Distance measurement method and device based on bidirectional signal, equipment and storage medium

Abstract

The invention provides a distance measurement method and device based on a bidirectional signal, equipment and a storage medium, relates to the technical field of communication, is applied to a vehicle end, and comprises the following steps: based on communication connection established with a mobile end, responding to a first signal sent by the mobile end, and sending a second signal to the mobile end, acquiring a signal intensity value of a second signal returned by the mobile terminal; and determining the distance from the mobile terminal to the vehicle terminal through a distance estimation fusion algorithm according to the signal intensity value of the first signal and the signal intensity value of the second signal. According to the invention, distance measurement is carried out according to the signal intensity values of the bidirectional signals received by the vehicle calling end and the mobile end, and the accuracy of distance measurement of the vehicle end and the mobile end can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a ranging method, apparatus, device and storage medium based on bidirectional signals. Background Art

[0002] With the rapid development of intelligent vehicles, digital keys are gradually replacing traditional physical keys and becoming the core of vehicle authentication and access control. Existing digital keys are typically based on wireless communication technologies such as Bluetooth or ultra-wideband (UWB). They connect to the vehicle through mobile devices (such as smartphones and smartwatches) to perform functions such as locking and engine start / stop. Compared to traditional keys, digital keys offer advantages such as remote authorization, multi-user sharing, and dynamic permission management, significantly improving user experience and vehicle safety.

[0003] Precise vehicle access control requires accurate distance determination between the mobile device and the vehicle. However, current distance measurement typically relies on unidirectional signals (e.g., wireless signals with a single frequency or phase). Single-phase signals are susceptible to multipath effects, environmental interference, and signal attenuation, leading to fluctuations in received signal strength and affecting ranging accuracy. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the accuracy of distance measurement.

[0005] To solve the above problems, the present invention provides a ranging method, apparatus, device and storage medium based on bidirectional signals.

[0006] In a first aspect, the present invention provides a distance measurement method based on a bidirectional signal, which is applied to a vehicle side. The distance prediction method based on a bidirectional signal includes: Based on the communication connection established with the mobile terminal, sending a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and obtaining a signal strength value of the second signal returned by the mobile terminal; The distance from the mobile terminal to the vehicle terminal is determined by a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0007] Optionally, the sending a second signal to the mobile terminal in response to the first signal sent by the mobile terminal includes: After receiving the first signal sent by the mobile terminal, the signal strength value of the first signal is obtained, and the second signal is sent to the mobile terminal.

[0008] Optionally, the communication connection includes a first channel and a second channel, and after receiving the first signal sent by the mobile terminal through the first channel, the second signal is sent through the second channel.

[0009] Optionally, obtaining the signal strength value of the second signal returned by the mobile terminal includes: The signal strength value of the second signal sent by the vehicle end is received through the second channel, wherein the signal strength value of the second signal is the signal strength value of the second signal obtained after the mobile end receives the second signal.

[0010] Optionally, determining the distance from the mobile terminal to the vehicle terminal by using a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal includes: Based on the state vector obtained at the previous moment, an estimated state vector at the current moment is obtained by a dynamic recursive principle, wherein the state vector includes the distance from the mobile terminal to the vehicle terminal at the previous moment and the rate of change of the distance; Based on the minimum mean square error principle, the estimated state vector at the current moment is weightedly fused with the signal strength value of the first signal and the signal strength value of the second signal to obtain the predicted state vector at the current moment, wherein the predicted state vector includes the distance from the mobile end to the vehicle end and the distance change rate at the current moment.

[0011] Optionally, performing weighted fusion on the estimated state vector at the current moment, the signal strength value of the first signal, and the signal strength value of the second signal to obtain the state vector at the current moment includes: Determining a nonlinear optimal weight by using a covariance of the estimated state vector and a covariance of an observation vector, wherein the observation vector includes a signal strength value of the first signal and a signal strength value of the second signal; The covariance of the estimated state vector and the observation vector are fused through the nonlinear optimal weight to obtain the predicted state vector.

[0012] Optionally, the method further includes: Controlling the execution of a vehicle unlocking operation based on a comparison result between the distance between the mobile terminal and the vehicle terminal and a preset first distance threshold; and or, Controlling the power system to wake up or sleep according to a comparison result between the distance between the mobile terminal and the vehicle terminal and a preset second distance threshold; and or, The power supply system is controlled to wake up or sleep according to a comparison result of the distance between the mobile terminal and the vehicle terminal and a preset third distance threshold.

[0013] In a second aspect, the present invention provides a distance measurement device based on a bidirectional signal, comprising: an acquisition module, configured to, based on a communication connection established with a mobile terminal, send a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and acquire a signal strength value of the second signal returned by the mobile terminal; The processing module is used to determine the distance from the mobile terminal to the vehicle terminal through a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the ranging method based on bidirectional signals as described in the first aspect when executing the computer program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the ranging method based on bidirectional signals as described in the first aspect is implemented.

