DE102018130876A1 - Method and system for locating a mobile identification transmitter of a vehicle - Google Patents

Abstract

The present disclosure relates to a method for locating a mobile identification transmitter of a vehicle. The method comprises determining a transit time of at least one localization signal from a plurality of localization signals, determining a signal strength of at least one localization signal from the plurality of localization signals, and locating the mobile identification transmitter with respect to the vehicle using the determined transit time and the determined signal strength. Locating the mobile identification transmitter comprises classifying a position of the mobile identification transmitter into at least one exterior area and at least one interior area of the vehicle by means of machine learning.

Description

The present disclosure relates to a method and a system for locating a mobile identification transmitter of a vehicle. The present disclosure particularly relates to tamper-proof keyless entry systems and keyless go systems for motor vehicles.

State of the art

Mobile identification transmitters (ID transmitters) can be used to contactlessly unlock vehicles and thus give the user of the identification transmitter access to the vehicle. In general, radio systems are used in which authentication data is transmitted wirelessly. In the event of positive authentication by the vehicle, the doors and / or the trunk are automatically unlocked. An engine start can also be authorized or enabled.

Such keyless systems include, for example, active keyless entry systems, passive keyless entry systems and keyless go systems. In active keyless entry systems, authentication is carried out by a user at the push of a button on the identification transmitter. Passive keyless entry systems do not require the user to actively operate the identification transmitter. Instead, the initiation takes place via an approach of the user or an actuation of a door handle. In the case of keyless go systems, the engine can be started, for example, by pressing a button on the dashboard of the vehicle without having to operate or actively use the identification transmitter.

Conventional keyless systems can be susceptible to manipulation. A relay can be used to suggest to the vehicle, for example, that the mobile identification transmitter is in a position other than its actual position (so-called "relay station attacks"). This means that the vehicle can be unlocked, started and unlawfully stolen by the owner.

Disclosure of the invention

It is an object of the present disclosure to provide a method and a system for locating a mobile identification transmitter of a vehicle, which enable more secure and / or reliable keyless functions. In particular, it is an object of the present disclosure to make keyless functions of vehicles tamper-proof.

This object is solved by the subject matter of the independent claims. Advantageous refinements are specified in the subclaims.

According to an independent aspect of the present disclosure, a method for locating a mobile identification transmitter of a vehicle, and in particular of a motor vehicle, is specified. The method comprises determining a transit time of at least one localization signal from a plurality of localization signals, determining a signal strength of at least one localization signal from the plurality of localization signals, and locating the mobile identification transmitter with respect to the vehicle using the determined transit time and the determined signal strength. Locating the mobile identification transmitter comprises classifying a position of the mobile identification transmitter into one or more exterior areas and one or more interior areas of the vehicle by means of machine learning.

According to the invention, a combination of the transit time of the signals and the signal strength is used to locate the identification transmitter reliably and tamper-proof. In addition, the localization is carried out using a machine learning method ("Machine Learning", ML) with which it can be reliably determined whether the identification transmitter is located in the exterior or interior of the vehicle. The keyless function is therefore tamper-proof and relay station attacks can be prevented.

The vehicle preferably sends out the large number of localization signals for locating the mobile identification transmitter, for example by means of a communication device. The location signals can also be used for information transmission, such as for the transmission of authorization information. The mobile identification transmitter can receive one or more of the location signals and can send location signals to the vehicle. For this purpose, the mobile identification transmitter can comprise suitable electronic means, such as antennas and / or coils for receiving and radiating the localization signals. The localization signals can be received on the vehicle or on the identification transmitter and the signal strength and the signal transit time can be determined therefrom.

The transit time and the signal strength can be determined from the same at least one localization signal. Alternatively, the transit time and the signal strength can be determined from different localization signals, for example from successive ones in time Localization signals. For example, the transit time can be determined from at least one first localization signal and the signal strength from at least one second localization signal.

The signal strength is preferably determined without constructive and / or destructive signal superimpositions. For example, the localization signals can be sent out as short UWB pulses and (together with all) reflection can be received. Because of the short impulses, there is little or no constructive or destructive interference between the different reception paths. This means that there are no overlaps that could lead to signal changes that are dependent on the phase position. In other words, the same signal properties can always be obtained, which can increase the reliability of the localization.

