RSSI-based distance measurement method and system, vehicle and vehicle approach sensing system

Abstract

The invention provides a distance measurement method and system based on RSSI, a vehicle and a vehicle approach sensing system, and relates to the technical field of wireless positioning, and the distance measurement method based on RSSI comprises the steps: building a path loss optimization model; and determining parameters of the path loss optimization model by using a gradient descent iterative formula, and substituting the obtained parameters into the established path loss optimization model to perform distance measurement operation. According to the RSSI-based distance measurement method provided by the invention, an existing logarithmic normal path loss model can be simplified, random noise in the logarithmic normal path loss model is omitted, and a path loss optimization model is established, so that the distance estimation is more rapid and accurate; the problem of large parameter errors can be effectively avoided, and the parameter determination efficiency can be effectively improved. Meanwhile, the path loss optimization model can be widely applied to various wireless positioning scenes and has good universality.

Description

Technical Field

[0001] The present application relates to the field of wireless positioning technology, and in particular to a ranging method, system, vehicle and vehicle approach sensing system based on RSSI. Background Art

[0002] As a wireless positioning method, the RSSI (Received Signal Strength Indicator) ranging method has many characteristics such as low cost, no need for additional expensive hardware support, simple operation and wide application range.

[0003] The basic principle of the RSSI ranging method can be simplified as follows: when a wireless signal is transmitted from the transmitter to the receiver, the signal strength will attenuate as the propagation distance increases. By measuring the received signal strength indicator, the distance between the transmitter and the receiver can be calculated. In other words, given the signal transmission power and the signal power received by the node, the distance between the nodes can be calculated by establishing a path loss model between the signal and the distance.

[0004] At present, in the related art, the path loss model generally includes a free space path loss model (Free Space Path Loss Model, FSPL) and a log-normal path loss model (Log-Distance Path Loss Model).

[0005] Among them, for the free space path loss model, it assumes that the signal propagates in an ideal environment without any obstacles or reflections, and the signal loss is only related to the propagation distance. Obviously, this model is only suitable for theoretical reference analysis and cannot meet the actual positioning needs.

[0006] For the log-normal path loss model, the log-normal path loss model is deeply influenced by the parameter and However, in the existing related technologies, these two parameters are generally determined based on operation or test experience. Obviously, the parameters determined based on experience and Will lead to the following problems: First, the efficiency of parameter determination is low. It often takes multiple parameter adjustments, simulations, and tests to determine the available parameters, and the optimality of the parameters cannot be guaranteed.

[0007] Second, the parameters determined have large errors and poor universality. Past experience may have statistical bias and human error problems, which will lead to large errors in the parameters determined. At the same time, as the environment and technology change, the parameters determined based on past experience may not be adapted to the current environment or technology, and there are problems of large errors and poor universality. Summary of the invention

[0008] In order to solve at least some of the technical problems in the related art, the present application provides a ranging method, system, vehicle and vehicle approach perception system based on RSSI.

[0009] In order to achieve the above objectives, the technical solutions adopted in this application include: According to a first aspect of the present application, a ranging method based on RSSI is provided, comprising the following steps: Step S1: Establish a path loss optimization model using the following formula: In the formula, is the distance between the signal sending end and the signal receiving end, is the RSSI reference value at a distance of one meter. RSSI is the received signal strength indicator. is the path loss factor; Step S2: Determine the parameters of the path loss optimization model using the gradient descent iterative formula and : In the formula, represents the parameters of the next generation, represents the parameters of the previous generation, is the learning rate, is the number of signal senders, For the The distance between the signal sending end and the signal receiving end, For the The RSSI value between the signal sending end and the signal receiving end; Step S3: The parameters obtained in step S2 and Substitute into the path loss optimization model established in step S1 to perform ranging operation.

[0010] Optionally, the RSSI-based ranging method further includes: Step S0: Use Gaussian filtering combined with mean filtering to pre-process RSSI data.

[0011] Optionally, the step S0 specifically includes: Step S0-1: using Gaussian filtering method and mean filtering method to filter the acquired RSSI data respectively; Step S0-2: taking the average value of the filtered data obtained in step S0-1 as the final filtering result.

