Multi-source cooperative positioning method, device and medium

Abstract

This application relates to the field of positioning technology, and discloses a multi-source cooperative positioning method, device, and medium. The method includes: acquiring multi-source positioning information uploaded by a target device; and locating each device to be located based on the multi-source positioning information. The target device is one of multiple devices to be located within an effective positioning area. Each device to be located periodically performs wireless positioning on the other devices, with the positioning times staggered. When a device to be located performs wireless positioning on another device, that device is the target device. The multi-source positioning information includes several types of current wireless positioning information for each device to be located and the inertial navigation information of the target device. This application's embodiments improve the accuracy of regional positioning through multi-source signal joint positioning.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and in particular to a multi-source cooperative positioning method, device and medium. Background Technology

[0002] Regional positioning technology uses wireless mobile communication networks to measure parameters of received radio waves and, based on specific algorithms, accurately determine the spatial location of a person or object at a given time, in order to provide users with relevant location information services.

[0003] Currently, most regional positioning tasks require a corresponding positioning system to view, track, and manage the location of the target, ensuring efficient control of the target. Therefore, the demand for accurate target location information is increasing daily. However, current regional positioning technologies rely solely on beacon positioning, employing a single positioning algorithm and excessively depending on the signal strength of fixed beacons, resulting in low accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a multi-source cooperative positioning method, device and medium, which can improve the accuracy of regional positioning by using multi-source signals for joint positioning.

[0005] This application provides a multi-source cooperative localization method, including:

[0006] The target device is one of multiple devices to be located within the effective positioning area. Each device to be located periodically performs wireless positioning on other devices to be located. The positioning times of the devices to be located are staggered. When a device to be located performs wireless positioning on other devices to be located, that device to be located is the target device. The multi-source positioning information includes several types of current wireless positioning information of each device to be located and the inertial navigation information of the target device.

[0007] Based on the multi-source positioning information, each of the devices to be located is located.

[0008] In some embodiments, the positioning time and positioning period for the device to be located to perform wireless positioning are determined based on the number of devices to be located.

[0009] In some embodiments, the positioning time of each of the devices to be located is configured by a leader device. The leader device configures the positioning time of the other devices to be located based on its own wireless positioning time and the number of devices to be located. The leader devices are a group of devices with the most remaining battery power among the devices to be located.

[0010] In some embodiments, each of the devices to be located also periodically performs wireless positioning on beacons within the effective positioning area, and the multi-source positioning information also includes several types of wireless positioning information of the beacon at present.

[0011] In some embodiments, locating each of the devices to be located based on the multi-source positioning information includes:

[0012] Based on the historical location information of the target device and the inertial navigation information, the current location of the target device is predicted to obtain the current location prediction information of the target device;

[0013] Based on the wireless positioning information, the current position of each of the devices to be located is observed to obtain the current position observation information of the devices to be located.

[0014] By fitting the predicted current position information and the observed current position information of the target device, the current position fitting information of the target device is obtained;

[0015] Based on the current location fitting information, the target device is located, and based on the current location observation information of the other devices to be located, the other devices to be located are located.

[0016] In some embodiments, predicting the current position of the target device based on the historical location information of the target device and the inertial navigation information includes:

[0017] Using the historical location information of the target device as the starting state and the inertial navigation information as the decision result, dynamic planning is performed on the target device, and the current position of the target device is determined based on the dynamic planning result.

[0018] In some embodiments, observing the current location of each of the devices to be located based on the wireless positioning information includes:

[0019] Based on the wireless positioning information, an observation matrix is ​​generated;

[0020] The observation matrix is ​​mapped to a preset position state space to obtain the current position fitting information of the target device.

[0021] In some embodiments, fitting the current location prediction information and current location observation information of the target device includes:

[0022] Calculate the residual between the current position prediction information and the current position observation information of the target device to obtain the current position residual information;

[0023] Based on the current position residual information, Kalman filtering is performed on the current position prediction information of the target device to obtain the current position fitting information of the target device.

[0024] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described multi-source cooperative localization method.

[0025] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-source cooperative localization method.