An artificial neural network is preferably used as the method of machine learning. For example, a multilayer perceptron (“multilayer perceptron”) can be used as the neural network.

The transit time of the at least one localization signal is typically used for determining the distance between the mobile identification transmitter and the vehicle or one or more anchor points of the vehicle. For locating the mobile identification transmitter, the distance values are preferably limited to a maximum value and larger values are replaced by a maximum value. The maximum value can be 4m, for example.

Additionally or alternatively, the signal strengths are limited to a minimum value and smaller values are replaced by a minimum value for locating the mobile identification transmitter. The minimum value can be, for example, -110dBm. This so-called "capping" can further increase the reliability of the localization.

If the position of the mobile identification transmitter is classified in the outside area, the method preferably includes determining a position of the mobile identification transmitter in the outside area using a localization algorithm. The localization algorithm can be a Bayesian filter or a Kalman filter. If the mobile identification transmitter is located outside the vehicle, the localization algorithm can, for example, determine an exact position of the mobile identification transmitter in the exterior in relation to the vehicle. The localization algorithm is typically downstream of the machine learning algorithm of the classification.

The vehicle preferably comprises a plurality of anchors or anchor points which are spaced apart from one another. At least one sensor for receiving the signals can be arranged at each anchor point of the plurality of anchor points. The transit times and signal strengths can be determined based on the signals received at the anchor points. The anchors can include exterior anchors and / or interior anchors. The exterior anchors can for example be arranged at the front of the vehicle and / or at the rear. The interior anchors can be arranged on the interior of the vehicle. The provision of a plurality of anchor points at different points in the vehicle permits a more precise determination of the position of the mobile identification transmitter with respect to the vehicle.

The transit time of the at least one localization signal is preferably used for a distance determination or distance measurement between the mobile identification transmitter and the vehicle, and in particular the anchor points. In some embodiments, at least a first measured distance value that is greater than a sum of a second measured distance value, a distance between two anchor points and a threshold value is rejected. This means that implausibly large distance values can be rejected. In other words, a plausibility check of the ToF measurements can be carried out.

For example, at least one first distance value can be measured at a first anchor point and the second distance value can be measured at a second anchor point. The distance can be an anchor distance between the first anchor point and the second anchor point. The threshold value can be a fixed value that is added to the distance between the anchor points and the second distance value. The threshold value can be selected appropriately to ensure that implausible distance values are rejected and plausible distance values are not rejected.

Preferably two real distance measurement values and one synthetic value are used for an initial position determination of the mobile identification transmitter in the outside area. In particular, three measurement values are generally necessary for a clear initial position determination. According to the present disclosure, however, only two measured values can be used, the third value being replaced by a fictitious measured value, for example for an interior anchor of the vehicle. The fictitious measured value can be a comparatively large value, for example at least 3 m or at least 4 m. This is particularly advantageous if the user or the mobile identification transmitter approaches the vehicle from a great distance.

Preferably, only two degrees of freedom x and y are used or left open for locating the mobile identification transmitter in the outside by means of the transit time (ie for measuring the distance), which span a plane essentially parallel to a standing area of the vehicle. The third degree of freedom z, which is perpendicular to the base of the vehicle, can be fixed (eg 1m). By limiting the degrees of freedom of the localization algorithm to only two dimensions, which for example define a level at the waist of the vehicle, the localization accuracy can be increased, in particular since the user typically holds the mobile identification transmitter at this height.

According to a further independent aspect of the present disclosure, a storage medium is specified, comprising a software program which is set up to be executed on at least one processor and thereby to carry out the method described in this document for locating a mobile identification transmitter of a vehicle.

According to a further independent aspect of the present disclosure, a system is specified, comprising at least one processor which is set up to carry out the method described in this document for locating a mobile identification transmitter of a vehicle.

The system is preferably a keyless system of the vehicle, such as an active keyless entry system and / or a passive keyless entry system and / or a keyless go system.

According to a further independent aspect of the present disclosure, a vehicle is specified, comprising the keyless system. The term vehicle includes cars, trucks, buses, campers, motorcycles, etc., which are used to transport people, goods, etc. In particular, the term includes motor vehicles for the transportation of people.