[0012] Optionally, the RSSI-based ranging method further includes: Step S4-1: Evaluate the model fitting effect according to the following formula: In the formula, is the coefficient of determination, is the regression sum of squares, is the total sum of squared deviations, For the The actual distance between the signal sending end and the signal receiving end, is the average value of the true distance, For the The model-predicted distance between a signal transmitter and a signal receiver; Step S4-2: If If the value of is greater than the preset threshold, the fitting effect is considered qualified.

[0013] Optionally, the preset threshold is set to 0.8.

[0014] According to the second aspect of the present invention, there is also provided a ranging system based on RSSI, which is applied to the ranging method based on RSSI described in any technical solution in the first aspect of the present invention, and the ranging system based on RSSI includes: A data acquisition module is used to obtain the RSSI value from the signal sending end to the signal receiving end; Data processing module, used to establish the path loss optimization model and determine the parameters and .

[0015] According to a third aspect of the present invention, there is further provided a vehicle, comprising the RSSI-based ranging system described in the second aspect of the present invention, wherein the data acquisition module and the data processing module are arranged on the vehicle.

[0016] Optionally, the vehicle further includes a smart key, on which a signal transmitting module is provided for sending a signal to a corresponding signal receiving module on the vehicle.

[0017] According to a fourth aspect of the present invention, there is also provided a vehicle approaching sensing system, comprising: A data sending module, used to be set in a mobile unlocking terminal matched with the vehicle; A data receiving module, used to be arranged on the vehicle and used to receive the signal sent by the data sending module; A data acquisition module, used to be arranged on the vehicle and used to acquire the RSSI value of the signal received by the data receiving module; The data processing module is used to be set on a vehicle and is used to execute the RSSI-based ranging method described in any technical solution in the first aspect of the present invention.

[0018] Beneficial effects: 1. First, compared with the log-normal path loss model in the prior art, the path loss optimization model established by the present invention optimizes it and omits the random noise in the log-normal path loss model. , to highlight the parameters and The effect of this method can achieve faster and more accurate distance estimation, which can not only effectively avoid the problem of large parameter errors, but also effectively improve the parameters. and Determine efficiency.

[0019] Second, the path loss optimization model established by the present invention is based on the log-normal distribution, which can effectively incorporate the random fluctuations in signal propagation caused by environmental factors, and can be widely applied to a variety of wireless positioning scenarios (for example, indoor, outdoor, macro and micro cellular systems, etc.), with good versatility. At the same time, this path loss optimization model is highly flexible, and the model can be optimized by adjusting parameters to accurately describe the path loss in the signal propagation process in different scenarios.

[0020] Third, the parameters of the path loss optimization model of the present invention are obtained through a gradient descent iterative formula based on measured data, which can enable the path loss optimization model of the present invention to effectively adapt to the current usage environment (scenario) and effectively avoid the problem of large parameter errors.

[0021] 2. Other beneficial effects or advantages of the present application will be described in detail in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] in: Figure 1 is a schematic diagram of the steps of a ranging method based on RSSI provided by an exemplary embodiment of the present application; Figure 2 is a schematic diagram of an indoor data measurement scenario provided by an exemplary embodiment of the present application; Figure 3 is a schematic diagram of an outdoor data measurement scenario provided by an exemplary embodiment of the present application; Figure 4 is a graph of measured indoor data provided by an exemplary embodiment of the present application; Figure 5 is a graph of measured outdoor data provided by an exemplary embodiment of the present application; Figure 6 is an indoor path loss optimization model fitting curve provided by an exemplary embodiment of the present application; Figure 7 is an outdoor path loss optimization model fitting curve provided by an exemplary embodiment of the present application; Figure 8 It is a schematic diagram of an optimized signal effect of mean filtering provided by an exemplary embodiment of the present application; Fig. 9 It is a schematic diagram of the optimized signal effect of median filtering provided by an exemplary embodiment of the present application; Fig.10 is a schematic diagram of an optimized signal effect of Gaussian filtering provided by an exemplary embodiment of the present application; Fig.11 It is a schematic diagram of the optimized signal effect of the mean combined with the median filtering provided by an exemplary embodiment of the present application; Fig.12 It is a schematic diagram of the optimized signal effect of Gaussian combined with mean filtering provided by an exemplary embodiment of the present application; Fig.13 It is a schematic diagram of the optimized signal effect of Gaussian combined with median filtering provided by an exemplary embodiment of the present application; Fig.14 It is a schematic diagram of the optimized signal effect of Gaussian combined with mean + median filtering provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0025] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0026] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application 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 limited to the listed steps or units, but optionally includes other steps or units that are not listed, or optionally includes other steps or units that are inherent to these processes, methods, products or devices. Among them, it should also be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0027] In order to facilitate relevant technical personnel to have a clearer and more accurate understanding of the technical solution of the present invention, the log-normal path loss model and its parameter determination process in the existing related technology are first described below.