[0026] The beneficial effects of this application are as follows: By combining intermittent communication and multi-source positioning, each device to be positioned is configured to periodically perform wireless positioning on other devices to be positioned, with the positioning times staggered. This allows each device to perform wireless positioning on other devices sequentially within a preset time period, uploading the obtained wireless positioning information and its own inertial navigation information. This allows the current location of other devices to be positioned to be determined based on their wireless positioning information, and the current location of the target device to be determined based on at least two multi-source positioning data sets, thus achieving positioning of each device. Therefore, through the time-division mutual positioning mechanism between the devices to be positioned, signal interference in densely populated environments is effectively reduced. Simultaneously, inertial navigation data compensates for instantaneous errors in wireless positioning, reducing reliance on beacons. Compared to traditional continuous communication positioning methods, the combination of intermittent communication and multi-source positioning can reduce power consumption while retaining long-term positioning information for each device to be positioned. Furthermore, the multi-source information fusion mechanism enhances the robustness of the positioning system and improves the accuracy of regional positioning. Attached Figure Description

[0027] Figure 1 This is a diagram illustrating the application environment of the multi-source cooperative localization method provided in the embodiments of this application.

[0028] Figure 2 This is a flowchart of the multi-source cooperative localization method provided in the embodiments of this application.

[0029] Figure 3 This is a flowchart of a method for locating various devices to be located, provided in an embodiment of this application.

[0030] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0034] The multi-source cooperative positioning method provided in this application can be executed by a computer device, which can be a terminal device or a server. The terminal device includes, but is not limited to, mobile phones, computers, smart home appliances, vehicle terminals, and aircraft. The server can be a standalone physical server, a server cluster composed of multiple physical servers, a distributed system, or a cloud server. Furthermore, the information, data, and signals involved in this application's embodiments are all authorized by the relevant parties or have been fully authorized by all parties, and the collection, use, and processing of related data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0035] Figure 1 This diagram illustrates the application environment of the multi-source cooperative localization method provided in the embodiments of this application. (See also...) Figure 1This method is applied to a multi-source cooperative positioning system. The system includes a host computer 101 and several devices 102 to be positioned. The host computer 101 is wirelessly connected to each device 102 via a network. Each device 102 is within an effective positioning area and can sense each other and achieve wireless positioning. Each device 102 periodically performs wireless positioning on other devices 102. The host computer 101 acquires multi-source positioning information uploaded by the target device and positions each device 102 based on this information. The target device is one of the multiple devices 102 within the effective positioning area. The positioning times of the devices 102 are staggered. When a device 102 performs wireless positioning on another device 102, that device is the target device. The multi-source positioning information includes several types of current wireless positioning information for each device 102 and the inertial navigation information of the target device during the current positioning cycle.

[0036] Figure 2 This is a flowchart of the multi-source cooperative localization method provided in the embodiments of this application. (See attached document.) Figure 2 In one embodiment, the method includes, but is not limited to, steps S201 to S202.

[0037] Step S201: Obtain multi-source positioning information uploaded by the target device.

[0038] The target device is one of multiple devices to be located within the effective positioning area. The effective positioning area refers to a pre-defined three-dimensional spatial range, which can be defined using geofencing technology or building floor plan coordinates to ensure effective wireless signal coverage between devices.

[0039] Each device to be located periodically performs wireless positioning on other devices to be located, with the timing of these positioning operations staggered. Multiple devices within the effective positioning area can use various wireless positioning technologies to wirelessly locate other devices, thereby obtaining their current wireless positioning information. Furthermore, the devices do not simultaneously perform wireless positioning on other devices; instead, they perform it periodically at staggered times. For example, there are 5 devices to be located within the effective positioning area. Each device periodically performs wireless positioning on the other devices. The positioning period can be 5 minutes. The positioning times of the 5 devices are staggered. Device A can perform wireless positioning on the other devices at the 60th second of the positioning period, device B can perform wireless positioning on the other devices at the 120th second of the positioning period, device C can perform wireless positioning on the other devices at the 180th second of the positioning period, device D can perform wireless positioning on the other devices at the 240th second of the positioning period, and device E can perform wireless positioning on the other devices at the 300th second of the positioning period.

[0040] When a device to be located wirelessly locates other devices to be located, that device is the target device. In other words, the identification of the target device is dynamic. At any given moment within the positioning cycle, the device to be located at that moment is the target device. For example, if there are five devices to be located within the effective positioning area, at the 60th second of the positioning cycle, the target device is device A; at the 120th second, it is device B; at the 180th second, it is device C; at the 240th second, it is device D; and at the 300th second, it is device E.