Figure list

• 1 ein Fahrzeug und einen mobilen Identifikationsgeber gemäß Ausführungsformen der vorliegenden Offenbarung,
• 2 ein Flussdiagram eines Verfahrens zum Lokalisieren eines mobilen Identifikationsgebers eines Fahrzeuges gemäß Ausführungsformen der vorliegenden Offenbarung,
• 3A und B eine Verarbeitung der Lokalisierungssignale mittels maschinellen Lernens,
• 4 ein System zum Lokalisieren eines mobilen Identifikationsgebers eines Fahrzeuges gemäß Ausführungsformen der vorliegenden Offenbarung,
• 5 eine Plausibilisierung von Abstandswerten, die aus den Laufzeiten der Lokalisierungssignale erhalten werden,
• 6 eine Bestimmung eines initialen Positionswertes gemäß Ausführungsformen der vorliegenden Offenbarung, und
• 7 schematisch eine beispielhafte Funktionsweise eines Sicherheitsalgorithmus gemäß Ausführungsformen der vorliegenden Offenbarung.

Exemplary embodiments of the disclosure are shown in the figures and are described in more detail below. Show it:

• 1 a vehicle and a mobile identification transmitter according to embodiments of the present disclosure,
• 2nd 2 shows a flowchart of a method for locating a mobile identification transmitter of a vehicle according to embodiments of the present disclosure,
• 3A and B processing of the localization signals by means of machine learning,
• 4th a system for locating a mobile identification transmitter of a vehicle according to embodiments of the present disclosure,
• 5 a plausibility check of distance values obtained from the transit times of the localization signals,
• 6 determining an initial position value according to embodiments of the present disclosure, and
• 7 schematically an exemplary operation of a security algorithm according to embodiments of the present disclosure.

Embodiments of the disclosure

Unless otherwise noted, the same reference numerals are used below for the same and equivalent elements.

1 shows a vehicle 100 and a mobile identification transmitter 10th according to embodiments of the present disclosure.

The mobile identification transmitter 10th communicates with a keyless system 110 of the vehicle 100 over a communication link 20th . The keyless system 110 is set up to a position of the mobile identification transmitter 10th to determine and control keyless functionalities based on the determined position. The keyless system 110 includes, for example, an active keyless entry system and / or a passive keyless entry system and / or a keyless go system.

The keyless system 110 can include a locking system of the vehicle, and in particular a central locking system. In addition or alternatively, the keyless system can 110 be set up to start the engine, for example, by actuating an operating element in the interior of the vehicle 100 to enable. If the keyless system 110 based on the specific position of the mobile identification transmitter 10th the vehicle can determine a positive authentication or authorization 100 can be unlocked by actuating the central locking. In addition, for example, an engine start button on the dashboard can be activated so that the driver can start the engine by pressing the engine start button.

The vehicle 100 can be a communication device 120 include, which is set up to the communication link 20th with the mobile identification transmitter 10th to provide. For communication with the mobile identification transmitter 10th can receive signals from the communication device 120 received and / or sent. Similarly, signals from the mobile identification transmitter 10th received and / or sent. The signals can be used to locate the identification transmitter 10th in relation to the vehicle 100 be used. The signals can therefore also be referred to as "localization signals". The signals can also be used for the transmission of information, for example for the transmission of identification information and / or authorization information from the mobile identification transmitter 10th to the vehicle 100 .

The communication link 20th is an ultra-wideband (UWB) communication link in accordance with some embodiments of the present disclosure. Accordingly, the location signals can be ultra wideband signals. For example, the UWB communication link can use a frequency range with a bandwidth of 500 MHz or more.

The term “mobile identification transmitter” as used in the present disclosure is to be understood as the device which is generally referred to as “radio key”. In other words, the mobile identification transmitter is not a device integrated in the vehicle, but the key that the driver generally carries with him.

2nd shows a flow diagram of a method 200 for locating a mobile identification transmitter of a vehicle according to embodiments of the present disclosure.

The procedure 200 includes in the block 210 determining a transit time of at least one first localization signal from a plurality of localization signals, in the block 220 determining a signal strength of the at least one first localization signal or another (second) localization signal from the plurality of localization signals, and in the block 230 locating the mobile identifier with respect to the vehicle using the determined transit time and the determined signal strength. Locating the mobile identification transmitter includes, in particular, classifying a position of the mobile identification transmitter into an exterior (area) and an interior (area) of the vehicle by means of machine learning.

