Wireless communication ranging system and method, electronic equipment and storage medium

Abstract

The invention discloses a wireless communication ranging system and method, electronic equipment and a storage medium. According to the wireless communication distance measurement method, a star flash SLE communication module is adopted, and the star flash SLE communication module is replaced in a communication module of a vehicle door controller and a wireless vehicle key and is used for transmitting and receiving wireless signals; the Kalman filter is connected with the satellite flash SLE communication module and is used for carrying out noise correction on the RSSI value; and the Gaussian regression process model is connected with the Kalman filter and is used for establishing a mathematical relationship between the optimized RSSI value and the distance so as to realize distance estimation. According to the invention, the technical problem of inaccurate distance measurement caused by low stability of vehicle-mounted wireless communication in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a wireless communication ranging system, method, electronic equipment and storage medium. Background Art

[0002] With the birth of the new generation of wireless short-range communication technology, Xingshan, and the development of the smart car industry, in order to meet the requirements of low latency, high reliability, and low power consumption in communication technology for smart connected cars, vehicle-mounted communication equipment based on Xingshan technology has begun to be used in vehicle installation. The application of smart car locks can greatly improve the safety, convenience, and intelligence level of vehicles. Smart car locks use wireless ranging technology to achieve remote unlocking and locking functions. Car owners do not need to approach the vehicle or even manually operate to automatically open and close the car locks. Moreover, according to the different ranging results of different doors for car keys, the function of opening the nearest door can be realized. This function relies on the wireless communication ranging technology between the car body and the key. The current mainstream ranging technologies include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), signal strength (RSSI), etc.

[0003] However, in actual applications of the vehicle-mounted wireless communication system, there is a lot of signal interference in the vehicle-mounted wireless communication system. The interference signals include but are not limited to other wireless signals, reflected signals of wireless signals emitted by the vehicle-mounted equipment itself, wireless signals of the vehicle-mounted equipment itself, cross-interference of wireless signals between the vehicle-mounted equipment and other vehicle-mounted equipment, etc. The interference signals have an adverse effect on the stability of wireless signal transmission of the vehicle-mounted wireless communication system, affecting the communication stability of the vehicle-mounted wireless communication system and the stability of ranging.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a wireless communication ranging system, method, electronic device and storage medium to at least solve the technical problem in the prior art that the stability of vehicle-mounted wireless communication is low, resulting in inaccurate ranging.

[0006] One aspect of the present application provides a vehicle-mounted wireless communication system, including a Star Flash SLE communication module, which is replaced in the communication modules of the door controller and the wireless car key and is used for transmission and reception of wireless signals; a Kalman filter, connected to the Star Flash SLE communication module, for performing noise correction on the RSSI value; a Gaussian regression process model, connected to the Kalman filter, for establishing a mathematical relationship between the optimized RSSI value and the distance, to achieve distance estimation.

[0007] Optionally, the noise correction process of the Kalman filter includes dynamically calibrating the time series data of the RSSI values ​​to improve the stability of the distance estimation.

[0008] Optionally, the Gaussian regression process model adopts a specific covariance function to adapt to the nonlinear characteristics of RSSI value changes in the vehicle environment.

[0009] Optionally, the system also includes a domain controller connected to the Gaussian regression process model, which is used to issue control instructions based on the distance estimation results to achieve opening and closing operations of the smart car lock.

[0010] Optionally, the domain controller also has a security authentication function, which can encrypt and decrypt received wireless signals to improve the security of communications.

[0011] One aspect of the present application provides a method for a vehicle-mounted wireless communication system, including transmitting and receiving wireless signals by a Star Flash SLE communication module; performing noise correction on RSSI values ​​through a Kalman filter; and using a Gaussian regression process model to establish a mathematical relationship between an optimized RSSI value and distance to achieve distance estimation.

[0012] Optionally, the Kalman filter dynamically adjusts filter parameters according to vehicle environmental noise characteristics during noise correction to optimize the accuracy of the RSSI value.