[0016] The beneficial effects of the bidirectional signal-based ranging method, device, equipment, and storage medium of the present invention are as follows: based on a communication connection established between a vehicle and a mobile terminal, the vehicle receives a first signal sent by the mobile terminal and then transmits a second signal back to the mobile terminal, while simultaneously obtaining the signal strength value of the first signal. After receiving the second signal sent by the vehicle, the mobile terminal obtains the signal strength of the second signal and returns the signal strength of the second signal to the vehicle. By obtaining the signal strength values ​​of the first and second signals, the vehicle terminal can effectively avoid interference and errors caused by single signal transmission. Through the interaction of the two-way signals sent by the vehicle end and the mobile end respectively, the accurate signal strength values ​​received by each end are obtained, and then the signal strength values ​​of the two-way signals can more accurately reflect the actual situation in the signal transmission path, thereby improving the accuracy of the ranging from the mobile end to the vehicle end; further, based on the signal strength values ​​of the first signal and the second signal, the distance from the mobile end to the vehicle end is determined using the distance estimation fusion algorithm. The algorithm can integrate the signal strength data of the two-way signals, combine the signal propagation characteristics and environmental factors for weighted calculation and optimization processing, and effectively offset the ranging deviation caused by signal attenuation, obstruction, multipath effect, etc. Through the fusion and precise analysis of the two-way signal data, the accuracy of the ranging can be significantly improved, thereby providing a reliable basis for the accurate distance perception between the vehicle end and the mobile end. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a flow chart of a ranging method based on bidirectional signals according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the interaction process between the mobile terminal and the vehicle terminal according to an embodiment of the present invention; Figure 3 Schematic diagram of the structure of a distance measuring device based on bidirectional signals according to an embodiment of the present invention; Figure 4 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0020] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] In the related art, accurate distance determination between the mobile terminal and the vehicle is a key prerequisite for achieving safe and efficient access during vehicle access control. The currently mainstream one-way signal ranging methods, such as wireless signal transmission using a single frequency or phase, face numerous challenges in practical applications. Due to the multipath effect, signals travel through multiple paths to the receiving end during transmission. Signals from different paths overlap, resulting in distortion of the signal waveform. Complex and changing environmental factors, such as obstructions such as buildings, vegetation, or unexpected objects, absorb, reflect, and scatter wireless signals, causing drastic changes in signal strength. Furthermore, as transmission distance increases, signal attenuation becomes increasingly pronounced, causing unstable fluctuations in received signal strength. These combined factors severely interfere with the accuracy of distance measurement based on signal strength, thereby impacting the reliability and safety of vehicle access control systems.

[0024] In response to the problems existing in the above-mentioned related technologies, this embodiment provides a ranging method, apparatus, device and storage medium based on bidirectional signals.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a distance measurement method based on a bidirectional signal, which is applied to a vehicle side. The distance prediction method based on a bidirectional signal includes: S100 , based on a communication connection established with a mobile terminal, sending a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and obtaining a signal strength value of the second signal returned by the mobile terminal.

[0026] Specifically, the interaction between the vehicle and mobile devices is fundamental to implementing functions such as access control. The vehicle can establish a stable communication connection with the mobile device through wireless communication technologies such as Bluetooth, Wi-Fi, and Ultra-Wideband (UWB). This connection provides a channel for subsequent data exchange. When the mobile device initiates an access request, it sends a first signal to the vehicle. This signal can include a periodic signal such as a broadcast signal, a heartbeat signal, or a clock signal, ensuring a continuous communication connection between the mobile and vehicle devices. After receiving the first signal, the vehicle records the signal strength value of the first signal and immediately responds by transmitting a second signal back to the mobile device. The second signal can also include a periodic signal such as a broadcast signal, a heartbeat signal, or a clock signal, enabling bidirectional signal transmission with the mobile device. Upon receiving the second signal from the vehicle, the mobile device uses its built-in signal detection module to analyze and process it to accurately determine the signal strength of the second signal. The mobile device then transmits this signal strength value back to the non-vehicle device. The vehicle end can predict the distance from the mobile end to the vehicle end based on the signal strength value of the first signal and the signal strength value of the second signal, thereby providing data support for subsequent vehicle access control, smart unlocking and other operations.

[0027] It should be noted that when measuring distance using the signal strength of the bidirectional signal transmitted between the mobile device and the vehicle, obtaining accurate bidirectional signal strength values ​​is crucial. Distance calculation accuracy depends primarily on the accuracy and synchronization of the bidirectional signal strength values. Signal strength is closely related to distance, and signals attenuate with increasing distance during propagation. However, if the time interval between the acquired signal strengths of the first and second signals is too long, ranging accuracy may be compromised. For example, after acquiring the signal strength value of the first signal sent by the mobile device to the vehicle at time t, the signal strength value of the second signal received by the mobile device may be acquired 10 seconds later. During this 10-second time difference, the user carrying the mobile device may have moved to completely different locations. In this case, the signal strength values ​​acquired at these two different times correspond to different actual distances between the mobile device and the vehicle. Using these unsynchronized bidirectional signal strength values ​​as observation data for distance calculation will result in significant errors and fail to accurately reflect the actual distance between the mobile device and the vehicle at the same moment. Therefore, in order to obtain the accurate distance between the vehicle end and the mobile end, it is necessary to ensure the synchronization of the first signal and the second signal. The time threshold can be set in advance so that the vehicle end can synchronously receive the signal strength value of the first signal and the signal strength value of the second signal within the specified time threshold, thereby making the signal strength values ​​of the received two-way signals almost the same at the same time, minimizing the impact of position changes caused by time difference on the ranging results, thereby ensuring the accuracy of the ranging.