The procedure 200 uses a combination of the time of flight (ToF) and the signal strength to locate the identification transmitter reliably and tamper-proof. In addition, the localization takes place using machine learning (“machine learning”, ML), with which it can be reliably determined whether the identification transmitter is located in the exterior or interior of the vehicle. In particular, machine learning can reduce and even prevent negative effects from external interference. The keyless function is therefore tamper-proof and relay station attacks can be prevented.

In some embodiments, the location signals are broadband signals, and in particular ultra wideband signals. In the case of relay station attacks, signal amplification can suggest to the vehicle that the mobile identification transmitter (key) is very close to the vehicle, even though it is actually very far away. Using the high frequency ToF based localization technique can solve this problem. The combination of time stamps and signal strengths can also prevent the localization process from being inaccurate and prone to interference due to the use of high frequencies.

The keyless system of the vehicle can transmit the large number of localization signals for locating the mobile identification transmitter, for example by means of the communication device. The mobile identification transmitter can receive one or more of the location signals and send location signals back to the vehicle. For this purpose, the mobile identification transmitter can comprise suitable electronic means, such as coils or antennas for receiving and emitting the localization signals. The localization signals sent back by the mobile identification transmitter can be received on the vehicle and the signal strength and the signal propagation time can be determined therefrom.

In the present disclosure, the transit time of the at least one localization signal can relate to a time period between the sending of the at least one localization signal from the vehicle and the reception of a corresponding localization signal at the vehicle. The time difference between the receipt of the localization signal at the identification transmitter and the sending of a corresponding localization signal can be transmitted to the vehicle, for example. Alternatively, the transit time can be determined on the mobile identification transmitter, for example by means of a time stamp, and the corresponding information can be transmitted to the vehicle.

Similarly, the signal strength of the at least one localization signal in the present disclosure can relate to a signal strength of the corresponding localization signal received on the vehicle. Alternatively, the signal strength can be measured on the mobile identification transmitter and the corresponding information can be transmitted to the vehicle.

In some embodiments, only two degrees of freedom x and y are used or left open for locating the mobile identification transmitter, and in particular for a distance measurement between the mobile identification transmitter and the vehicle, which span a plane parallel to a standing area of the vehicle . The third degree of freedom z, which is perpendicular to the standing surface of the vehicle, can be fixed.

In some embodiments, the vehicle may include at least one sensor for receiving the signals. In particular, the vehicle can have one or more anchor points (also referred to as “anchors”), at least one sensor being arranged at each anchor point. The anchor points can be spaced apart. In particular, the anchor points can include exterior anchors and interior anchors. The exterior anchors can for example be arranged at the front of the vehicle and / or at the rear. The interior anchors can be arranged on (or on top of) the interior of the vehicle. The provision of a plurality of anchor points at different points in the vehicle permits a more precise determination of the position of the mobile identification transmitter with respect to the vehicle.

3A and B show machine learning processing of the localization signals to classify the position of the mobile identifier into "inside" and "outside" the vehicle according to embodiments of the present disclosure.

In the present disclosure, machine learning or an ML algorithm is used in order to classify the position of the mobile identification transmitter into an exterior area and an interior area of the vehicle by means of the transit time and the signal strength. For example, machine learning can use an artificial neural network, such as a multilayer perceptron (“multilayer perceptron”).

In an exemplary embodiment, the ML algorithm can also be used 12 Input values are implemented. The following table 1 shows the distance measurement using ToF ("Range") and signal strength measurement ("RXP") 12 Input values shown: Table 1 1 Range TRX 1 2nd Range TRX 2 3rd Range TRX 3 4th Range TRX 4 5 Range TRX 5 6 Range TRX 6 7 RXP TRX 1 8th RXP TRX 2 9 RXP TRX 3 10th RXP TRX 4 11 RXP TRX 5 12 RXP TRX 6

However, the present disclosure is not limited to this and the ML algorithm can be implemented with a different number of input values. In particular, the ML algorithm can use less or more than 12 Input values are implemented.

In some embodiments of the present disclosure, the signal strength is determined or measured using a method that neglects constructive and / or destructive superimpositions of the various reception paths. This can be done, for example, by summing the signal energies of the reconstructed undisturbed signal pulses of the individual reception paths. Referring to Table 1, the signal strength values can RXP can be determined via a summation of signal energies of the individual received localization signals.