[0041] Multi-source positioning information includes several types of current wireless positioning information for each device to be positioned and the inertial navigation information of the target device. Each device to be positioned is equipped with an inertial navigation module to record its inertial navigation information in real time. When a device to be positioned performs wireless positioning on other devices to be positioned, the device to be positioned acts as the target device, integrates the several types of current wireless positioning information obtained from the wireless positioning of each device to be positioned and the inertial navigation information of the target device into multi-source positioning information, and uploads this multi-source positioning information to the host computer. For example, given five devices to be located within the effective positioning area, at the 60th second of the current positioning cycle, device A, as the target device, uploads several types of wireless positioning information of devices B, C, D, and E, along with its inertial navigation information, to the host computer. The duration of this inertial navigation information extends from the 60th second of the previous positioning cycle to the 60th second of the current positioning cycle. At the 120th second of the current positioning cycle, device B, as the target device, uploads several types of wireless positioning information of devices A, C, D, and E, along with its inertial navigation information, to the host computer. The duration of this inertial navigation information extends from the 120th second of the previous positioning cycle to the 120th second of the current positioning cycle. At the 180th second of the current positioning cycle, device C, as the target device, uploads several types of wireless positioning information of devices A, B, D, and E, along with its inertial navigation information, to the host computer. Several types of wireless positioning information and the inertial navigation information of the device to be positioned C are uploaded to the host computer. The duration of the inertial navigation information is from the 180th second of the previous positioning cycle to the 180th second of the current positioning cycle. At the current time, which is the 240th second of the current positioning cycle, the device to be positioned D, as the target device, uploads several types of wireless positioning information of the devices to be positioned A, B, C, and E, as well as the inertial navigation information of the device to be positioned D, to the host computer. The duration of the inertial navigation information is from the 240th second of the previous positioning cycle to the 240th second of the current positioning cycle. At the current time, which is the 300th second of the current positioning cycle, the device to be positioned E, as the target device, uploads several types of wireless positioning information of the devices to be positioned A, B, C, and D, as well as the inertial navigation information of the device to be positioned E, to the host computer. The duration of the inertial navigation information is from the 300th second of the previous positioning cycle to the 300th second of the current positioning cycle.

[0042] Preferably, the wireless positioning information can be based on one or more of the following technologies: satellite positioning technology, base station positioning technology, WIFI positioning technology, Bluetooth positioning technology, and / or radio frequency positioning technology.

[0043] Step S202: Based on multi-source positioning information, locate each device to be located.

[0044] After acquiring the multi-source positioning information uploaded by the target device, the host computer determines the current location of other devices to be located based on their wireless positioning information. It then derives the target device's current location based on its inertial navigation information and historical location information, thus locating each device. The historical location information of the target device can be obtained from the previous derivation of its current location, or from the multi-source positioning information uploaded by one or more previous target devices.

[0045] The multi-source cooperative positioning method provided in this application combines intermittent communication and multi-source positioning. By configuring each device to be positioned to periodically perform wireless positioning on other devices, with the positioning times staggered, each device sequentially performs wireless positioning on other devices within a preset time period, uploading the obtained wireless positioning information and its own inertial navigation information. This allows the current location of other devices to be positioned to be determined based on their wireless positioning information, and the current location of the target device to be determined based on at least two multi-source positioning data points, thus achieving positioning of each device. Therefore, through the time-division mutual positioning mechanism between the devices, signal interference in densely populated environments is effectively reduced. Simultaneously, inertial navigation data compensates for instantaneous errors in wireless positioning, reducing reliance on beacons. Compared to traditional continuous communication positioning methods, the combination of intermittent communication and multi-source positioning can reduce power consumption while retaining long-term positioning information for each device. Furthermore, the multi-source information fusion mechanism enhances the robustness of the positioning system and improves the accuracy of regional positioning.

[0046] In some embodiments, the positioning time and positioning period for wireless positioning of the device to be positioned are determined based on the number of devices to be positioned.