[0028] S200 , determining the distance from the mobile terminal to the vehicle terminal through a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0029] Specifically, based on a communication connection established between a mobile device and a vehicle, the mobile device sends a first signal to the vehicle. Upon receiving this signal, the vehicle uses its built-in signal strength detection module to obtain the signal strength of the first signal. This value reflects the signal strength of the mobile device's signal after attenuation due to environmental interference, multipath effects, and other factors during transmission. Subsequently, the vehicle responds to the first signal by transmitting a second signal back to the mobile device. The mobile device also uses its own signal strength detection module to obtain the signal strength of the second signal, which reflects the signal strength of the vehicle-side signal after attenuation during transmission. The vehicle-side system then calculates the accurate distance based on the signal strength values ​​of the first and second signals. Using a distance estimation fusion algorithm, the algorithm comprehensively considers wireless signal propagation models, such as the free-space propagation model and the logarithmic distance path loss model, as well as the impact of environmental factors (such as the degree of obstruction and signal reflection) on signal strength. Using weighted averaging, Kalman filtering, and machine learning techniques, the algorithm analyzes and fuses the two sets of signal strength values ​​to accurately calculate the actual distance from the mobile device to the vehicle, providing reliable data support for subsequent vehicle unlocking and starting operations.

[0030] It should be noted that in vehicle-side ranging, the distance estimation fusion algorithm integrates the signal strength data of the two-way signals interacting between the mobile terminal and the vehicle, and optimizes the data using weighting and filtering techniques based on signal propagation theory and environmental models. It can effectively eliminate ranging errors caused by factors such as occlusion, multipath effects, and environmental interference during signal transmission, and convert signal attenuation characteristics in complex environments into accurate distance parameters. This provides high-precision vehicle-to-mobile distance perception for scenarios such as autonomous driving, vehicle-road collaboration, and automatic parking, improving the safety and intelligence level of vehicle operation. Among them, distance estimation fusion algorithms include weighted averaging algorithms, Kalman filtering algorithms, and algorithms based on machine learning. Weighted average algorithm: Assign a corresponding weight to each measurement value based on factors such as the reliability and accuracy of different signal sources or different measurement values. Generally, measurements with higher accuracy and better stability are given larger weights, while measurements with lower accuracy or greater interference are given smaller weights. Then, all measurement values ​​are multiplied by their corresponding weights and added together, and then divided by the sum of all weights to obtain the fused distance estimate. For example, in the scenario where the distance from the mobile end to the vehicle end is measured by a two-way signal, if a signal has a higher measurement accuracy in a specific environment, its measurement value is given a higher weight. Finally, the measurement results of the two signals are combined by weighted averaging to obtain a more accurate distance estimate; Kalman filter algorithm: The distance estimation problem is regarded as a dynamic system, and the state changes and observation process of the system are described by establishing state equations and observation equations. It uses the state estimate at the previous moment and the observation at the current moment to update the state estimate at the current moment to minimize the mean square value of the estimation error. Specifically, the Kalman filter first predicts the current state based on the motion model of the system, and then The predicted value is compared with the actual observed value (the signal strength value of the first signal and the signal strength value of the second signal), and the predicted value is adjusted through a gain matrix to obtain a more accurate estimate. In the distance estimation from the mobile end to the vehicle end, the Kalman filter can effectively deal with problems such as the change of signal strength value over time and measurement noise, and continuously optimize the distance estimation result to make it closer to the true value; the algorithm based on machine learning: first collect a large amount of sample data containing signal strength values ​​and corresponding actual distances. These sample data cover a variety of different environmental conditions and signal propagation conditions. Then, use these sample data to train the machine learning model, such as artificial neural network, support vector machine and other models. During the training process, the model automatically learns the complex mapping relationship between signal strength value and distance. After the training is completed, when a new signal strength value is input, the model can output the corresponding distance estimate according to the learned mapping relationship. The algorithm based on machine learning can adapt to complex and changing environments and improve the accuracy of distance estimation through continuous learning and optimization, but it requires a large amount of sample data and high computing resources for training and inference.

[0031] In this embodiment, based on the communication connection established between the vehicle end and the mobile end, after the vehicle end obtains the first signal sent by the mobile end, the second signal is returned to the mobile end, and the signal strength value of the first signal is obtained. After the mobile end obtains the second signal sent by the vehicle end, the signal strength of the second signal is obtained and returned to the vehicle end. The vehicle end can effectively avoid the interference and error caused by single signal transmission by obtaining the signal strength value of the first signal and the signal strength value of the second signal. Through the interaction of the bidirectional signals sent by the vehicle end and the mobile end, the accurate signal strength values received by each other are obtained, and the signal strength values of the bidirectional signals can more accurately reflect the real situation in the signal transmission path, thereby improving the accuracy of the ranging from the mobile end to the vehicle end. Further, according to the signal strength values of the first signal and the second signal, a distance estimation fusion algorithm is used to determine the distance from the mobile end to the vehicle end. The algorithm can integrate the signal strength data of the bidirectional signals, combine the signal propagation characteristics and environmental factors for weighted calculation and optimization processing, effectively offset the ranging deviation caused by signal attenuation, shielding, multipath effect, etc. Through the fusion and accurate analysis of the bidirectional signal data, the accuracy of the ranging can be significantly improved, thereby providing a reliable basis for the accurate distance perception of the vehicle end and the mobile end.

[0032] Optionally, the response to the first signal sent by the mobile end comprises: After receiving the first signal sent by the mobile end, the signal strength value of the first signal is obtained, and the second signal is sent to the mobile end.

[0033] Specifically, the vehicle end receives the first signal sent by the mobile end through the communication connection established with the mobile end, and obtains the signal strength value of the received first signal. At the same time, the vehicle end immediately returns the second signal to the mobile end through the communication connection, so that the mobile end receives the second signal sent by the vehicle end within a preset time interval.