Damit ist f ausschließlich von der Signalleistung P_i abhängig.In particular, the reception time and the signal power of each individual received reflection can be determined with a suitable method. The summation of the individual powers is carried out using a method that is not influenced by the relative phase shift between the individual reflections: $$P_{Ges}=G\left(f\left(P_1\right)+f\left(P_{\mathrm{2nd}}\right)+...+f\left(P_n\right)\right)$$

So f is exclusively from the signal power P _i dependent.

For example, the localization signals can be sent out as short UWB pulses and received together with all reflections. Due to the short pulses, no constructive or destructive interference of the localization signals is possible. This means that there are no overlaps that could lead to signal changes. In other words, regardless of the signal propagation time of the reflection paths, the same signal levels can always be obtained, which can increase the reliability of the localization.

3A shows a processing process 310 for the 12 Input values from table 1. The Raw values are in the block 311 entered in which there is a cap for the runtime and / or the signal strength.

In particular, the transit time of the at least one localization signal is used for determining the distance between the mobile identification transmitter and the vehicle, wherein for locating the mobile identification transmitter (e.g. all), distance values that are less than or equal to a first maximum value are limited to a second maximum value. The first maximum value and the second maximum value can be the same. The first and / or second maximum value can be 4m, for example.

In addition or as an alternative, all signal strengths that are less than or equal to a first minimum value are replaced by a second minimum value for locating the mobile identification transmitter. The first minimum value and the second minimum value can be the same or different. The first and / or second minimum value can be, for example, -110dBm. With such a “capping”, the sensor input values are manipulated by limiting the distance measured values or the field strength values to a maximum or minimum value.

In the block 312 NaN handling takes place. Input values that indicate NaN ("Not a Number") can be replaced here, for example with the second maximum or minimum value.

In the block 313 the RXP values are linearized, but not for the range values. In particular, the RXP values can be converted from dBm to mW (milli-watts).

In the block 314 there is a standardization. Here the input values are scaled so that they have the same standard deviations.

3B shows an exemplary neural network 320 , and in particular a multi-layer perceptron that can be used as an ML algorithm. The neural network is with 12 Neurons per layer ("hidden layer") shown. However, the present disclosure is not limited to this and the neural network may have fewer or more neurons per layer.

4th shows a system 400 for locating a mobile identification transmitter of a vehicle according to embodiments of the present disclosure.

In the example of the 4th is in the block 410 a combination of three algorithms. block 414 denotes the ML algorithm that classifies the position of the mobile identification transmitter into the interior and exterior of the vehicle. In other words, the block 414 decided whether the mobile identification transmitter is inside or outside the vehicle.

In the block 412 shows a localization algorithm for the outside. If the ML algorithm has decided that the mobile identification transmitter is located outside the vehicle, the localization algorithm can determine an exact position of the mobile identification transmitter in the exterior. The localization algorithm is typically located downstream of the exterior classification or the ML algorithm. The localization algorithm can be, for example, a Bayesian filter or a Kalman filter.

In the block 420 the mobile identification transmitter can be assigned to a zone of the outside space. For example, the exterior of the vehicle can be divided into a plurality of zones, the mobile identification transmitter being assigned to a specific zone by means of the localization algorithm. As a result, the mobile identification transmitter can be precisely located outside.

In the block 416 a security algorithm is shown. The security algorithm can be compared to the ML algorithm in the block 414 and the localization algorithm in the block 412 an inaccurate but tamper-proof algorithm. The security algorithm can, for example, plausibility check whether the position determination of the ML algorithm in the block 414 and the location determination of the location algorithm in the block 412 were manipulated (blocks 430 and 432 ).

The results of the plausibility check blocks 430 and 432 be in the block 440 combined and in block 450 output as the end result of the position of the mobile identification transmitter relative to the vehicle. If the result is positive, at least one keyless function, such as a keyless entry function and / or keyless go function, can be enabled.

In the following 5 to 7 are aspects of the in the 2nd shown method and in the 4th shown system explained in detail. In particular, aspects of the distance measurement by means of the ToF measurements are explained.

5 shows a plausibility check of distance values obtained from the transit times of the localization signals.