[0047] In one specific embodiment, the positioning period for wireless positioning of the device to be located can be positively correlated with the number of devices to be located. Specifically, the interval between two consecutive wireless positioning operations of the device to be located can be the same. As the number of devices to be located within the effective positioning area increases, the positioning period for wireless positioning of the device to be located also increases. For example, for 5 devices to be located within the effective positioning area, the positioning period for each device to be located can be 300 seconds; for 6 devices to be located within the effective positioning area, the positioning period for each device to be located can be 360 ​​seconds.

[0048] In one specific embodiment, the positioning time for wireless positioning of the device to be positioned can be configured with the number of devices to be positioned, and the interval between two consecutive positioning times of the device to be positioned can be negatively correlated with the number of devices to be positioned. Specifically, after determining the positioning period for wireless positioning of the device to be positioned and the positioning times of at least two devices to be positioned, the positioning times of other devices to be positioned can be determined based on the number of devices to be positioned, and within the same positioning period, the positioning times of the other devices to be positioned are made to fall between the positioning times of the two devices to be positioned. For example, for five devices to be positioned within the effective positioning area, the positioning period for wireless positioning of each device to be positioned can be 300 seconds. First, the positioning time of device A to be positioned can be determined as the 60th second of the positioning period, and the positioning time of device E to be positioned can be determined as the 300th second of the positioning period. Then, the positioning interval and positioning time of other devices to be positioned can be determined based on the number of devices to be positioned, resulting in the positioning times of devices B, C, and D being the 120th second, 180th second, and 240th second of the positioning period, respectively.

[0049] In some embodiments, the positioning time of each device to be located is configured by the leader device. The leader device configures the positioning time of other devices to be located for wireless positioning based on its own positioning time and the number of devices to be located.

[0050] Leader devices are a group of devices with the highest remaining battery power among all the devices to be located. In essence, a leader device is a specific device used to coordinate the positioning sequence of multiple devices. This can be implemented using wireless nodes equipped with communication and battery monitoring modules. A remaining battery power selection mechanism ensures the leader device's continuous coordination capability. The group of devices with the highest remaining battery power is selected by monitoring and ranking the devices' battery levels in real time. This can be achieved through a periodic battery power reporting and comparison mechanism, thus preventing low-battery devices from prematurely failing due to frequent coordination tasks.

[0051] Within the effective positioning area, multiple devices to be located periodically report their battery status via wireless communication. The devices with the highest remaining battery power are selected as leader devices. The leader device calculates the positioning time offset for each device based on its preset positioning cycle and the total number of devices to be located in the area, and broadcasts the time allocation table to the other devices. For example, if there are five devices to be located in the area, the leader device can set its own positioning time as the base time point, and the other four devices will sequentially perform positioning operations with a fixed time interval delay, thus avoiding interference caused by multiple devices simultaneously sending wireless signals. Furthermore, the leader device can dynamically update its settings when device battery power changes, ensuring the stability of the coordination function. Therefore, by distributively electing high-battery devices as leaders and autonomously generating dynamic time allocation strategies, the system reduces dependence on fixed infrastructure, avoids communication delays and single-point-of-failure risks associated with centralized scheduling, solves the signal interference problem caused by timing conflicts during multi-device collaborative positioning, reduces differences in battery consumption due to uneven coordination task loads among devices to be located, and improves the system's robustness in scenarios with changing battery power of devices to be located through a dynamic leader election mechanism.

[0052] In some embodiments, each device to be located also periodically performs wireless positioning of beacons within the effective positioning area, and the multi-source positioning information also includes several types of wireless positioning information of the beacons at present.

[0053] A beacon is a wireless signal transmitting device that is fixedly deployed in an effective positioning area. Specifically, it can be implemented using a positioning base station based on UWB or WIFI to provide a stable position reference.