[0013] Optionally, a Gaussian regression process model utilizes pre-trained model parameters in distance estimation to make predictions based on historical RSSI values ​​and distance data.

[0014] Optionally, the method also includes issuing control instructions according to the distance estimation result through the domain controller to achieve automatic management of the smart car lock.

[0015] Optionally, before issuing a control instruction, the domain controller performs a security authentication process to verify the source of the wireless signal and ensure communication security.

[0016] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the wireless communication ranging method in any one of the above items is executed when the program is running.

[0017] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute any of the wireless communication ranging methods described above.

[0018] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the wireless communication ranging method in any one of the above items is implemented.

[0019] In an embodiment of the present invention, the system includes a Star Flash SLE communication module, which is replaced in the communication module of the door controller and the wireless car key, and is used for the transmission and reception of wireless signals; a Kalman filter, which is connected to the Star Flash SLE communication module, and is used to perform noise correction on the RSSI value; a Gaussian regression process model, which is connected to the Kalman filter, and is used to establish a mathematical relationship between the optimized RSSI value and the distance, and realize distance estimation. The present invention significantly improves the performance and safety of the vehicle-mounted wireless communication system by introducing the Star Flash SLE communication module, the Kalman filter and the Gaussian regression process model. The Star Flash SLE communication module ensures the efficient transmission and reception of wireless signals, the Kalman filter effectively corrects the noise of the RSSI value, and the Gaussian regression process model accurately establishes the mathematical relationship between the RSSI value and the distance, making the distance estimation more accurate, thereby solving the technical problem in the prior art that the stability of the vehicle-mounted wireless communication is low, resulting in inaccurate distance measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a schematic diagram of the architecture of a wireless communication ranging system according to one embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of the training process of a Gaussian regression process model according to one embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a Kalman filter process according to one embodiment of the present invention;

[0024] Figure 4 is a flow chart of a wireless communication ranging method according to one embodiment of the present invention;

[0025] Figure 5 1 is an example diagram of a ranging process of a wireless communication ranging method according to one embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] It should be noted that the ranging technologies in the prior art include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival difference (AOA) and the like.

[0029] TOA is a technology used to measure the propagation time of a signal. It determines the propagation distance of a signal by measuring the time it takes for the signal to be sent and received. Based on the propagation speed of the signal, the transmission distance of the signal can be calculated, which can be used for applications such as positioning and time synchronization. The TOA system requires equipment with precise clock synchronization, otherwise it will lead to increased positioning errors. In a multipath propagation environment, the signal may propagate through multiple paths to reach the receiver, resulting in increased uncertainty in the signal propagation time, which in turn affects the positioning accuracy of the TOA technology.

[0030] TDOA is a positioning technology based on time difference. In the field of communication and positioning, TDOA technology determines the location of the signal source by measuring the time difference between the signal arriving at different receiving points, and by measuring and calculating the time difference between the signal arriving at each receiving point, the location of the signal source is determined. TDOA, like TOA, is also a time-related positioning technology, so it also requires that the clocks between different receivers be highly synchronized. TDOA technology also requires complex time difference calculations and signal processing, especially in the face of complex signal propagation environments and noise interference, the algorithm has a high computational complexity.

[0031] AOA is a technology used to measure the direction of arrival of a signal. In the field of communication and positioning, AOA technology determines the location of the signal source by measuring the angle at which the signal arrives at the receiver. By analyzing the angle difference at which the signal arrives at different receivers, the position of the signal source relative to the receiver is calculated to achieve precise positioning. AOA technology has high requirements for directional antennas or antenna arrays, and requires precise calibration and deployment of antennas, which increases the complexity and cost of the system.

[0032] Reference Figure 1 According to an embodiment of the present invention, a vehicle-mounted wireless communication system is provided, including a Star Flash SLE communication module, which is replaced in the communication modules of the door controller and the wireless car key and is used for transmitting and receiving wireless signals; a Kalman filter, which is connected to the Star Flash SLE communication module and is used for performing noise correction on the RSSI value; a Gaussian regression process model, which is connected to the Kalman filter and is used to establish a mathematical relationship between the optimized RSSI value and the distance to achieve distance estimation.

