Detection method for actuation gestures and associated calibration procedure

Abstract

Method for actuation query for a function on a motor vehicle (1) with a sensor arrangement, wherein the sensor arrangement has at least two spaced sensor devices (2, 3) for detecting approaches of a body part, comprising the steps, Monitoring the signals of a first sensor device (2) from the sensor devices (2, 3) until a characteristic first signal response s1 is detected by the first sensor device (2) and detecting an associated time t1, Monitoring the signals of a second sensor device (3) from the sensor devices (2, 3) until a characteristic second signal response s2 is detected by the second sensor device (3) and detecting an associated time t2, Monitoring the signals of the second sensor device (3) until a characteristic third signal response s3 is detected by the second sensor device (3) and detecting an associated time t3, Monitoring the signals of the first sensor device (2) from the sensor devices (2, 3) until a characteristic fourth signal response s4 is detected by the first sensor device (2) and detecting an associated time t4, Determining at least two time differences, formed by subtracting two different values from t1, t2, t3 and t4, Evaluating the time differences and generating an actuation signal depending on the evaluation, Depending on the verification of an ID transmitter, a user profile assigned to the ID transmitter with stored comparison parameters is used to evaluate the time differences.

Description

[0001] The invention relates to a detection method for detecting actuation gestures on a motor vehicle. In particular, the invention relates to a method for detecting actuation gestures performed by a user to access a vehicle function and for calibrating the detection individually for a user.

[0002] Devices for the contactless actuation of motor vehicle functions are known from the prior art. For example, DE 10 2008 063 366 A1 describes a contactless actuated tailgate. This device allows a user to perform an actuation gesture in the foot area under the rear bumper to open the tailgate. For this purpose, this device uses capacitance sensors whose detection ranges are aligned to detect different spatial areas, and whose signals are used to detect an actuation gesture.

[0003] A locking device for vehicles is described in DE 103 36 335 A1. In this device, a capacitive proximity switch is located near a handle on a vehicle door. This switch only responds and acts on a lock located in the door when two conditions are met.

[0004] An example of a device for actuating electrical or electromechanical devices on or in a vehicle can be found in DE 10 2004 021 505 B3.

[0005] Such contactless operation of a tailgate is useful for comfort and safety if, for whatever reason, it is difficult for a person to operate a tailgate manually.

[0006] Motion detection can detect a body movement, such as performing a fake kick, lifting and swinging a leg, or similar. However, it should be avoided that actions are detected and a function is triggered even if no specific actuation gesture was actually performed. This can happen, for example, when objects (balls, pets, or similar) enter the detection area.

[0007] DE 10 2004 041 709 B3 discloses a device for contactless actuation of a tailgate, which proposes the use of two sensor arrangements with separate detection areas. For this purpose, an ultrasonic distance detection system, which is already provided on the motor vehicle for distance measurements, can be used as one of the sensor devices.

[0008] However, existing recording systems are often too unspecific with regard to the user and do not have the desired recording accuracy.

[0009] The object of the invention is to provide an evaluation method for contactless sensor arrangements which increases the detection accuracy.

[0010] This object is achieved by a method having the features of patent claim 1 and a method for calibrating a sensor arrangement having the features of patent claim 8.

[0011] According to the invention, the method monitors the signals from at least two sensor arrangements. These can be, for example, capacitive sensor arrangements, as in the aforementioned publication. A first of the sensor arrangements is monitored until a characteristic signal response s1 is received from it or until it is detected. This first characteristic signal response is a condition for the rest of the method and can also serve as a trigger for the entire method.

[0012] Subsequently, the second sensor arrangement is monitored until a second characteristic signal s2 is detected. In this context, a characteristic signal is any signal that highly reliably indicates a change in the position of a body within the sensor's detection range. In particular, a significant increase or decrease in a signal may be detected. In the case of capacitive detection, this would be the increase or decrease in the capacitance value or a charge state. The corresponding detection methods and signal responses are known from the art, e.g., the documents cited above.