[0028] For the log-normal path loss model, the mathematical formula can be expressed as: in, Indicates the distance from the signal sending end to the receiving end. Represents the reference distance. Generally, You can take 1m. and They represent the distance from the receiving end to the sending end. and The signal power at It is the path loss factor, and its specific value needs to be determined according to the environment. The mean is 0 and the standard deviation is A normally distributed random variable with , representing the shadow effect.

[0029] Distance signal transmitter The RSSI value received at can be expressed as: in, is the transmission power of the signal, when Pick When (generally, The distance is 1 meter), it can be expressed as: in, Indicates the reference distance RSSI value at meter.

[0030] From the above three equations, the relationship between RSSI and distance is: According to the above formula, the lognormal path loss model is deeply influenced by the parameter and These two parameters play a crucial role in determining the relationship between RSSI and distance. Therefore, the parameters and The selection of is very important for building the model.

[0031] In existing related technologies, researchers usually set the above parameters based on past experience, but this also leads to problems such as low parameter efficiency, large errors in the determined parameters and poor versatility.

[0032] In view of this, the present invention provides a new solution, which is based on the log-normal path loss model, first simplifying it, and then combining the measured data and the gradient descent method to determine the parameters in the model. and , in order to build a unified path loss model. In order to quickly and accurately obtain a path loss model with small error and good universality.

[0033] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0034] Example 1 According to the first aspect of the present application, Figure 1 As shown, a ranging method based on RSSI is provided, comprising the following steps: Step S1: Establish a path loss optimization model using the following formula: In the formula, is the distance between the signal sending end and the signal receiving end, is the RSSI reference value at a distance of one meter. RSSI is the received signal strength indicator. is the path loss factor; Step S2: Determine the parameters of the path loss optimization model using the gradient descent iterative formula and : In the formula, represents the parameters of the next generation, represents the parameters of the previous generation, is the learning rate, is the number of signal senders, For the The distance between the signal sending end and the signal receiving end, For the The RSSI value between the signal sending end and the signal receiving end; Step S3: The parameters obtained in step S2 and Substitute into the path loss optimization model established in step S1 to perform ranging operation.

[0035] Through the above technical solution, firstly, compared with the log-normal path loss model in the existing related technology, the path loss optimization model established by the present invention optimizes it and omits the random noise in the log-normal path loss model. , to highlight the parameters and The effect of this method can achieve faster and more accurate distance estimation, which can not only effectively avoid the problem of large parameter errors, but also effectively improve the parameters. and Determine efficiency.

[0036] Second, the path loss optimization model established by the present invention is based on the log-normal distribution, which can effectively incorporate the random fluctuations in signal propagation caused by environmental factors, and can be widely applied to a variety of wireless positioning scenarios (for example, indoor, outdoor, macro and micro cellular systems, etc.), with good versatility. At the same time, this path loss optimization model is highly flexible, and the model can be optimized by adjusting parameters to accurately describe the path loss in the signal propagation process in different scenarios.

[0037] Third, the parameters of the path loss optimization model of the present invention are obtained through a gradient descent iterative formula based on measured data, which can enable the path loss optimization model of the present invention to effectively adapt to the current usage environment (scenario) and effectively avoid the problem of large parameter errors.

[0038] To facilitate understanding by relevant technical personnel, the following describes how the present invention optimizes the log-normal path loss model in the existing related technology to obtain the path loss optimization model of the present invention.