[0033] Optionally, the noise correction process of the Kalman filter includes dynamically calibrating the time series data of the RSSI values ​​to improve the stability of the distance estimation.

[0034] Optionally, the Gaussian regression process model adopts a specific covariance function to adapt to the nonlinear characteristics of RSSI value changes in the vehicle environment.

[0035] Optionally, the system also includes a domain controller connected to the Gaussian regression process model, which is used to issue control instructions based on the distance estimation results to achieve opening and closing operations of the smart car lock.

[0036] Optionally, the domain controller also has a security authentication function, which can encrypt and decrypt received wireless signals to improve the security of communications.

[0037] According to an embodiment of the present invention, an embodiment of a wireless communication ranging method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] The method embodiment can also be executed in an electronic device including a memory and a processor, a similar control device or a cloud. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above structural description is only illustrative and does not limit the structure of the above electronic device. For example, the electronic device may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0039] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processor (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, and the like. Among them, different processing units may be independent components or integrated into one or more processors. In some instances, the electronic device may also include one or more processors.

[0040] The memory can be used to store computer programs, such as storing computer programs corresponding to the wireless communication ranging method in the embodiment of the present invention. The processor implements the above-mentioned wireless communication ranging method by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The communication device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the communication device includes a network adapter (network interface controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (radio frequency, RF) module, which is used to communicate with the Internet wirelessly. In some embodiments of the present solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, and instructions can be sent to electronic devices through the mobile device.

[0042] The display device may be a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the electronic device. In some embodiments, the electronic device has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch sensitive surface, and executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0043] Figure 4 is a flow chart of a wireless communication ranging method according to one embodiment of the present invention. Figure 4 As shown, the method comprises the following steps:

[0044] Step S101, the Star Flash SLE communication module transmits and receives wireless signals.

[0045] Step S102: performing noise correction on the RSSI value through a Kalman filter.

[0046] Step S103, using the Gaussian regression process model to establish a mathematical relationship between the optimized RSSI value and the distance, to achieve distance estimation.

[0047] Optionally, the Kalman filter dynamically adjusts filter parameters according to vehicle environmental noise characteristics during noise correction to optimize the accuracy of the RSSI value.

[0048] Optionally, a Gaussian regression process model utilizes pre-trained model parameters in distance estimation to make predictions based on historical RSSI values ​​and distance data.

[0049] Optionally, the method also includes issuing control instructions according to the distance estimation result through the domain controller to achieve automatic management of the smart car lock.

[0050] Optionally, before issuing a control instruction, the domain controller performs a security authentication process to verify the source of the wireless signal and ensure communication security.

[0051] Exemplarily, the above solution is specifically implemented as follows:

[0052] First of all, in order to realize the wireless ranging function between the car door and the key, the hardware needs to add the Star Flash module to the existing door controller and wireless car key, and integrate them into a vehicle wireless communication system based on Star Flash SLE communication. At the same time, for communication security, a security authentication module needs to be added. The software needs to use the Kalman filter algorithm to filter the measurement signal. The filtered signal is converted according to the probability propagation model of the Gaussian regression process to obtain distance information and achieve accurate ranging.

[0053] Secondly, the model parameters of the Gaussian regression process and the covariance of the Kalman filter model are learned. According to the specific situation, multiple signal nodes are set, and each node collects multiple signal samples. Assume that m fixed signal nodes and one mobile signal node are set, and each fixed node collects k signals sent from the mobile node (these signals are the signals received by the SLE receiver and carry signal strength RSSI information). These samples are first sent to the location server that performs the location calculation, and the Kalman filter is used to correct the measured RSSI value, so as to eliminate the influence of noise and find the best estimate of the actual RSSI value received by each fixed node.