[0013] The times of detection of both the first signal s1 in the first sensor array t1 and the second signal s2 in the second sensor array t2 are recorded and stored. Finally, further signal responses are queried. First, the second sensor array is monitored again until a characteristic signal response s3 can be detected there. A corresponding time t3 is stored.

[0014] Finally, the first sensor arrangement is monitored until a characteristic signal response s4 is queried; this time point is also stored as time value t4.

[0015] Depending on the sensor used, the control and recording of the individual signal responses takes place in a sensor-specific manner, as is known from the state of the art. Further interpretation and processing of the signals is essential.

[0016] According to the invention, this method utilizes the fact that at least two sensor arrangements are interrogated in time, wherein one of the sensor arrangements, in the formulation of the claim the first sensor arrangement, is the first to detect a change in state due to an actuation gesture due to its arrangement and orientation on the motor vehicle. This sensor arrangement can, for example, be oriented towards the rear area of the motor vehicle, while the second sensor arrangement is oriented more in the area below the motor vehicle and detects changes in state there. It is inevitable that the user or the body part performing the gesture will first enter the detection range of the first sensor arrangement, which accordingly also determines the further method steps.

[0017] If the actuation gesture is executed correctly, the user or the operating body part also enters the detection range of the second sensor and triggers the characteristic second signal response at time t2.

[0018] Once the gesture is fully executed in one direction, the user returns to their starting position, with the part of the operating body that was moved to execute the gesture leaving the detection range of the second sensor arrangement, resulting in a characteristic signal response s3 at a time t3. Finally, the first sensor arrangement also detects the user's return to their starting position and outputs the signal response s4 at time t4. It is essential that the temporal sequence of events is coordinated with the spatial arrangement of the sensor arrangement on the motor vehicle with separate sensor devices.

[0019] In a further method step, the acquisition times of the characteristic signal responses are subjected to arithmetic operations; in particular, time differences are formed from time-value pairs. In this way, the presence of an actuation gesture can be inferred. For this purpose, the ratios of the time differences can be determined (for example, the ratio of the time differences for a forward movement and a backward movement) and these ratios can be subjected to a plausibility check. Furthermore, it can be checked whether the corresponding time differences of the characteristic signals lie within tolerance ranges around time differences that are realistic for a specific usage gesture. These time differences can be stored as comparison parameters.

[0020] In contrast to known methods, this method takes into account the highly individual temporal component of the sensor information to increase detection accuracy. It not only checks whether the signal sequence from the vehicle's sensors is detected in a plausible sequence, but also evaluates the precise temporal sequence. According to the invention, at least four different time values are evaluated—however, more time values can also be recorded and considered.

[0021] Since the times and comparison parameters are adaptable to an individual, the invention can take into account the fact that different users will perform an actuation gesture differently. For example, older people will exhibit a different movement profile than younger people. However, with existing facilities, a corresponding adaptation of the method is generally not possible. However, according to the invention, an individually adapted evaluation can be achieved by changing the comparison parameters.

[0022] In principle, any sensor arrangement for detecting time-dependent changes in the position of objects in space can be used. For example, the established capacitive sensors in motor vehicles can be used; these have low power consumption and are robust and resilient.

[0023] Preferably, in the method for detecting movement gestures, the sequence of determined time differences is compared with comparison parameters stored in the motor vehicle system. Additional tolerance values, by which deviations from the values are acceptable, can also be stored in the motor vehicle system. This system-side storage of comparison parameters allows the recording and detection to be adapted over the entire service life of the motor vehicle. If the system-side stored comparison parameters are changed, the recording accuracy can be optimized for a changed user profile. In addition, comparison parameter groups can be saved for different users, which can then be retrieved depending on the user. For example, if a user is identified by a personal ID transmitter, the parameter set belonging to the user can be used to compare the recorded time differences.

[0024] In addition, customized or user-dependent tolerance values can be used to compare the time differences. This allows for the fact that older users, for example, exhibit greater fluctuation in their time profiles when executing a movement gesture and therefore require more tolerant detection. Furthermore, depending on user preference, the tolerance can be reduced to further reduce false detection.