[0039] For the lognormal path loss model, the random noise It is random, and its size and direction are uncertain. In the case of a large amount of data, will be averaged out, that is, For the entire log-normal path loss model, its effect is small. And RSSI reference value at a distance of 1m is the main influencing factor of the model. Based on this, the present invention converts random noise into Omitted to make the parameters more prominent and Therefore, the mathematical formula of the log-normal path loss model can be simplified to: According to the above formula, in the parameter and If the RSSI value is known, it can be converted into the distance between the signal transmitter and the signal receiver, that is, Due to the path loss factor It is closely related to the positioning environment and the path loss factor cannot be simply determined based on experience. Therefore, the present invention introduces a gradient descent iterative method and combines the measured data to determine the parameters and , ensuring that the RSSI value can be more accurately converted into distance during positioning, thereby improving positioning accuracy.

[0040] For details, please refer to Figure 2 and Figure 3 ,in, Figure 2 This is a schematic diagram of the indoor data measurement scene. Figure 3 It is a schematic diagram of the outdoor data measurement scenario.

[0041] In the experiment, four Bluetooth signal transceivers were used as signal transmitters and a single Bluetooth signal transceiver was used as the signal receiver. The parameter configurations of all signal transmitters were kept consistent (specifically, the transmit power of all signal transmitters was set to 0dBm and the signal transmission interval was 30ms). In order to record the changes of RSSI values ​​with distance, four groups of actual measurements were carried out. Each group of actual measurements counted the RSSI data sent by different signal transmitters received by the signal receiver at different distances (0.5m, 1m, 1.5m, 2m, 2.5m, 3m, 3.5m, 4m, 4.5m, 5m, 5.5m, and 6m).

[0042] 30 RSSI data samples are collected at each distance and can be filtered into a RSSI representative value through Gaussian combined mean filtering (see below). Figure 4 , outdoor measured RSSI data can be found at Figure 5 (exist Figure 4 and Figure 5 In the figure, Distance represents distance).

[0043] In this embodiment, it can be understood that although the parameter settings of the signal transmitting end are the same, due to the inevitable slight differences between the devices, even at the same distance, the RSSI values ​​corresponding to each signal transmitting end will still show certain differences.

[0044] If a path loss model is established for each set of data, the universality of the model will be greatly reduced. Therefore, the present invention fits the data of all sets to an overall model and uses the gradient descent method to determine the parameters in the model. and to construct a unified path loss model.

[0045] Specifically, Assume the distance is When , the RSSI value between the signal transmitter and the signal receiver is RSSI. According to the log-normal loss model formula, the loss function is for: That is, the sum of squares of the errors between all predicted values ​​of the model and the true values, multiplied by is for the convenience of derivation, is the number of senders.

[0046] From the above formula, the gradient of the loss function is: Since the direction of the gradient vector in a multivariate function points to the direction in which the function value increases fastest, the negative gradient direction is the direction in which the function value decreases fastest.

[0047] Therefore, the gradient descent iteration formula can be obtained as: in, It is the learning rate in gradient descent, which represents the step size or rate at which the function value decreases in each iteration.

[0048] By continuously executing the gradient descent iteration process, we can gradually approach and find the optimal parameter value that minimizes the objective function. The indoor path loss optimization model fitting curve can be found in Figure 6 The outdoor path loss optimization model fitting curve can be found in Figure 7 (exist Figure 6 and Figure 7 In the figure, Distance represents the distance and Fitted RSSI represents the RSSI after fitting).

[0049] The parameters of the indoor path loss optimization model and the outdoor path loss optimization model are obtained by the gradient descent iterative method. They are -48.48 and -47.24 respectively, and the parameters They are 1.88 and 2.56 respectively. That is, the mathematical expressions of the indoor path loss optimization model and the outdoor path loss optimization model are: Depend on Figure 6 and Figure 7 It can be seen that the path loss model fitted by the gradient descent iterative method can better simulate the relationship between the signal transmission distance and the received RSSI value.

[0050] For RSSI ranging, since RSSI is very sensitive to the environment, it is often interfered and affected by various noises, resulting in a decrease in signal quality. In addition, as the signal propagation distance increases, the fluctuation range of RSSI also increases significantly. Therefore, it is necessary to filter and optimize multiple measured RSSI values ​​to obtain a relatively stable signal strength value.

[0051] In the current related technologies, common signal filtering algorithms include mean filtering, median filtering and Gaussian filtering. (Since the related algorithms of mean filtering, median filtering and Gaussian filtering are widely used in this field, they will not be described here.) The present invention provides a new solution, that is, using a Gaussian filter combined with a mean filter method to perform filtering preprocessing on RSSI data.