[0034] Exemplarily, as Figure 2As shown, assuming that the user carries a mobile terminal device integrated with a specific communication module, the mobile terminal device can be a smart phone 221, a smart watch 222, or a smart ring 223, or a wearable smart device or a portable mobile terminal 22. Taking a smart phone Bluetooth key as an example, of course, other suitable wireless communication technologies can also be used in addition to Bluetooth. First, the communication module of the vehicle end anchor point on the vehicle end 21 enters the working state and waits to establish a communication connection with the mobile terminal. When the user carrying the smart phone 221 approaches the vehicle within a certain range (such as a distance threshold set according to the effective distance of Bluetooth communication), the smart phone 221 broadcasts a first signal (broadcast signal) at a set frequency (for example, once every 5 seconds) through a first channel. After the communication module of the vehicle end 21 receives the broadcast signal, it immediately reads the signal strength value of the signal and records it. At the same time, the communication module of the vehicle end 21 returns a second signal (which can be a heartbeat signal) to the smart phone 221 through the second signal of the established Bluetooth communication connection within a very short time (such as milliseconds). After receiving the heartbeat signal returned by the vehicle end 21, the smart phone 221 also records the received signal strength value and feeds back the signal strength value data of the heartbeat signal to the vehicle end 21 through a second channel. After completing the signal interaction once, the vehicle end 21 stores the received broadcast signal strength value (the signal strength value of the first signal) and the received heartbeat signal strength value (the signal strength value of the second signal) fed back by the smart phone 221 in the data processing unit of the vehicle end 21. Subsequently, the data processing unit of the vehicle end 21 will calculate the actual distance from the smart phone 221 carried by the user to the vehicle end 21 according to the pre-set distance estimation fusion algorithm, and integrate the broadcast signal strength value and the heartbeat signal strength value and other data, so that the vehicle 21 can realize automatic unlocking, power system and power system operation according to the distance.

[0035] In this optional embodiment, the interaction between the first signal sent by the mobile terminal 22 and the second signal returned by the vehicle end 21 can effectively offset the signal attenuation error caused by environmental interference, multipath effect and other factors in the process of one-way signal transmission, and avoid accidental deviation of a single signal strength value due to external factors. Through fast and effective two-way signal interaction, the environmental conditions of the two signal transmissions can be ensured to be similar. By comparing the strength values of the first signal and the second signal, the two sets of data are analyzed and calibrated using the distance estimation fusion algorithm, so that the loss of the signal in the transmission process can be more accurately calculated, and the distance from the mobile terminal to the vehicle end 21 can be measured with high precision, providing reliable distance data support for the automatic unlocking, automatic parking and other functions of the vehicle 21.

[0036] Optionally, the communication connection includes a first channel and a second channel, and the second signal is sent through the second channel after the first signal sent by the mobile terminal is received through the first channel.

[0037] Specifically, the channel of the communication connection established between the vehicle end and the mobile end is used as the channel for signal transmission. During the signal transmission process, different channels have different frequencies, bandwidths and anti-interference characteristics. The first channel is specifically used to receive the first signal sent by the mobile end. Its frequency selection, filtering method and other parameters can be optimized according to the receiving requirements to ensure the integrity and stability of the received signal; the second channel is responsible for sending the second signal, and can also adjust the transmission power, modulation method, etc. in a targeted manner to ensure that the second signal is efficiently and stably transmitted to the mobile end, reduce interference, attenuation and distortion during signal transmission, ensure signal quality, and obtain the signal strength value data of the second signal returned by the mobile end through the second channel. The dual-channel mode avoids signal conflicts and delays that may occur when switching between single-channel transmission and reception, making the interaction time between the first and second signals more precise and controllable. At the same time, the differences in environmental interference factors encountered by the two sets of signals when transmitted through independent channels can also be utilized by the distance estimation fusion algorithm. By comprehensively analyzing and calibrating data such as signal strength values ​​transmitted through different channels, errors caused by the channel's own characteristics and environmental factors can be effectively eliminated, achieving more accurate measurement of the distance from the mobile end to the vehicle end, and providing reliable distance data support for the vehicle's intelligent interaction functions.

[0038] For example, taking a Bluetooth signal connection as an example, a Bluetooth low energy connection has 40 channels, indexed from 0 to 39, with a 2MHz interval, starting at 2402MHz. Channels 37, 38, and 39 are dedicated advertising channels (primary channels) for transmitting advertising signals. Each broadcast event sequentially transmits data on these three channels, starting with the lowest channel number, i.e., channels 37, 38, and 39. Furthermore, starting with Bluetooth 5.0, all data channels other than the dedicated advertising channel can also be used for advertising, referred to as secondary advertising channels. Heartbeat signals are typically transmitted on the data channel (secondary channel) after a Bluetooth device successfully establishes a connection. While connected, devices exchange data at regular intervals. The heartbeat signal, a signal that maintains the connection and synchronization, is transmitted on the data channel along with other application data.

[0039] In this optional embodiment, since different channels may differ in frequency, bandwidth, and anti-interference characteristics, the first channel can be optimized for reception requirements. By accurately selecting frequencies and enhancing filtering, the impact of external interference on the reception of the first signal can be minimized, thereby obtaining a more realistic and accurate first signal strength value. The second channel is specifically used to send the second signal. Parameters such as the transmission power and modulation method can be adjusted according to the signal transmission characteristics to keep the second signal stable during transmission, reduce signal attenuation and distortion, and thereby make the second signal strength value received by the mobile terminal more accurate. In addition, independent channels avoid the signal loss and errors that may be introduced by switching between single-channel transmission and reception, allowing the signal strength values ​​obtained at both ends to more accurately reflect the signal transmission status, providing a reliable data basis for subsequent operations such as ranging based on signal strength values.