[0054] Afterwards, the filtered RSSI value is input into the GPR propagation model to approximate the distance between the fixed node and the mobile node. A GPR model needs to be defined for each node, that is, m GPR models. The input of each model is the RSSI value received from the corresponding fixed node, and the output is the propagation distance of the communication signal between the fixed node and the mobile node. The Kalman filter provides the best estimate of the RSSI value by minimizing the mean square error of a given noisy RSSI sample. The GPR model is used to estimate the distance of the measured RSSI value through regression without the need for a lengthy calibration phase. It is more accurate than traditional propagation models, and then triangulation is used to locate the position of the fixed node. The training flow chart is as follows: Figure 2 shown.

[0055] Finally, the learned and trained GPR model and Kalman filter are written into the actuator and domain controller and deployed on the previously modified hardware equipment. That is, an SLE module is installed between the center of the cockpit and the wireless car key. The car key is used as a mobile node and a signal transmitter. The slave anchor points around the car are used as fixed nodes and signal receivers. The main anchor point in the center of the cockpit is used as a distance calculation server. Star Flash SLE communication is established between the nodes. When the car key approaches the car body, its SLE module also broadcasts its position and sends SLE signals. After receiving the signal, each anchor point performs filtering and sends the vehicle distance information to the main anchor point in the center of the cockpit. After the distance is calculated and compared in the center of the cockpit, the domain controller issues a control command to open the door lock closest to the car key.

[0056] Optionally, the following embodiments are also included:

[0057] First, replace the communication modules of the door controller and the wireless car key with the Star Flash SLE module to ensure lower power consumption, lower latency and higher reliability. Initialize the SLE module and set different communication frequencies, data transmission rates, power and other parameters according to different application scenarios. After that, integrate the SLE module with the application to program the functions of the SLE communication module and the method for processing signals. This part is about making changes to the hardware facilities to facilitate the use of the ranging model and method designed by the present invention. The wireless communication structure diagram between the door and the key is shown in the figure below. Figure 1 The center of the cockpit is used as the main anchor point for ranging, and the four doors are used as slave anchor points to jointly receive the SLE wireless signal sent by the wireless car key. After establishing communication, the four slave anchor points transmit the signal to the center of the cockpit, i.e. the main anchor point, through the Star Flash SLE. After calculating and comparing the distance, the body domain controller sends a control signal to control the car lock to open or close.

[0058] Afterwards, the Star Flash SLE wireless communication technology is used to transmit signals between the car key and the door sensor to obtain the signal strength received by the door sensor, that is, the RSSI value. Since the RSSI value is directly used for ranging, it is easily affected by noise. Therefore, the present invention uses a Kalman filter to estimate the RSSI measurement value. The Kalman filter can estimate the probability of a state p(x) by observing it, and uses the covariance as a measure of the accuracy of the estimation. For each new observation, the filter will obtain the mean and covariance of the system state to provide a new estimate. The general RSSI signal follows the discrete time linear difference equation:

[0059] x k =A k-1 x k-1 +w k-1 (1)

[0060] The observation system is given by:

[0061] y k -H k x k +υ k (2)

[0062] Random process w k and v k They are system noise and observation noise respectively, assuming that they are related to the covariance matrix Q k and R k Independent of each other, A is a matrix that relates the previous estimate to the current estimate, while the H matrix defines the relationship between the measurement and the expected estimate. In the automotive wireless communication system applied by the present invention, both the A matrix and the H matrix are equal to 1, because the estimate here is just a constant that can be directly measured. Based on the system model and the measurement model, the filter should estimate the true RSSI value. If all measurements up to the kth are available, the a posteriori estimate of the state can be found If all measurements up to time k-1 are available, the prior for the RSSI value at time k can be found This makes xk a predicted estimate of the state, and x+k a smoothed estimate of the state. In addition, the Kalman filter provides a measure of the uncertainty of the state estimate at each iteration, which is calculated by the estimation error covariance p. The filtering algorithm can be summarized using the following formula. The Kalman filter flowchart is as follows Figure 3 As shown:

[0063]