[0052] Specifically, the obtained RSSI data are first filtered using a Gaussian filtering method and a mean filtering method, respectively; and then the average value of the obtained filtered data is taken as the final filtering result.

[0053] Compared with the common signal filtering algorithms in the existing related technologies, this filtering method has the characteristics of minimum signal fluctuation and highest stability. It can have a good signal fluctuation suppression effect, can effectively reduce the random fluctuation of the signal, and improve the possibility of the signal.

[0054] For example, in an exemplary implementation, the signal transmitter and the signal receiver may be placed on the same horizontal line with a constant distance therebetween, 300 RSSI data samples may be collected continuously, and different filtering algorithms may be used to filter every 10 samples, and the filtering results may be compared and analyzed.

[0055] Among them, different filtering algorithms include mean filtering, median filtering, Gaussian filtering, mean combined with median filtering, Gaussian combined with mean filtering, Gaussian combined with median filtering, and Gaussian combined with median + mean filtering.

[0056] in, Figure 8 The optimized signal effect of mean filtering is shown. Fig. 9 The optimized signal effect of median filtering is shown. Fig.10 The optimization signal effect of Gaussian filtering is shown. Fig.11 The optimized signal effect of mean combined with median filtering is shown. Fig.12 The optimized signal effect of Gaussian combined with mean filtering is shown. Fig.13 The optimized signal effect of Gaussian combined with median filtering is shown. Fig.14 It shows the optimized signal effect of Gaussian combined with mean + median filtering.

[0057] The comparison of the results of each filtering algorithm is shown in Table 1 below.

[0058] Table 1 Comparison of filtering methods (unit: dBm) According to the analysis of the data in the above table, it can be observed that the mean values ​​of the signals processed by different filtering algorithms are roughly similar, all maintained at around -65dBm. However, it is obvious that the variance, standard deviation and range of the Gaussian combined with mean filtering method are the smallest compared with other filtering methods. In other words, the signal data processed by the Gaussian combined with mean filtering method has the smallest fluctuation and the highest stability. At the same time, the fluctuation range of the Gaussian combined with mean filtering method is relatively small, indicating that it is effective in suppressing signal fluctuations, and can more effectively reduce the random fluctuations of the signal and improve the reliability of the signal.

[0059] As can be seen from the above, the path loss model fitted by the gradient descent method can better simulate the relationship between the signal transmission distance and the received RSSI value. However, it is also necessary to quantitatively evaluate the fitting effect of the evaluation model in order to determine the best model.

[0060] In view of this, in one embodiment of the present invention, the RSSI-based ranging method of the present invention may further include: Step S4-1: Evaluate the model fitting effect according to the following formula: In the formula, is the coefficient of determination, is the regression sum of squares, is the total sum of squared deviations, For the The actual distance between the signal sending end and the signal receiving end, is the average value of the true distance, For the The model-predicted distance between a signal transmitter and a signal receiver; Step S4-2: If If the value of is greater than the preset threshold, the fitting effect is considered qualified.

[0061] Specifically, taking the above indoor path loss optimization model and outdoor path loss optimization model as examples, the calculated The values ​​are shown in the following table.

[0062] Table 2 Model fitting coefficient Generally, The value is between 0 and 1. The closer it is to 1, the better the model fitting effect. As shown in Table 2 above, the R-squared coefficients of the fitted indoor path loss optimization model and the outdoor path loss optimization model are both close to 1, which indicates that the fitted model can more accurately reflect the relationship between the RSSI value and the signal propagation distance in the experimental environment.

[0063] In one embodiment of the present invention, the preset threshold of the present invention can be set to 0.8.

[0064] Of course, it is understandable that the preset threshold of the present invention can be selected and adjusted according to actual conditions, and is not limited to the value of 0.8, and the present invention does not make any specific limitation on this.

[0065] In order to verify the actual performance of the path loss model of the present invention in RSSI ranging, ranging verification is performed indoors and outdoors. In the experiment, the signal transmitter is placed at positions 0.5m, 1m, 2m, 4m, and 6m away from the signal receiving end, and 30 predicted distances of RSSI ranging are collected and recorded at each position.

