle movement. This allows for a reasonable prediction of the vehicle's position even when satellite signals are lost.

[0047] This invention optimizes the iteration termination condition of the capuchin monkey decision-making mechanism to avoid excessive consumption of computing resources caused by the unlimited operation of the capuchin monkey decision-making mechanism.

[0048] Figure 3 This is a schematic diagram of an auxiliary positioning device provided in another embodiment of the present invention. Figure 3 As shown, the device includes:

[0049] Search start module 310 is used to simulate the decision-making mechanism of capuchin monkeys when satellite signals are lost, and the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle.

[0050] Search termination module 320 is used for the follower to perform intensive search in the target area until the target location of the vehicle in the target area is determined or the satellite signal is restored.

[0051] The auxiliary positioning device provided in the embodiments of the present invention can execute the auxiliary positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0052] Optionally, the real-time feedback information includes historical data and vehicle dynamic models, and the search start module 310 includes:

[0053] The position trajectory estimation unit is used by the leader to determine the possible position and trajectory of the vehicle based on real-time feedback information from the vehicle.

[0054] The location segmentation unit is used by the leader to predict the target area where the vehicle is located based on the possible location and movement trajectory.

[0055] Optionally, the search end module 320 includes:

[0056] The search unit is used by the follower to perform an intensive search in the target area to obtain candidate locations;

[0057] The first termination unit is used to determine the candidate position as the target position of the vehicle in the prediction area if the candidate position meets the preset positioning accuracy.

[0058] The second termination unit is used to supplement the search results of the dense search into the real-time feedback information if the candidate location does not meet the preset positioning accuracy, and return to the execution leader to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle, until the candidate location meets the preset positioning accuracy or the satellite signal is restored.

[0059] Optionally, the historical data includes vehicle motion information, environmental information, and user vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent the changes in the vehicle's motion parameters in space.

[0060] Optionally, the device further includes:

[0061] The dataset construction module is used to build historical datasets based on vehicle motion information and environmental information.

[0062] The decision learning module is used to simulate and learn the decision-making and search processes of capuchin monkeys when faced with multiple choices, based on the historical dataset, to obtain a first machine learning model and a second machine learning model, respectively.

[0063] The role determination module is used to determine the first machine learning model as the leader in the capuchin monkey decision-making mechanism and the second machine learning model as the follower in the capuchin monkey decision-making mechanism.

[0064] Optionally, the device further includes:

[0065] The region determination module is used to simulate the decision-making mechanism of capuchin monkeys when satellite signals are not lost. The leader predicts the target area where the vehicle is located based on the vehicle's historical data and global information, including satellite signals.

[0066] The location determination module is used by the follower to perform an intensive search in the target area to determine the target location of the vehicle in the target area.

[0067] The auxiliary positioning device described in further detail can also execute the auxiliary positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0068] Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0069] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0070] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0071] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as assisted localization methods.

[0072] In some embodiments, the assisted positioning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the assisted positioning method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the assisted positioning method by any other suitable means (e.g., by means of firmware).

[0073] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0075] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0076] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0077] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0078] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0079] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0080] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An auxiliary positioning method, characterized in that, The method includes: When satellite signals are lost, the decision-making mechanism of capuchin monkeys is simulated, and the leader predicts the target area where the vehicle is located based on the real-time feedback information from the vehicle. The follower conducts an intensive search in the target area until the vehicle's target location in the target area is determined or the satellite signal is restored; The real-time feedback information includes historical data and vehicle dynamic models. The leader predicts the target area where the vehicle is located based on the real-time feedback information, including: The leader determines the vehicle's possible location and trajectory based on real-time feedback information from the vehicle. The leader predicts the target area where the vehicle is located based on the possible location and movement trajectory; The follower conducts an intensive search of the target area until the vehicle's target location is determined or satellite signals are restored, including: The follower performs an intensive search within the target area to obtain candidate locations; If the candidate location meets the preset positioning accuracy, then the candidate location is determined as the target location of the vehicle in the predicted area; If the candidate location does not meet the preset positioning accuracy, the search results of the dense search are added to the real-time feedback information, and the execution leader is returned to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle, until the candidate location meets the preset positioning accuracy or the satellite signal is restored. Before the leader predicts the target area where the vehicle is located based on the vehicle's real-time feedback information, the following steps are also included: A historical dataset is constructed based on vehicle motion information and environmental information; Based on the historical dataset, the first machine learning model and the second machine learning model were obtained by simulating the decision-making and search processes of capuchin monkeys when faced with multiple choices. The first machine learning model is identified as the leader in the capuchin monkey decision-making mechanism, and the second machine learning model is identified as the follower in the capuchin monkey decision-making mechanism. Data related to the leader's decision-making tasks is extracted from historical datasets, and a basic machine learning model is trained based on this data to obtain a first machine learning model for the leader; data related to the follower's search tasks is extracted from historical datasets, and a basic machine learning model is trained based on this data to obtain a second machine learning model for the follower. During the adjustment and optimization process, both the leader and the follower can continuously adjust their search strategies, which include adjusting the search step size, adjusting the search direction, or changing the precision of the grid division area.

2. The method according to claim 1, characterized in that, The historical data includes vehicle motion information, environmental information, and user vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent the changes in the vehicle's motion parameters in space.

3. The method according to claim 1, characterized in that, The method further includes: Simulating a capuchin monkey decision-making mechanism when satellite signals are not lost, the leader predicts the target area where the vehicle is located based on historical vehicle data and global information, including satellite signals. The follower performs an intensive search within the target area to determine the target location of the vehicle within the target area.

4. An auxiliary positioning device, controlled by the auxiliary positioning method as described in any one of claims 1-3, characterized in that, The device includes: The search start module is used to simulate the decision-making mechanism of capuchin monkeys when satellite signals are lost, and the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle. The search termination module is used by the follower to conduct an intensive search in the target area until the target location of the vehicle in the target area is determined or the satellite signal is restored.

5. The apparatus according to claim 4, characterized in that, The real-time feedback information includes historical data and vehicle dynamic models, and the search start module includes: The position trajectory estimation unit is used by the leader to determine the possible position and trajectory of the vehicle based on real-time feedback information from the vehicle. The location segmentation unit is used by the leader to predict the target area where the vehicle is located based on the possible location and movement trajectory.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the assisted positioning method according to any one of claims 1-3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the assisted positioning method according to any one of claims 1-3.

Images