In 5 three anchors or anchor points on the vehicle are shown as examples. The anchors comprise a first anchor A , which is arranged at a front roof position of the vehicle, a second anchor B , which is located on the right of the stern, and a third anchor C. , which is located on the left at the stern. The first anchor A is an interior anchor and the second anchor B as well as the third anchor C. are outdoor anchors. Each anchor has one or more sensors that are set up to receive the location signals.

Based on the one at the first anchor A received transit times of the localization signals, a first distance a is determined ("ranging"). The signals are not received in a directionally resolved manner, as a result of which the first distance a forms a circle around the first anchor A represents around. Similarly, based on the transit times of the localization signals received at the second and third anchors, a second distance b and a third distance become c determined ("Ranging").

For the plausibility check of the individual distance measurements, a plausibility check formula can be used that rejects all distance measurements that indicate an implausibly large distance. For example, distance values that are greater than a sum of ( i ) another distance value, ( ii ) a distance between two anchor points and ( iii ) are a threshold value, are discarded. The distance can be a distance between two anchor points, one of which is subjected to the plausibility check and the other of which has measured the distance value to which the anchor distance is added.

In the example of the 5 is the distance between the first anchor point A and the second anchor point B marked with d. If a measured (first) distance value is greater than the sum of the anchor distance d, the second distance b and a threshold value, this distance value can be rejected as implausible. In the example of the 5 this applies to the first distance a, which is greater than the sum of the distance d, the second distance b and a threshold value (not shown).

The threshold value can be a fixed value which is added to the distance between the anchor points and the other measured distance value. The threshold value can be selected appropriately to ensure that implausible distance values are rejected and plausible distance values are not rejected.

6 10 shows a determination of an initial position value according to embodiments of the present disclosure.

In some embodiments, two distance measurement values b ', c 'and a synthetic value a' is used. In particular, three measurement values are generally required for an initial position determination. According to the present disclosure, however, only two real measured values can be used, the third value being able to be replaced by a fictitious measured value, for example for an interior anchor. The fictitious measured value can be a comparatively large value. This is particularly advantageous if the user or the mobile identification transmitter approaches the vehicle from a great distance.

For example, a first position is calculated based on at least two ToF distance measurements, at least one of which comes from an outside anchor. In order to eliminate uncertainty in the position determination, a synthetic distance measurement, i.e. an artificially generated value. This enables the algorithm to select a solution for a position further away from the vehicle.

For example, the synthetic value can be calculated as follows. In a first case, the two real distance measurements are carried out by a first indoor anchor and an outdoor anchor. The shorter of the two measured values is identified and the third value is calculated for a second interior anchor as the sum of ( i ) the shorter measured value, ( ii ) the distance between the position of the anchor with the shorter measurement and the second interior anchor and ( iii ) a fixed value. The fixed value can be about 0.5m, for example. The first interior anchor and the second interior anchor are different anchors and are spaced from one another by the aforementioned distance.

In a second case, the two real ToF distance measurements are carried out by two outside anchors. The shorter of the two measurements is identified. If it is a front anchor, the front interior anchor is selected. If it is a rear anchor, the rear interior anchor is selected. The third value is calculated for the selected interior anchor as the sum of ( i ) the shorter measured value, ( ii ) the distance between the position of the anchor with the shorter measurement and the selected interior anchor and ( iii ) a fixed value. The fixed value can be about 0.5m, for example.

The initial position of the identification transmitter in the exterior can be calculated, for example, at a certain height, such as z = 1m (only x and y as free parameters), using the two real distance measurement values and the synthetic value.

7 shows schematically an exemplary operation of the with reference to the 4th explained security algorithm.

The vehicle includes four exterior anchors, namely two front anchors and two rear anchors, as well as two interior anchors. The anchors are in the appropriate places in the 7 represented as rectangles.

The localization algorithm, such as the Bayesian filter or Kalman filter, can be a high-precision localization algorithm. However, the localization algorithm can have a low level of manipulation security. To avoid security gaps, the security algorithm for securely locating the mobile identification transmitter can be a less precise algorithm that is highly secure for this. The combination of these two algorithms enables high-precision and tamper-proof location of the mobile identification transmitter.