[0066] Among them, it should be noted that 0.5m and 1m belong to the short distance range, which can be used to verify the model accuracy at close range; 2m and 4m are medium distances, which are the main working range of the model; 6m is a long distance, which can verify the performance of the model at a long distance.

[0067] In indoor environments, the predicted distance values ​​measured at each distance are shown in Tables 3.1 to 3.5 below.

[0068] Table 3.1 Predicted distance values ​​measured indoors and outdoors at a true distance of 0.5 m (unit: m) Table 3.2 Predicted distance values ​​measured indoors and outdoors at a real distance of 1m (unit: m) Table 3.3 Predicted distance values ​​measured indoors and outdoors at a real distance of 2m (unit: m) Table 3.4 Predicted distance values ​​measured indoors and outdoors at a real distance of 4m (unit: m) Table 3.5 Predicted distance values ​​measured indoors and outdoors at a real distance of 6m (unit: m) Furthermore, the performance of RSSI ranging at different distances is comprehensively evaluated, and the performance parameters are shown in Table 4 below.

[0069] Table 4 RSSI ranging performance evaluation parameters at different distances (unit: m) From Table 4 above, we can see that from the two key indicators of error mean and error variance, First, whether in indoor or outdoor environments, the RSSI-based ranging method of the present invention has shown relatively good and stable performance. The error mean and error variance values ​​in the outdoor environment are lower than those in the indoor environment. This shows that in the outdoor environment, due to the relatively open environment, the interference and influence on signal propagation are smaller, the deviation between the distance predicted by the RSSI-based ranging method of the present invention and the actual distance is smaller, the predicted distance value is more concentrated, and the accuracy is higher.

[0070] Second, in indoor and outdoor environments, the maximum error of the RSSI-based ranging method of the present invention is also at a relatively low level. Therefore, in practical applications, a more realistic path loss model should be constructed according to specific application scenarios. Compared with the model that simply relies on empirical value parameters, this method can often bring better ranging effects.

[0071] According to the second aspect of the present invention, there is also provided a ranging system based on RSSI, which is applied to the ranging method based on RSSI of any technical solution in the first aspect of the present invention, and the ranging system based on RSSI comprises: A data acquisition module is used to obtain the RSSI value from the signal sending end to the signal receiving end; Data processing module, used to establish the path loss optimization model and determine the parameters and .

[0072] Through the above technical scheme, the RSSI-based ranging system of the present invention can not only effectively avoid the problem of large gap errors, but also effectively improve the ranging efficiency. At the same time, the RSSI-based ranging system of the present invention also has good versatility and can be widely used in various wireless positioning scenarios.

[0073] According to a third aspect of the present invention, a vehicle is further provided, comprising the RSSI-based ranging system in the second aspect of the present invention, wherein a data acquisition module and a data processing module are arranged on the vehicle.

[0074] In this way, after the vehicle of the present invention is equipped with the RSSI-based ranging system, it can achieve fast and accurate ranging in a variety of wireless positioning scenarios.

[0075] In one embodiment of the present invention, the vehicle of the present invention further includes a smart key, and the smart key is provided with a signal transmitting module for sending a signal to a corresponding signal receiving module on the vehicle.

[0076] In this way, when the vehicle of the present invention is equipped with an RSSI-based ranging system and a signal transmission module is installed on the smart key, fast and accurate ranging between the vehicle and the smart key can be achieved in a variety of application environments, thereby facilitating the realization of other functions, such as keyless entry or keyless start.

[0077] It is understandable that, first, the vehicle of the present invention should be understood in a broad sense, which refers to all land mobile devices used to transport personnel, goods or for other special purposes, generally driven by a power device, and having wheels or tracks to be able to travel on roads or specific sites. Specifically, the vehicle of the present invention includes but is not limited to: passenger cars (sedans, sports cars, SUVs, MPVs, etc.), commercial vehicles (trucks, buses, special vehicles, such as light trucks, buses, fire trucks, garbage trucks, etc.), motorcycles, special vehicles (agricultural machinery), rail transit vehicles, modified vehicles, etc. The present invention does not make specific limitations on this.