[0054] While periodically performing mutual positioning between devices, the device to be positioned simultaneously collects wireless signal parameters from beacons. For example, within a positioning cycle, the device can receive the identification code and signal strength broadcast by the beacon according to a preset time window, and calculate its relative distance to the beacon using a triangulation algorithm. The fixed coordinates of the beacon are input into the positioning system as known parameters and fused with mutual positioning data and inertial navigation data. Therefore, when environmental interference causes positioning errors in the wireless signals between devices, the stable signal from the beacon can serve as a calibration source to correct the accumulated errors in inertial navigation, while also compensating for positioning blind spots caused by signal blockage between devices. Alternatively, when the beacon is moved and its position information is updated, the system can correct errors caused by the updated beacon position information, reducing reliance on the beacon. Therefore, by introducing a multi-source data fusion mechanism of mutual positioning between devices and beacon positioning, redundant positioning information sources are constructed. When a beacon fails due to signal attenuation or obstruction, the positioning data between devices can still maintain basic positioning functions. When the signals between devices are interfered with, the beacon data can provide an independent verification path, avoiding complete failure of the positioning system. The cross-verification mechanism of multi-source positioning information reduces the impact of a single signal source anomaly on the overall positioning result and improves the stability and reliability of the positioning system in complex environments.

[0055] Figure 3 This is a flowchart illustrating a method for locating various devices to be located, as provided in an embodiment of this application. (See attached document.) Figure 3 In one embodiment, the method includes, but is not limited to, steps S301 to S304.

[0056] Step S301: Based on the historical location information and inertial navigation information of the target device, predict the current position of the target device to obtain the current position prediction information of the target device.

[0057] Step S302: Based on the wireless positioning information, observe the current position of each device to be positioned to obtain the current position observation information of the device to be positioned.

[0058] Step S303: Fit the current position prediction information and the current position observation information of the target device to obtain the current position fitting information of the target device.

[0059] Step S304: Based on the current position fitting information, locate the target device; based on the current position observation information of other devices to be located, locate the other devices to be located.

[0060] Historical location information of the target device refers to the location data of the target device before the current positioning time. It can be the location data of the target device in the previous positioning cycle or when the target device uploaded multi-source positioning information. Specifically, it can be implemented by using coordinate information recorded by the storage module, which is used as the initial state for predicting the current position of the target device.

[0061] Current position fitting information refers to the position estimate obtained by combining current position prediction information and current position observation information. Specifically, the Kalman filter algorithm can be used to iteratively correct the prediction residuals to eliminate errors from a single data source.

[0062] After receiving the multi-source positioning information uploaded by the target device

[0063] The historical location information of the target device is loaded as the initial state in the prediction phase. Combined with the acceleration and angular velocity data recorded in the inertial navigation information, a dynamic programming algorithm is used to simulate the device's trajectory and generate the target device's current position prediction information. Simultaneously, each device to be located acquires distance or angle measurements between itself via wireless signal interaction, constructs an observation matrix, and maps it to a preset position state space, generating the current position observation information for each device. Then, the target device's current position prediction information and current position observation information are aligned using residual calculation. Subsequently, Kalman filtering is used to iteratively correct the prediction results, finally outputting the fused current position fitting information of the target device. This fitting information serves as the positioning result for the target device, while the observation information of other devices to be located is directly used for their positioning calculations. Thus, by fusing inertial navigation data and wireless positioning data from multiple devices to be located, dual verification of dynamic prediction and real-time observation is achieved. Through multi-source data complementarity, positioning continuity can be maintained even when some signals are lost. Furthermore, a distributed positioning network was constructed by utilizing mutual measurements among the devices to be positioned. By fusing inertial navigation predictions with observation data from multiple devices, dynamic calibration of the positioning results was achieved, reducing the accumulation of errors caused by environmental factors. The collaborative observation among devices enhanced the robustness of the positioning system, enabling it to maintain stable positioning output in complex indoor environments.

[0064] In some embodiments, predicting the current position of the target device based on its historical location information and inertial navigation information includes: performing dynamic planning on the target device using its historical location information as the starting state and inertial navigation information as the decision result, and determining the current position of the target device based on the dynamic planning result.

[0065] Dynamic programming is a method to optimize path selection by decomposing a multi-stage decision-making process. Specifically, it can be implemented using recursive or iterative algorithms and is used to process time-series information in inertial navigation data.