[0040] Optionally, obtaining the signal strength value of the second signal returned by the mobile terminal includes: The signal strength value of the second signal sent by the vehicle end is received through the second channel, wherein the signal strength value of the second signal is the signal strength value of the second signal obtained after the mobile end receives the second signal.

[0041] In this optional embodiment, firstly, the independent second channel can reduce interference and conflict during signal transmission. Since the second channel can be optimized according to the transmission characteristics of the second signal, such as selecting the appropriate frequency band, modulation method and transmission power, it can ensure the stable transmission of the second signal to the mobile terminal, so that the acquired signal strength value is closer to the actual transmission state. Secondly, the mobile terminal directly obtains the second signal strength value, which can avoid signal strength data errors caused by intermediate link processing or single channel switching, and ensure the timeliness and accuracy of data acquisition. At the same time, when analyzing the two sets of data based on the distance estimation fusion algorithm in combination with the first signal strength value, the accurate acquisition of the second signal strength value can help the algorithm more accurately calculate the signal loss during transmission, eliminate errors caused by environmental factors, channel characteristics, etc., thereby achieving high-precision measurement of the distance between the mobile terminal and the vehicle terminal, and providing reliable distance data support for vehicle precision control, intelligent interaction and other functions.

[0042] Optionally, determining the distance from the mobile terminal to the vehicle terminal by using a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal includes: Based on the state vector obtained at the previous moment, an estimated state vector at the current moment is obtained by a dynamic recursive principle, wherein the state vector includes the distance from the mobile terminal to the vehicle terminal at the previous moment and the rate of change of the distance; Based on the minimum mean square error principle, the estimated state vector at the current moment is weightedly fused with the signal strength value of the first signal and the signal strength value of the second signal to obtain the predicted state vector at the current moment, wherein the predicted state vector includes the distance from the mobile end to the vehicle end and the distance change rate at the current moment.

[0043] Specifically, the dynamic recursive principle uses historical data and the current state to deduce future states. It encompasses algorithms such as the Kalman filter, the extended Kalman filter, and the unscented Kalman filter. Assuming that distance changes uniformly over time and accounting for process noise, the current estimated state vector is derived using a pre-defined state transition relationship.

[0044] The state transition relationship satisfies: ; Among them, f() is the state transition relationship, Δt is the time interval between the current moment and the previous moment, is the state vector of the previous moment, d k-1 is the distance from the mobile end to the vehicle end at the previous moment, is the rate of change of the distance from the mobile end to the vehicle end at the previous moment, is the process noise, ω 1，k-1 is the first process noise component, ω 2，k-1 is the second process noise component, which has a mean of 0 and an initial state of ω0=[0, 0] T .

[0045] According to the state vector, a partial derivative of the state transfer relationship is obtained to obtain the state transfer matrix, and the state transfer matrix is ​​multiplied by the state vector at the previous moment to obtain the estimated state vector at the current moment.

[0046] The state transition matrix satisfies: ; The estimated state vector satisfies: ; Among them, F k-1 is the state transition matrix, is the estimated state vector at the current moment, d k|k-1 is the estimated distance from the mobile terminal to the vehicle terminal at the current moment, is the estimated distance change rate from the mobile terminal to the vehicle terminal at the current moment, k is the current moment, and k-1 is the previous moment.

[0047] Furthermore, based on the principle of minimum mean square error, the estimated state vector x at the current moment is k|k-1 and the observation vector z k Fusion is performed to obtain the predicted state vector x at the current moment k, wherein the observation vector z k = [RSSI A-B , RSSI B-A ] T , RSSI A-B is a signal strength value of the first signal received by the vehicle end from the mobile end, RSSI B-A is a signal strength value of the second signal received by the mobile end from the vehicle end, A is the mobile end, B is the vehicle end, and the predicted state vector , d x is the actual distance from the mobile end to the vehicle end at the current time, is the distance change rate from the mobile end to the vehicle end at the current time.

[0048] In the optional embodiment, the state vector at the previous time is obtained, and the estimated state vector at the current time is obtained by using the dynamic recursive principle. At the same time, the signal strength value of the first signal and the signal strength value of the second signal are combined, and the accurate predicted state vector at the current time is obtained by weighted fusion based on the minimum mean square error principle. The dynamic recursion fully considers the distance from the mobile end to the vehicle end at the previous time and the distance change rate, and continuously updates the state vector in combination with the time factor, which can better fit the actual motion trend of the mobile end, and is more timely and accurate in tracking the distance change compared with relying on single time data, thereby effectively reducing the ranging error caused by the movement of the mobile end. Moreover, the estimated state vector is weighted and fused with the signal strength value of the first signal and the signal strength value of the second signal, and the signal strength values of the two-way signals are fully utilized. The signal strength value of the first signal reflects the signal transmission condition from the mobile end to the vehicle end, and the signal strength value of the second signal reflects the signal transmission condition from the vehicle end to the mobile end. The combination of the two-way signals can more comprehensively consider various influencing factors in signal propagation, such as multipath fading, non-line-of-sight propagation, etc., thereby reducing the ranging deviation caused by signal interference. The application of the minimum mean square error principle ensures that the predicted state vector is more close to the real situation in the fusion process, thereby greatly improving the ranging accuracy and providing more reliable data support for the intelligent control function of the vehicle based on distance.