[0064]

[0065] Then, a propagation model for estimating the distance between two nodes is established. Given the measured RSSI value, the probability propagation model assumes that the input and output are randomly distributed. In the application scenario of the present invention, the input x of the propagation model is the measured value of RSSI, and the output y is the estimated distance. The input and output have a functional relationship f, accompanied by a certain amount of noise, as shown in the following formula:

[0066] y=f(x)+ε (8)

[0067] The Gaussian regression process used in the present invention is a mathematical tool for estimating the output of a system under new inputs given a set of noisy input and output data. A Gaussian process is a set of random variables that obey a Gaussian distribution, whose mean and covariance are given by the following equations (9) and (10):

[0068] μ x =E[f(x)] (9)

[0069] k(x,x′)-E[(f(x)-μ x )(f(r′)-μ x′ )] (10)

[0070] In the Gaussian model, ε is assumed to be independent zero-mean Gaussian noise In addition, the covariance function is chosen to be a squared exponential with the following formula:

[0071]

[0072] For this covariance function, it can be seen that when the input values ​​are close, the corresponding random variables have a high correlation, almost equal to On the contrary, when x p Stay away from x q When , the covariance approaches zero. The separation factor l is used to define the amount of input change required to cause a significant change in the output. After adding noise to the system, the covariance function is given by:

[0073]

[0074] where δ pq is a special function that is zero everywhere except for p=q. This results in only the diagonal elements of the covariance matrix being interrupted by noise. For n data points, the GPR model covariance matrix is ​​evaluated as:

[0075]

[0076] The covariance function of the new input x is given by:

[0077]

[0078] The estimated output y at a new input x is given by the estimated mean given by:

[0079]

[0080] The uncertainty of the estimate is given by the variance of the estimate, which is given by:

[0081] υar(y * )=K ** -K * K -1 K *T (16)

[0082] The present invention applies Kalman filtering to the correction process of RSSI measurement data, combines it with the probability propagation model of Gaussian regression process, and uses the RSSI value after filtering for ranging. Kalman filter is a typical powerful mathematical tool for estimation problems. In the field of vehicle-mounted wireless communications, Kalman filter can estimate the actual RSSI value from a set of noisy RSSI measurement values ​​of the signal receiver. Kalman filter is used because it has known optimality in achieving minimum mean square error. After obtaining the filtered RSSI value, a mathematical relationship between it and the distance is established. The present invention uses Gaussian process regression (GPR) to simulate signal propagation as a function of distance. The ranging flow chart is shown in the figure. Figure 5 As shown in the figure, the filtered RSSI signal can be well converted into distance information in a suitable GPR mathematical model to achieve the function of accurate distance measurement.

[0083] An embodiment of the present invention further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the wireless communication ranging method described in any of the above embodiments is executed when the program is running.

[0084] Optionally, in this embodiment, the processor in the vehicle may be configured to run an executable program to perform the following steps:

[0085] Step S101, the Star Flash SLE communication module transmits and receives wireless signals.

[0086] Step S102: performing noise correction on the RSSI value through a Kalman filter.

[0087] Step S103, using the Gaussian regression process model to establish a mathematical relationship between the optimized RSSI value and the distance, and perform distance estimation.

[0088] Optionally, the Kalman filter dynamically adjusts filter parameters according to vehicle environmental noise characteristics during noise correction to optimize the accuracy of the RSSI value.

[0089] Optionally, a Gaussian regression process model utilizes pre-trained model parameters in distance estimation to make predictions based on historical RSSI values ​​and distance data.

[0090] Optionally, the method also includes issuing control instructions according to the distance estimation result through the domain controller to achieve automatic management of the smart car lock.

[0091] Optionally, before issuing a control instruction, the domain controller performs a security authentication process to verify the source of the wireless signal and ensure communication security.

[0092] An embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the wireless communication ranging method described in any of the above embodiments.

[0093] Optionally, in this embodiment, the above executable program may be configured to store an executable program for performing the following steps:

[0094] Step S101, the Star Flash SLE communication module transmits and receives wireless signals.