[0025] In a preferred embodiment of the invention, the method for detecting the movement gesture is aborted if a predetermined maximum duration has been exceeded since the method was initiated. The detection method can be initiated either by a targeted actuation by the user or by the detection of a signal response in the first detection sensor. The sensor used for this purpose is usually the one with the detection area furthest from the vehicle. Exceeding the predetermined maximum duration ensures that a random sequence of untargeted signal responses cannot trigger an actuation. The predetermined maximum duration for the execution of the movement gesture, calculated from the first detection by a first sensor, is usually a time window of a few seconds, e.g. 3 to 10 seconds.Reducing the maximum duration reduces the number of false positives, but on the other hand increases the demands on the user.

[0026] In an advantageous development of the invention, further characteristic signals sx with associated detection times tx are recorded from the temporal signal responses of the sensors. These further characteristic signals can lie between the aforementioned signals s1, s2, s3 and s4, or lie before or after these signals in time. Other characteristic signals that can be considered include, for example, the signal-to-noise ratio of the sensor responses as well as maximum and minimum values. These further characteristic signals can also be evaluated in addition to the time differences in order to further increase the recognition accuracy of the movement gesture. If, for example, a certain signal-to-noise ratio is not met, this can be attributed to severe impairment of the sensors, e.g. due to external influences, and the generation of a positive confirmation response is prevented.

[0027] This consideration of additional signal responses further increases the security and reliability of the detection process.

[0028] It is also advantageous if, in addition to the time differences, at least one of the signals s1, s2, s3, s4 itself is evaluated, and the actuation is also detected depending on this evaluation. The characteristic signals at which the times mentioned according to the invention are recorded are signal patterns, in particular signal changes that indicate a significant change in the detection range of the respective sensor arrangement. In the case of capacitive sensors, for example, a strong change in capacitance is detected as soon as a body part of a user moves within the detection range, approaches, or generally changes its position relative to the sensor arrangement.

[0029] According to the invention, it may be sufficient to use only the time differences between the detection of these signals to generate the actuation signal. However, an additional evaluation of at least one of these signals increases the detection accuracy. For this purpose, an individual signal can be considered in its absolute values, e.g., a signal increase or decrease of one of the signals. However, links can also be formed between the characteristic signals, for example, by calculating the ratios of the signal strengths of the different sensor responses. These values can be evaluated alongside the time differences.

[0030] In a further preferred embodiment of the invention, an ID transmitter that can be carried by a user is queried in order to verify authorization for access to the requested function, wherein the query of the ID transmitter takes place after the time t1 and preferably before the time t4.

[0031] Coupling the process with a query of the ID transmitter ensures that only authorized persons have access to the activated function. If the query occurs after time t1, the sensor response of the first sensor array can be used to wake up the system for ID transmitter query. This query typically takes place via wireless communication with the vehicle's control system. The query before time t4 ensures that there is no noticeable delay, since the ID transmitter query does not occur after the operating gesture has been verified.

[0032] According to the invention, depending on the ID transmitter verification, a user profile associated with the ID transmitter with stored comparison parameters is used to evaluate the time differences. Depending on which user and associated ID transmitter is detected, different parameter sets stored in the vehicle can be accessed. This reduces false detections and increases detection reliability.

[0033] According to the invention, a method for calibrating a sensor arrangement is implemented such that, upon request by the user, the sensor arrangement is placed into a calibration mode. The user can then perform actuation gestures to train the system for their individual movement sequences. The calibration state can be initiated by appropriate switching arrangements on the motor vehicle or by a trigger on the ID transmitter or vehicle key carried by the user. On the motor vehicle side, the characteristic signal responses are then recorded, but the associated times are not evaluated but used as future comparison parameters. A user accordingly places the sensor arrangement in a calibration-sensitive state, performs the actuation gesture, and then acknowledges that it is a gesture representative of them.The corresponding signal times t1, t2, t3, and t4 are used to generate comparison parameters for future evaluations. This can also be done by averaging across numerous training sessions. Depending on how much the temporal values differ during training by the same person, the tolerance range for future recordings can be expanded or narrowed. This can be done, for example, by calculating a standard deviation and the spread.