• • Innenraum/Außenraum-Prüfung für die Innenraum-Anker: Thi1, Thi2 (Kreise 701 in der 7)
• • Entsperrprüfung für alle Anker: THo1 ... THo6 (alle Kreise in der 7 mit Ausnahme der Kreise 701)

The security algorithm can, for example, carry out a threshold check using a threshold algorithm. Threshold values can be defined for each anchor:

• • Indoor / outdoor testing for indoor anchors: Thi1 , Thi2 (Circles 701 in the 7 )
• • Unlocking test for all anchors: THo1 ... THo6 (all circles in the 7 except for the circles 701 )

The ToF distance measured values can be used as input values, for example R1 .... R6.

An interior test can be carried out by (R1 <Thi1) or (R2 <Thi2). An unlock test can be carried out by (R1 <Tho1) or (R2 <Tho2) or (R3 <Tho3) or (R4 <Tho4) or (R5 <Tho5) or (R6 <Tho6). If the interior or unlock test delivers a negative result, the plausibility check can be assumed to be unsuccessful.

According to the invention, a combination of the transit time of the signals and the signal strength is used to locate the identification transmitter reliably and tamper-proof. In addition, the localization takes place using machine learning (“machine learning”, ML), with which it can be reliably determined whether the identification transmitter is located in the exterior or interior of the vehicle. The keyless function is therefore tamper-proof and relay station attacks can be prevented.

Claims (15)

A method (200) for locating a mobile identification transmitter (10) of a vehicle (100), comprising: Determining (210) a propagation time of at least one localization signal from a plurality of localization signals; Determining (220) a signal strength of at least one localization signal from the plurality of localization signals; and Locating (230) the mobile identification transmitter (10) with respect to the vehicle (100) using the determined transit time and the determined signal strength, wherein the localization of the mobile identification transmitter comprises: Classifying a position of the mobile identification transmitter (10) in at least one exterior area and at least one interior area of the vehicle (100) by means of a machine learning method. The method (200) according to Claim 1 , the determination (220) of the signal strength not taking into account constructive and / or destructive superimpositions of localization signals. The method (200) according to Claim 1 or 2nd , using an artificial neural network (320) as the method of machine learning. The method (200) according to one of the Claims 1 to 3rd , wherein the transit time of the at least one localization signal is used for a distance determination between the mobile identification transmitter (10) and the vehicle (100), and wherein for the localization of the mobile identification transmitter (10) all distance values that are equal to or greater than a first maximum value , can be replaced by a second maximum value. The method (200) according to one of the Claims 1 to 4th , wherein for locating the mobile identification transmitter (10) all signal strengths that are equal to or less than a first minimum value are replaced by a second minimum value. The method (200) according to one of the Claims 1 to 5 The method (200), if the position of the mobile identification transmitter (10) is classified in the outdoor area, further comprises: determining a position or zone of the mobile identification transmitter (10) in the outdoor area using a localization algorithm (412). The method (200) according to Claim 6 , the localization algorithm (412) being a Bayesian filter or a Kalman filter. The method (200) according to one of the Claims 6 or 7 , wherein the transit time of the at least one localization signal is used for a distance determination between the mobile identification transmitter (10) and the vehicle (100), and wherein first distance values that are greater than a sum of a second distance value, a distance between two anchor points and a threshold value , be discarded. The method (200) according to one of the Claims 6 to 8th , wherein the transit time of the at least one localization signal is used for a distance determination between the mobile identification transmitter (10) and the vehicle (100), and wherein for an initial position determination of the mobile identification transmitter (10) less than three real distance measurement values are used. The method (200) according to one of the Claims 6 to 9 , wherein two degrees of freedom are used for locating the mobile identification transmitter (10), which span a plane parallel to a standing surface of the vehicle (100). The method (200) according to one of the Claims 6 to 10th A further algorithm is used for secure localization of the mobile identification transmitter (10), which locates less precisely, in particular less precisely than the localization algorithm (412). The method (200) according to one of the Claims 1 to 11 , the plurality of localization signals being ultra-wideband signals. Storage medium comprising a software program which is set up to be executed on at least one processor, and thereby to carry out the method (200) according to one of the Claims 1 to 12 to execute. System (400) comprising at least one processor which is used to execute the method (200) according to one of the Claims 1 to 12 is set up. Vehicle (100) comprising the system (400) according to Claim 14 .

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