[0095] Step S102: performing noise correction on the RSSI value through a Kalman filter.

[0096] Step S103, using the Gaussian regression process model to establish a mathematical relationship between the optimized RSSI value and the distance, and perform distance estimation.

[0097] Optionally, the Kalman filter dynamically adjusts filter parameters according to vehicle environmental noise characteristics during noise correction to optimize the accuracy of the RSSI value.

[0098] Optionally, a Gaussian regression process model utilizes pre-trained model parameters in distance estimation to make predictions based on historical RSSI values ​​and distance data.

[0099] Optionally, the method also includes issuing control instructions according to the distance estimation result through the domain controller to achieve automatic management of the smart car lock.

[0100] Optionally, before issuing a control instruction, the domain controller performs a security authentication process to verify the source of the wireless signal and ensure communication security.

[0101] An embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the wireless communication ranging method described in any of the above embodiments is implemented.

[0102] Optionally, in this embodiment, the above computer program implements the following steps when executed by a processor:

[0103] Step S101, the Star Flash SLE communication module transmits and receives wireless signals.

[0104] Step S102: performing noise correction on the RSSI value through a Kalman filter.

[0105] Step S103, using the Gaussian regression process model to establish a mathematical relationship between the optimized RSSI value and the distance, and perform distance estimation.

[0106] Optionally, the Kalman filter dynamically adjusts filter parameters according to vehicle environmental noise characteristics during noise correction to optimize the accuracy of the RSSI value.

[0107] Optionally, a Gaussian regression process model utilizes pre-trained model parameters in distance estimation to make predictions based on historical RSSI values ​​and distance data.

[0108] Optionally, the method also includes issuing control instructions according to the distance estimation result through the domain controller to achieve automatic management of the smart car lock.

[0109] Optionally, before issuing a control instruction, the domain controller performs a security authentication process to verify the source of the wireless signal and ensure communication security.

[0110] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0111] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] In some embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0116] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A wireless communication ranging system, characterized in that: include: The Star Flash SLE communication module is replaced in the communication modules of the door controller and the wireless car key for transmitting and receiving wireless signals; A Kalman filter, connected to the Star Flash SLE communication module, is used to perform noise correction on the RSSI value; The Gaussian regression process model is connected to the Kalman filter and is used to establish a mathematical relationship between the optimized RSSI value and the distance for distance estimation.

2. The wireless communication ranging system according to claim 1, characterized in that: The noise correction process of the Kalman filter includes dynamically calibrating the time series data of the RSSI value.

3. The wireless communication ranging system according to claim 1, characterized in that: It also includes a domain controller, which is connected to the Gaussian regression process model and is used to issue control instructions according to the distance estimation result to control the opening and closing of the smart car lock.

4. The wireless communication ranging system according to claim 3, characterized in that: The domain controller also has a security authentication function for encrypting and decrypting received wireless signals.

5. A wireless communication ranging method, applied to the wireless communication ranging system described in any one of 1 to 4 above, characterized in that: include: The wireless signal is transmitted and received by the Xingshan SLE communication module; The RSSI value is noise corrected by Kalman filter; The Gaussian regression process model is used to establish the mathematical relationship between the optimized RSSI value and the distance for distance estimation.

6. The wireless communication ranging method according to claim 5, characterized in that: The Kalman filter dynamically adjusts filter parameters according to vehicle environment noise characteristics during noise correction.

7. The wireless communication ranging method according to claim 5, characterized in that: The Gaussian regression process model uses pre-trained model parameters in distance estimation and makes predictions based on historical RSSI values ​​and distance data.

8. The wireless communication ranging method according to claim 5, characterized in that: Before issuing control instructions, the domain controller performs a security authentication process to verify the source of the wireless signal.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 5 to 8 when running.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 5 to 8.

11. A computer program product, characterized in that It comprises a computer program which, when executed by a processor, implements the method according to any one of claims 5 to 8.

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