[0066] When predicting the current position of a target device, the historical position information of the target device is first used as the initial state for dynamic programming, for example, using the coordinates of the previous moment as the starting point. Then, the motion parameters in the inertial navigation information are transformed into state transition conditions, for example, integrating acceleration data to obtain the displacement increment. The dynamic programming algorithm evaluates the cost of multiple possible movement paths, for example, calculating the matching degree between the displacement increment and the historical trajectory under different paths using the state transition equation. Finally, the endpoint of the path with the lowest cost is selected as the current position prediction result, for example, using the coordinates of the path's end point as the prediction output. Thus, by combining historical trajectory with real-time motion data through dynamic programming, error offsets are automatically corrected during path selection, effectively improving the utilization rate of inertial navigation data, reducing the accumulation rate of positioning errors in complex environments, and enhancing the continuity of the device's movement trajectory by fusing historical position information with real-time motion parameters, especially maintaining stable positioning output when wireless signals are interfered with.

[0067] In some embodiments, observing the current position of each device to be located based on wireless positioning information includes: generating an observation matrix based on the wireless positioning information; mapping the observation matrix to a preset position state space to obtain the current position fitting information of the target device.

[0068] An observation matrix is ​​a mathematical matrix constructed using multi-source wireless positioning data. Specifically, it can be implemented using multi-dimensional parameters such as signal strength, time difference of arrival, and angle information as matrix elements, and is used to systematically represent the spatial relationships between devices.

[0069] Location state space refers to a pre-established probability distribution model of equipment location, which can be implemented using a three-dimensional coordinate system or a rasterized map model, and is used to transform discrete observation data into continuous spatial location estimates.

[0070] After acquiring the wireless positioning information uploaded by the target device, the feature parameters of different positioning sources are first extracted and an observation matrix is ​​constructed. For example, Bluetooth signal strength, UWB time difference of arrival, and / or WIFI angle information can be selected as row vector elements of the matrix. Subsequently, the observation matrix is ​​projected onto the position state space using a preset coordinate transformation algorithm, and the peak point of the probability density distribution of the target device in the state space is calculated using a spatial interpolation algorithm. This peak point is identified as the current position fitting information of the target device. Thus, by constructing a multi-dimensional observation matrix to integrate multi-source positioning data and performing data fusion in the position state space, the influence of measurement errors from a single signal source is effectively suppressed. It can comprehensively utilize multiple wireless positioning feature parameters and achieve collaborative correction of multi-source data through mathematical modeling and spatial mapping methods, significantly improving the stability and accuracy of device positioning in complex indoor environments. This solves the technical defects of traditional single-beacon positioning methods, such as weak anti-interference capability and severe error accumulation.

[0071] In some embodiments, fitting the current position prediction information and the current position observation information of the target device includes: calculating the residual between the current position prediction information and the current position observation information of the target device to obtain the current position residual information; and performing Kalman filtering on the current position prediction information of the target device based on the current position residual information to obtain the current position fitting information of the target device.

[0072] The residual refers to the difference between the predicted value and the observed value. It can be realized through matrix subtraction or error function calculation and is used to quantify the degree of deviation of the prediction result.

[0073] Kalman filtering refers to the process of optimizing noisy data using a recursive algorithm. Specifically, it can be implemented by using state update equations and covariance matrix adjustments to dynamically correct prediction errors and improve positioning accuracy.

[0074] During the positioning process, the predicted position of the target device is first generated using inertial navigation data, while the observed position is obtained using wireless signal measurement. Then, by calculating the residual between the predicted and observed positions of the target device, the degree of deviation between the prediction and the actual environment can be identified. Furthermore, the Kalman filter dynamically adjusts the parameters of the prediction model based on the residual information, gradually converging to the optimal position estimate through an iterative update process. For example, when transient interference occurs in the observed data, the filtering algorithm can automatically reduce the weight of abnormal data, maintaining the stability of the positioning results. Thus, by fusing the residual analysis of predicted and observed data, a dual verification mechanism is constructed, effectively suppressing the error propagation problem of a single data source. Simultaneously, the dynamic adjustment capability of the Kalman filter overcomes the limitations of fixed filtering parameters and can adapt to noise variations in different environments.

[0075] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application. The following refers to... Figure 4 This describes an electronic device provided according to embodiments of the present application. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0076] like Figure 4 As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), a display unit 440, etc.

[0077] The storage unit stores program code, which can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the above-described multi-source cooperative localization method section of this specification according to various exemplary embodiments of this disclosure.

[0078] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 4201 and / or cache memory 4202, and may further include a read-only memory (ROM) 4203.