[0034] It is essential that the characteristic temporal sequence of signal values is taken into account in future evaluation procedures, which significantly increases detection accuracy and reliability. The sensor arrangement no longer needs to be designed for a general, tolerant detection profile, but can instead be adapted much more selectively and precisely to the specific user.

[0035] In the method for calibrating the detection device, the invention provides that a user performs the calibration process multiple times, and the comparison parameters generated in the previous runs are combined with the comparison parameters determined in the current run to form new comparison parameters. If the calibration process is performed multiple times, both a moving average can be calculated and the fluctuation range of the respective time differences around these average values can be recorded. In this case, the method is also adaptable in terms of its tolerance, since the average values are assigned different tolerance ranges depending on the actual movement sequences performed, which are stored in the system.

[0036] This can be done by having the user reset existing parameters so that they, for example as the new owner of the vehicle, can carry out a completely new learning process or calibration. Alternatively, it can be provided that a sliding method with the values is used to calculate the new parameters. This method takes into account a limited number of previous values, while values prior to this number are excluded from the evaluation. For example, a sliding average can be calculated over a certain number of runs, for example ten runs, and this number of runs is also taken into account when determining the tolerance ranges. Thus, if a user has completed ten learning runs, they can be sure that previous calibration processes will no longer be taken into account in future evaluations.

[0037] In a preferred embodiment, an associated counter is incremented with each run of the calibration process, which is used for a running averaging, i.e., a moving or weighted average of the comparison parameters to be stored. In this way, averaging can also be performed over a larger number of learning processes, with the recognition accuracy being continually refined. In this case, however, care must be taken to prevent mixing of different user profiles as much as possible in order to avoid unnecessarily reducing the recognition accuracy. As already explained above, the system can store associated comparison parameters and data in multiple user profiles for this purpose.

[0038] In addition to the temporal differences, additional comparison parameters can also be stored, as explained above with regard to the characteristic signal responses. For example, if, during the learning process, the ratios of the signal responses of the various sensors are saved in addition to the difference values and used in subsequent recognition processes, this increases the accuracy of the process. In this way, in addition to the temporal profile, which is highly individual for each user, the shape and mass of the respective operating body parts can also be included in the evaluation, as these influence the absolute signal responses. This evaluation can also be weighted.

[0039] The invention will now be explained in more detail with reference to the accompanying figures. Fig. 1A shows schematically the arrangement of a sensor arrangement for carrying out the method according to the invention; Fig. 1B shows the schematic arrangement of Fig. 1A in a different view; Fig. 2A shows the sequence of a learning process in the form of a program flow chart; Fig. 2B and Fig. 2C explain the Fig. 2A shown subroutines; Fig. Figure 3 shows schematically the determination of the times and time differences according to the invention;

[0040] In Fig. 1A shows the rear of a vehicle 1. A sensor electrode 2 is mounted in the area of the rear bumper. A further sensor electrode 3 is arranged below the sensor electrode 2. The sensor electrodes 2 and 3 are each connected to the control and evaluation device 5. A vehicle control unit 4 is arranged at any other location in the vehicle (see Fig. 1B).

[0041] The electrodes are charged via the associated control / evaluation device 5, and the change in capacitance of the electrodes upon approach of a body, e.g., a part of the operator's body, can be detected by charge evaluation. This principle of a capacitive sensor is well known in the field of automotive engineering.

[0042] In this example, the sensor electrode arrangement 3 runs essentially parallel to the electrode 2.

[0043] When requested to operate the vehicle, an operator standing behind the vehicle can, for example, move their lower leg in a pivoting motion under the bumper. This movement and approach is detected by both the electrode array 2 and the sensor electrode 3, as the change in capacitance is repeatedly queried and evaluated. In this regard, reference is also made to the aforementioned document DE 10 2008 063 366 A1.

[0044] An actual opening command is generated only by the central control unit 4. The control / evaluation unit 5 supplies this control unit 4 with a corresponding operating signal, which is generated using a neural network. The control unit 4 determines whether an actual opening is triggered based on this signal and other parameters (vehicle standstill, etc.).