[0049] Optionally, the weighted fusion of the estimated state vector at the current time and the signal strength value of the first signal and the signal strength value of the second signal to obtain the state vector at the current time comprises: determining a nonlinear optimal weight through the covariance of the estimated state vector and the covariance of the observation vector, wherein the observation vector comprises the signal strength value of the first signal and the signal strength value of the second signal; fusing the covariance of the estimated state vector and the observation vector through the nonlinear optimal weight to obtain the predicted state vector.

[0050] Specifically, the observation relationship is constructed through the observation vector, and the partial derivative of the observation relationship is calculated according to the estimated distance from the mobile terminal to the vehicle terminal at the current moment to obtain the observation matrix.

[0051] The observation relationship satisfies: ; The observation matrix satisfies: ; Among them, h() is the observation relationship, γ k =[γ 1，k , γ 2，k ] T is the observation noise, which has a mean of 0 and an initial state of γ0=[γ0,γ0] T , RSSI def，A-B RSSI is the reference signal strength value from the mobile terminal to the vehicle terminal. def，A-B is the reference signal strength value from the mobile terminal to the vehicle terminal, n A-B is the path loss index from the mobile terminal to the vehicle terminal, n B-A is the path loss index from the vehicle end to the mobile end, H k is the observation matrix.

[0052] And, according to the state vector x at the previous moment k-1 The covariance of the previous moment is the state covariance matrix P k-1 , according to the state covariance matrix P at the previous moment k-1 and the state matrix F at the previous moment k-1 Perform matrix multiplication to obtain the current prediction covariance matrix P k|k-1 , according to the prediction covariance matrix P k|k-1 and the observation matrix H k , the nonlinear optimal weight is obtained through the preset weight relationship.

[0053] The prediction covariance matrix satisfies: ; The nonlinear optimal weight satisfies: ; The observation vector covariance satisfies: ; Among them, Q k-1 is the process noise covariance, R k The observation vector covariance matrix, is the variance of the signal strength value of the first signal, is the variance of the signal strength values ​​of the second signal.

[0054] Furthermore, the calculated nonlinear optimal weight K k The estimated state vector x at the current moment k|k-1 and the observation vector z x Perform weighted fusion to obtain the predicted state vector x at the current moment k , thus obtaining the accurate distance d from the mobile terminal to the vehicle terminal at the current moment x .

[0055] The predicted state vector satisfies: ; The estimated state vector x at the current moment k|k-1 The weighted fusion is performed with the observation vector to obtain the predicted state vector x at the current moment k , where the predicted state vector x k Including the actual distance d from the mobile end to the vehicle end at the current moment x and the corresponding distance change rate .

[0056] In this optional embodiment, the weights can be dynamically adjusted based on the covariance of the estimated state vector and the covariance of the observation vector, depending on the actual environment. When the mobile device moves rapidly, the uncertainty of the estimated state vector increases, and the covariance matrix becomes larger. In this case, the algorithm reduces the weight of the prediction model. Conversely, when the signal environment is complex and the Received Signal Strength Indicator (RSSI) fluctuates dramatically, the covariance of the observation vector increases, and the algorithm reduces the impact of the signal strength value in the observed data. This adaptive adjustment mechanism avoids the bias that may be introduced by fixed weights, making the fusion result closer to the actual distance. Furthermore, the nonlinear optimal weight can capture complex nonlinear relationships in signal propagation, such as multipath and non-line-of-sight propagation, which are difficult to effectively handle with traditional linear weighting methods. By considering the complete information of the covariance matrix (rather than simply the mean or variance), the algorithm can more accurately assess the reliability of each data source, achieving optimal fusion in various scenarios. For example, when there are metal obstacles around the vehicle, the attenuation characteristics of the bidirectional RSSI signal vary greatly. Nonlinear weighting can more reasonably distribute the contributions of the two, thereby significantly improving the ranging accuracy, especially in complex environments.

[0057] For example, when actually measuring the distance between the mobile terminal and the vehicle terminal, initialization is first performed to determine the initial communication distance d0 of the communication signal used for ranging. The initial communication distance d0 can be determined in combination with the actual effective propagation distance of the communication signal and the actual usage distance requirement of the vehicle digital key.

[0058] The initial distance satisfies: ; Among them, RSSI ref RSSI is the reference value of the initial signal strength value received at 1 meter. d is the signal strength value when the distance between the mobile terminal and the vehicle terminal is d, and n is the path loss index.

[0059] At the same time, the initial signal strength value RSSI can be calculated based on a preset error function through an optimization algorithm, such as an optimization algorithm for a nonlinear least squares problem (Levenberg-Marquardt, LM). ref and path loss index n to optimize, so as to find the initial signal strength value RSSI with the minimum error function ref and path loss exponent n. The initial state vector , wherein the rate of change of the initial distance of the initial communication distance d0 can be 0.

[0060] The initial covariance corresponding to the initial state vector satisfies: ; The error function satisfies: ; Where P0 is the initial covariance, is the variance of the initial distance d0, and a value can be estimated according to the distance measurement situation, for example, it can be 0, 1 or other values. is the initial distance change rate The variance of the process noise is likely to be close to 0.01 (m / s)² for adults in a flat, barrier-free, and familiar indoor environment. If the pace needs to be adjusted occasionally during walking, if there are external interferences, or if the walking route is not absolutely straight, the speed fluctuation will be relatively large, possibly reaching 0.04 (m / s)². The process noise is unpredictable, so the process noise covariance can be initially set to an all-zero square matrix. E() is the error function, RSSI i is the signal strength value received at the i-th measurement point, m is the number of measurement points, d i is the distance between the i-th measurement point and the vehicle end.