[0079] Storage unit 420 may also include a program / utility 4204 having a set (at least one) program module 4205, such program module 4205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0080] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0081] Electronic device 400 can also communicate with one or more external devices 400' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. Network adapter 460 can communicate with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0082] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-source cooperative localization method.

[0083] The multi-source cooperative positioning method, apparatus, device, and medium provided in this application combine intermittent communication and multi-source positioning. By configuring each device to be positioned to periodically perform wireless positioning on other devices to be positioned, with the positioning times staggered, each device to be positioned sequentially performs wireless positioning on other devices to be positioned within a preset time period, and uploads the obtained wireless positioning information and its own inertial navigation information. This allows the current position of other devices to be positioned to be determined based on their wireless positioning information, and the current position of the target device to be determined based on at least two multi-source positioning data sets, thereby achieving positioning of each device to be positioned. Therefore, through the time-division mutual positioning mechanism between the devices to be positioned, signal interference in densely populated environments is effectively reduced. Simultaneously, inertial navigation data is used to compensate for the instantaneous errors in wireless positioning, reducing the dependence on beacons. Compared to traditional continuous communication positioning methods, the combination of intermittent communication and multi-source positioning can reduce the power consumption required for positioning while retaining long-term positioning information for each device to be positioned. Furthermore, the multi-source information fusion mechanism enhances the robustness of the positioning system and improves the accuracy of regional positioning.

[0084] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.

[0085] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0086] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0087] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0088] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A multi-source cooperative localization method, characterized in that, include: Acquire multi-source positioning information uploaded by the target device; The target device is one of a plurality of devices to be located within the effective positioning area. Each device to be located periodically performs wireless positioning on the other devices to be located. The positioning times of the devices to be located are staggered. When a device to be located performs wireless positioning on the other devices to be located, the device to be located is the target device. The multi-source positioning information includes several types of current wireless positioning information of each device to be located and the inertial navigation information of the target device. Based on the multi-source positioning information, each of the devices to be located is located; The step of locating each of the devices to be located based on the multi-source positioning information includes: Based on the historical location information of the target device and the inertial navigation information, the current location of the target device is predicted to obtain the current location prediction information of the target device; Based on the wireless positioning information, the current position of each of the devices to be located is observed to obtain the current position observation information of the devices to be located. By fitting the predicted current position information and the observed current position information of the target device, the current position fitting information of the target device is obtained; Based on the current location fitting information, the target device is located, and based on the current location observation information of the other devices to be located, the other devices to be located are located.

2. The multi-source cooperative localization method according to claim 1, characterized in that, The positioning time and positioning period for the device to be positioned via wireless positioning are determined based on the number of devices to be positioned.

3. The multi-source cooperative localization method according to claim 2, characterized in that, The positioning time of each of the devices to be located is configured by the leader device. The leader device configures the positioning time of the other devices to be located based on its own wireless positioning time and the number of devices to be located. The leader devices are the devices with the most remaining battery power among the devices to be located.

4. The multi-source cooperative localization method according to any one of claims 1 to 3, characterized in that, Each of the devices to be located also periodically performs wireless positioning on beacons within the effective positioning area, and the multi-source positioning information also includes several types of wireless positioning information of the beacon at present.

5. The multi-source cooperative localization method according to claim 1, characterized in that, The step of predicting the current position of the target device based on its historical location information and inertial navigation information includes: Using the historical location information of the target device as the starting state and the inertial navigation information as the decision result, dynamic planning is performed on the target device, and the current position of the target device is determined based on the dynamic planning result.

6. The multi-source cooperative localization method according to claim 1, characterized in that, The step of observing the current location of each of the devices to be located based on the wireless positioning information includes: Based on the wireless positioning information, an observation matrix is ​​generated; The observation matrix is ​​mapped to a preset position state space to obtain the current position fitting information of the target device.

7. The multi-source cooperative localization method according to claim 1, characterized in that, The fitting of the target device's current location prediction information and current location observation information includes: Calculate the residual between the current position prediction information and the current position observation information of the target device to obtain the current position residual information; Based on the current position residual information, Kalman filtering is performed on the current position prediction information of the target device to obtain the current position fitting information of the target device.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the multi-source cooperative localization method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-source cooperative localization method as described in any one of claims 1 to 7.

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