[0078] Second, the smart key of the present invention should also be understood in a broad sense, which refers to a key integrated with advanced technology, which can be physical (for example, a physical key with physical buttons and / or a touch screen) or virtual (for example, a virtual key that uses a mobile phone APP to realize the key function, or a virtual key based on cloud services and can be managed and shared through the Internet). The present invention does not make specific limitations on this.

[0079] According to the fourth aspect of the present invention, a vehicle approach sensing system is also provided, comprising a data sending module, a data receiving module, a data acquisition module and a data processing module. The data sending module is used to be set in a mobile unlocking terminal matched with the vehicle; the data receiving module is used to be set in the vehicle and used to receive the signal sent by the data sending module; the data acquisition module is used to be set in the vehicle and used to obtain the RSSI value of the signal received by the data receiving module; the data processing module is used to be set in the vehicle and used to execute the RSSI-based ranging method as described in any technical solution in the first aspect of the present invention.

[0080] In this way, the vehicle approach sensing system of the present invention can realize fast and accurate distance measurement between the vehicle and the mobile unlocking terminal in a variety of application environments, thereby facilitating the realization of other functions, such as keyless entry or keyless start.

[0081] It is understandable that, in this embodiment, the mobile unlocking terminal may be a smart key, or a mobile terminal (eg, a mobile phone) having a vehicle unlocking function, which is not specifically limited in the present invention.

[0082] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A ranging method based on RSSI, characterized in that: The steps include: Step S1: Establish a path loss optimization model using the following formula: In the formula, is the distance between the signal sending end and the signal receiving end, is the RSSI reference value at a distance of one meter. RSSI is the received signal strength indicator. is the path loss factor; Step S2: Determine the parameters of the path loss optimization model using the gradient descent iterative formula and : In the formula, represents the parameters of the next generation, represents the parameters of the previous generation, is the learning rate, is the number of signal senders, For the The distance between the signal sending end and the signal receiving end, For the The RSSI value between the signal sending end and the signal receiving end; Step S3: The parameters obtained in step S2 and Substitute into the path loss optimization model established in step S1 to perform ranging operation.

2. The RSSI-based ranging method according to claim 1, characterized in that: The RSSI-based ranging method also includes: Step S0: Use Gaussian filtering combined with mean filtering to pre-process RSSI data.

3. The RSSI-based ranging method according to claim 2, characterized in that: The step S0 specifically includes: Step S0-1: using Gaussian filtering method and mean filtering method to filter the acquired RSSI data respectively; Step S0-2: taking the average value of the filtered data obtained in step S0-1 as the final filtering result.

4. The RSSI-based ranging method according to claim 1, characterized in that: The RSSI-based ranging method also includes: Step S4-1: Evaluate the model fitting effect according to the following formula: In the formula, is the coefficient of determination, is the regression sum of squares, is the total sum of squared deviations, For the The actual distance between the signal sending end and the signal receiving end, is the average value of the true distance, For the The model-predicted distance between a signal transmitter and a signal receiver; Step S4-2: If If the value of is greater than the preset threshold, the fitting effect is considered qualified.

5. The RSSI-based ranging method according to claim 4, characterized in that: The preset threshold is set to 0.

8.

6. A ranging system based on RSSI, characterized in that: Applied to the RSSI-based ranging method according to any one of claims 1 to 5, the RSSI-based ranging system comprises: A data acquisition module is used to obtain the RSSI value from the signal sending end to the signal receiving end; Data processing module, used to establish the path loss optimization model and determine the parameters and .

7. A vehicle, characterized in that: It comprises the RSSI-based ranging system as claimed in claim 6, wherein the data acquisition module and the data processing module are arranged on the vehicle.

8. The vehicle according to claim 7, characterized in that The vehicle further comprises a smart key, on which a signal transmitting module is arranged for sending a signal to a corresponding signal receiving module on the vehicle.

9. A vehicle approaching sensing system, characterized in that: include: A data sending module, used to be set in a mobile unlocking terminal matched with the vehicle; A data receiving module, used to be arranged on the vehicle and used to receive the signal sent by the data sending module; A data acquisition module, used to be arranged on the vehicle and used to acquire the RSSI value of the signal received by the data receiving module; A data processing module is configured to be arranged on a vehicle and to execute the RSSI-based ranging method as claimed in any one of claims 1 to 5.

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