[0061] Furthermore, according to the state vector x at the previous moment k-1 and the state matrix F at the previous moment k-1 The estimated state vector x at the current moment is obtained through the preset estimated state vector relationship k|k-1 At the same time, according to the state matrix F at the previous moment k-1 and the state covariance matrix P k-1 , the current prediction covariance matrix P is obtained through the preset prediction covariance relationship k|k-1 , and then through the current moment prediction covariance matrix P k|k-1, observation vector covariance matrix R k and the observation matrix H k , the nonlinear optimal weight K is obtained through the preset nonlinear optimal weight relationship k , and finally according to the estimated state vector x obtained previously k|k-1 , observation vector z x and nonlinear optimal weight K k , through the preset state prediction relationship, the predicted state vector x at the current moment is obtained k , so that the predicted state vector x k Get the exact distance from the mobile terminal to the vehicle terminal at the current moment.

[0062] In addition, the covariance matrix at the current moment is updated by predicting the covariance matrix, and the update relationship satisfies: ; Where I is the identity matrix.

[0063] When determining the covariance of the observation vector, it is necessary to pre-establish an exponential model for how the variance of RSSI changes with RSSI. This model can predict that as the RSSI decreases, the variance value will gradually increase, which is consistent with the actual situation. Furthermore, the nonlinear optimal weight can be controlled by controlling the variance. That is, the smaller the RSSI value, the farther away from the vehicle, and vice versa. For example, in wireless communication, the signal gradually attenuates as the distance increases during propagation. For example, when a vehicle digital key communicates with the vehicle via Bluetooth, when the digital key is close to the vehicle, the signal loss during transmission is small, the received signal strength is high, and the RSSI value is large. Conversely, when the digital key is far from the vehicle, the signal attenuation is large, and the RSSI value is small. Therefore, the distance to the vehicle can be roughly judged based on the size of the RSSI value. By controlling the covariance matrix R of the observation vector, the signal attenuation is large, and the RSSI value is small. k Can well adjust the nonlinear optimal weight K k , thereby controlling the final predicted state vector x k , so that the distance from the vehicle to the mobile terminal at the current moment is closer to the real value.

[0064] The exponential model satisfies: ; ; Among them, a A-B is the first coefficient, a B-A is the second coefficient, b A-B is the third coefficient, b B-A is the fourth coefficient, which can be obtained by data fitting or experimental calibration.

[0065] Optionally, the method further includes: Controlling the execution of a vehicle unlocking operation based on a comparison result between the distance between the mobile terminal and the vehicle terminal and a preset first distance threshold; and or, Controlling the power system to wake up or sleep according to a comparison result between the distance between the mobile terminal and the vehicle terminal and a preset second distance threshold; and or, The power supply system is controlled to wake up or sleep according to a comparison result of the distance between the mobile terminal and the vehicle terminal and a preset third distance threshold.

[0066] Specifically, the vehicle unlocking operation is controlled based on the current distance between the mobile device and the vehicle: First, a pre-set distance value, serving as a first distance threshold, is set. When the distance between the vehicle and the mobile device is detected to be less than this threshold, it indicates that the user carrying the mobile device has approached the vehicle to a certain distance, and the vehicle control system determines that the unlocking condition has been met. For example, if the owner approaches the vehicle with the digital key and the distance shortens to within the first distance threshold (assuming it is 1.5 meters), the vehicle automatically unlocks, allowing the owner to open the door and enter. Conversely, if the distance exceeds this threshold by a certain range, the vehicle may automatically lock to ensure vehicle safety.

[0067] Controlling the powertrain's wakeup or sleep mode based on the distance between the mobile device and the vehicle: The second distance threshold is set for powertrain operation. When the distance between the vehicle and the mobile device meets a specific condition (i.e., less than the second distance threshold), indicating that the owner has moved into the valid range, the vehicle's powertrain can be awakened and activated. For example, the vehicle's powertrain will only be awakened when the mobile device is within 3 meters of the vehicle (the assumed second distance threshold). This means that the vehicle's engine can start normally when the owner presses the start button. If the distance exceeds this threshold, the vehicle's powertrain remains dormant. Even if the start button is pressed, the system will determine that the start conditions are not met and prevent the powertrain from starting.

[0068] Controlling the power system's wakeup or sleep mode based on the distance between the mobile device and the vehicle: The third distance threshold is used to control the power system, which includes some of the vehicle's auxiliary power functions, such as interior lighting and multimedia system power. When the distance between the mobile device and the vehicle falls below the third distance threshold, the system can wake up the vehicle's power system according to settings, and the vehicle system can automatically activate some power functions. For example, when the driver approaches the vehicle within 2 meters (the assumed third distance threshold), the vehicle's power system wakes up and can automatically activate the interior ambient lighting to create a comfortable boarding environment. When the distance exceeds this threshold, the vehicle's power system goes into sleep mode, and some non-essential power systems may automatically shut down to conserve power. The first, second, and third distance thresholds can be set separately or as the same distance threshold based on the driver's actual needs.