[0045] Fig. Figure 2A shows the sequence of a training process for the method according to the invention. First, in step 10, the training process is triggered. This can be done, for example, by pressing a corresponding button on the user's ID transmitter. The user presses a button or key combination on their ID transmitter or vehicle key to signal to the vehicle that a training process for the sensor arrangement should now begin.

[0046] In step 20, the vehicle checks whether an authorized ID transmitter is within the vehicle's detection range. This can also include a distance determination and a plausibility check. Only a user within a specified radius of the vehicle should be allowed to start the training process.

[0047] If the authorization check in step 20 is positive, a timer is reset and started in step 30. In step 40, the subroutine for monitoring the first capacitive sensor is initiated. An explanation of the monitoring subroutines is provided in Fig. 2B. The temporal signal curve of the first capacitive sensor is monitored and a characteristic signal change is determined. This characteristic signal change is given by a significant change in the sensor value, e.g., by a sharp increase or decrease (see explanations for Fig. 3). Short-term signal peaks are ignored. During monitoring, a repeated check is performed to determine whether the previously started timer TS has exceeded a timeout limit t0. If this is the case, the process is aborted. Otherwise, sensor monitoring continues until a characteristic signal change is detected. The detection methods previously used for capacitive sensors can be used to detect the characteristic signal response.

[0048] Once the characteristic signal change of the first capacitive sensor has been detected, the corresponding first time value t1 is stored in step 50. In steps 60 and 70, the process is performed analogously for the second capacitive sensor.

[0049] As in Fig. 1a and Fig. As can be seen in Figure 1b, the capacitive sensors 2, 3 have different arrangements and different detection ranges. A properly performed training process will inevitably first result in a characteristic signal in the first capacitive sensor 2 and then in the second capacitive sensor 3. This is due to the fact that, in the intended manner, the movement by a user first passes through the detection range of the first capacitive sensor 2 and then the second capacitive sensor 3. The movement when moving from behind under the vehicle is always detected first by the first and then by the second sensor.

[0050] This sequence is also reproduced in the detection method and training method according to the invention and thus implicitly contains a plausibility check of the correct sequence of the movement process.

[0051] In step 70, a second time value t2 is again stored for the characteristic signal change of the second capacitive sensor, whereby during the monitoring the exceeding of a maximum permissible time specification is repeatedly checked and the process is terminated if necessary.

[0052] In steps 80 and 90, the second capacitive sensor 3 is again monitored, but this time in anticipation of another characteristic signal change, namely one that indicates a part of the operating body moving out of the detection range of the second capacitive sensor. Monitoring the first and second capacitive sensors in steps 40 and 50, and 60 and 70, respectively, aims to detect a body part moving into the detection range. Detection in steps 80 and 90, as well as 100 and 110, occurs in anticipation of the part of the operating body moving out of the detection range. Therefore, the characteristic signal changes are evaluated accordingly, and inappropriate signal responses are discarded. This also constitutes an implicit plausibility check, since the chronological sequence is repeatedly checked by monitoring for the timeout condition being exceeded.It is clear at this point that the characteristic signal response for each sensor can be understood as a completely different signal response.

[0053] If no timeout has occurred by the time the fourth time value is saved in step 110 for the characteristic signal response of the first capacitive sensor in step 100, the calculation of the time differences and the storage of the time differences are carried out in step 120. The associated method is described as an example in Fig. 2C. Various time differences characteristic of the movement sequence are determined. The time differences determined from the current run are weighted with the time differences already present in the system to calculate averages and summarize fluctuation ranges from the current training session and historical time differences from previous training sessions. The new weighted averages and fluctuation ranges are then saved as comparison parameters for future evaluation procedures.

[0054] Fig. Figure 3 shows, using two signal curves from the two sensor arrangements 2, 3, the temporal progression and the determination of the corresponding times and time differences. The two sensor arrangements are still capacitive sensor arrangements (see Fig. 1A, Fig. 1B), which react with signal changes when a part of the operator's body moves into or out of the detection area. These signal changes are represented by the signal changes s1, s2, s3, and s4. The times t1, t2, t3, and t4 correspond to the corresponding signal changes. Time t0 represents the starting point of the detection process or training process. The time arrows d1, d2, d3, and d4 represent selected time differences between the detection times.