[0069] In this optional embodiment, the vehicle is controlled to perform unlocking, power system and power supply system operations respectively based on the comparison results between the distance between the vehicle and the mobile terminal and different preset distance thresholds. In terms of unlocking operation, keyless and convenient entry is achieved, which improves the user experience while ensuring the safe locking of the vehicle; in terms of power system operation control, it can effectively prevent illegal remote start-up, ensure reasonable start-up conditions, and avoid abnormal start-up failures; in terms of power system operation control, intelligent power management can be achieved, and the power supply function can be turned on or off as needed, which not only creates a comfortable use environment but also saves electricity, thereby improving the convenience, safety and energy efficiency of vehicle use as a whole. like Figure 3 As shown, an embodiment of the present invention provides a bidirectional signal-based ranging device 300, including: an acquisition module 310 configured to, based on a communication connection established with a mobile terminal, send a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and acquire a signal strength value of the second signal returned by the mobile terminal; The adjustment module 320 is configured to determine the distance from the mobile terminal to the vehicle terminal by using a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0070] The bidirectional signal-based ranging device of this embodiment is used to implement the bidirectional signal-based ranging method described above. Its advantages over the prior art are the same as those of the bidirectional signal-based ranging method described above over the prior art, and will not be repeated here.

[0071] like Figure 4 As shown, an electronic device 400 provided by an embodiment of the present invention includes a memory 410 and a processor 420; the memory 410 is used to store a computer program; the processor 420 is used to implement the above-mentioned ranging method based on bidirectional signals when executing the computer program.

[0072] In other words, an electronic device 400 includes a memory 410 and a processor 420 coupled to the memory 410; the memory 410 is configured to store a computer program; and the processor 420 is configured to perform the following operations when executing the computer program: Based on the communication connection established with the mobile terminal, sending a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and obtaining a signal strength value of the second signal returned by the mobile terminal; The distance from the mobile terminal to the vehicle terminal is determined by a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0073] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the foregoing bidirectional signal-based ranging method is implemented.

[0074] In other words, a non-volatile computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following operations: Based on the communication connection established with the mobile terminal, sending a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and obtaining a signal strength value of the second signal returned by the mobile terminal; The distance from the mobile terminal to the vehicle terminal is determined by a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

[0075] An electronic device 400 that can serve as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 400 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 400 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0076] Electronic device 400 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). In this application, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these units can be selected based on actual needs to achieve the objectives of the embodiments of the present invention. Furthermore, the functional units in the various embodiments of the present invention can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. These integrated units can be implemented in either hardware or software functional units.

[0078] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A ranging method based on bidirectional signals, characterized in that: Applied to the vehicle side, the distance prediction method based on bidirectional signals includes: Based on the communication connection established with the mobile terminal, sending a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and obtaining a signal strength value of the second signal returned by the mobile terminal; The distance from the mobile terminal to the vehicle terminal is determined by a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

2. The distance measurement method based on bidirectional signals according to claim 1, characterized in that: The sending a second signal to the mobile terminal in response to the first signal sent by the mobile terminal includes: After receiving the first signal sent by the mobile terminal, the signal strength value of the first signal is obtained, and the second signal is sent to the mobile terminal.

3. The distance measurement method based on bidirectional signals according to claim 2, characterized in that: The communication connection includes a first channel and a second channel. After receiving the first signal sent by the mobile terminal through the first channel, the second signal is sent through the second channel.

4. The distance measurement method based on bidirectional signals according to claim 3, characterized in that: The acquiring the signal strength value of the second signal returned by the mobile terminal includes: The signal strength value of the second signal sent by the vehicle end is received through the second channel, wherein the signal strength value of the second signal is the signal strength value of the second signal obtained after the mobile end receives the second signal.

5. The distance measurement method based on bidirectional signals according to claim 1, wherein: The determining the distance from the mobile terminal to the vehicle terminal by using a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal includes: Based on the state vector obtained at the previous moment, an estimated state vector at the current moment is obtained by a dynamic recursive principle, wherein the state vector includes the distance from the mobile terminal to the vehicle terminal at the previous moment and the rate of change of the distance; Based on the minimum mean square error principle, the estimated state vector at the current moment is weightedly fused with the signal strength value of the first signal and the signal strength value of the second signal to obtain the predicted state vector at the current moment, wherein the predicted state vector includes the distance from the mobile end to the vehicle end and the distance change rate at the current moment.

6. The distance measurement method based on bidirectional signals according to claim 5, characterized in that: The step of performing weighted fusion of the estimated state vector at the current moment, the signal strength value of the first signal, and the signal strength value of the second signal to obtain the state vector at the current moment includes: Determining a nonlinear optimal weight by using a covariance of the estimated state vector and a covariance of an observation vector, wherein the observation vector includes a signal strength value of the first signal and a signal strength value of the second signal; The covariance of the estimated state vector and the observation vector are fused through the nonlinear optimal weight to obtain the predicted state vector.

7. The distance measurement method based on bidirectional signals according to claim 1, characterized in that: Also includes: Controlling the execution of a vehicle unlocking operation based on a comparison result of the distance between the mobile terminal and the vehicle terminal and a preset first distance threshold; and or, Controlling the power system to wake up or sleep according to a comparison result between the distance between the mobile terminal and the vehicle terminal and a preset second distance threshold; and or, The power supply system is controlled to wake up or sleep according to a comparison result of the distance between the mobile terminal and the vehicle terminal and a preset third distance threshold.

8. A distance measuring device based on a bidirectional signal, characterized in that: include: an acquisition module, configured to, based on a communication connection established with a mobile terminal, send a second signal to the mobile terminal in response to a first signal sent by the mobile terminal, and acquire a signal strength value of the second signal returned by the mobile terminal; The processing module is used to determine the distance from the mobile terminal to the vehicle terminal through a distance estimation fusion algorithm according to the signal strength value of the first signal and the signal strength value of the second signal.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the ranging method based on bidirectional signals according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the ranging method based on bidirectional signals according to any one of claims 1 to 7 is implemented.

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