[0055] In this illustrated example, it is clear that the upper signal curve belongs to the first sensor arrangement 2, and at time s1 the user's leg is moved towards the vehicle, causing a signal change, in this case a signal drop, in sensor 2. The operating body part, in particular the lower leg and foot of the user moves through the detection range of the first sensor 2 and reaches the detection range of the second sensor 3 at time t2 with signal response s2. After stopping the movement and moving the foot back, it leaves the detection range of the second sensor arrangement 3 again at time t3 and signal s3 and also leaves the detection range of the first sensor 2 again at time t4, which is expressed in signal s4.

[0056] In this example, significant signal changes are evaluated as characteristic signals s1, s2, s3 or s4, but absolute value comparisons or other detection criteria can also be used.

[0057] The evaluation of an activity process during everyday activity is largely similar to that in the Fig. 2A, Fig. 2B and Fig. 2C. This evaluation procedure is described in Fig. 4. The subroutine for monitoring the sensors was described above with reference to Fig. 2B explained.

[0058] The key difference to the calibration procedure is that instead of storing time differences, a comparison of time differences and other parameters is now performed in step 220. The motor vehicle uses values that were determined and stored during the training processes.

[0059] Numerous modifications are possible within the scope of the invention. In particular, various types of sensor arrangements can be used for the evaluation. The invention is by no means limited to the use of capacitive sensors. Furthermore, capacitive sensors can be combined with other sensor arrangements, such as the inclusion of rear ultrasonic sensors for parking maneuvers. Furthermore, the method can be combined with the use of other, already existing sensors.

[0060] For example, it is possible to use a rear-view camera to check whether a user is actually present in the relevant area when a training process is initiated. The training process can only be carried out if the rear-view camera detects a suitable body in the detection area. Furthermore, the invention is by no means limited to the use of two sensors; rather, additional sensors can also be used, which further increases the detection accuracy of the method. It is essential that the temporal sequence of the movement gesture and not just the signal change caused by the movement gesture is included in the evaluation. Since the temporal sequence is extremely individual for each user, this increases the detection accuracy of a corresponding sensor arrangement.

Claims

[1] Method for actuation query for a function on a motor vehicle (1) with a sensor arrangement, wherein the sensor arrangement has at least two spaced sensor devices (2, 3) for detecting approaches of a body part, comprising the steps, Monitoring the signals of a first sensor device (2) from the sensor devices (2, 3) until a characteristic first signal response s1 is detected by the first sensor device (2) and detecting an associated time t1, Monitoring the signals of a second sensor device (3) from the sensor devices (2, 3) until a characteristic second signal response s2 is detected by the second sensor device (3) and detecting an associated time t2, Monitoring the signals of the second sensor device (3) until a characteristic third signal response s3 is detected by the second sensor device (3) and detecting an associated time t3, Monitoring the signals of the first sensor device (2) from the sensor devices (2, 3) until a characteristic fourth signal response s4 is detected by the first sensor device (2) and detecting an associated time t4, Determining at least two time differences, formed by subtracting two different values from t1, t2, t3 and t4, Evaluating the time differences and generating an actuation signal depending on the evaluation, Depending on the verification of an ID transmitter, a user profile assigned to the ID transmitter with stored comparison parameters is used to evaluate the time differences. [2] Method according to claim 1, characterized bythat during evaluation the determined time differences are compared with stored comparison parameters, whereby an actuation signal indicating an operating request is generated if the time differences deviate from the comparison parameters by less than specified tolerance values. [3] Method according to claim 1 or 2, wherein the method is aborted and a negative actuation signal is generated if the method lasts longer than a predetermined maximum duration. [4] Method according to one of claims 1 to 3, wherein further characteristic signals sx are detected with associated detection times tx. [5] Method according to one of claims 1 to 4, wherein in addition to the time differences, at least one of the signals s1, s2, s3, s4 is also evaluated, so that the actuation signal is also generated as a function of this evaluation. [6] Method according to one of claims 1 to 5, wherein an ID transmitter which can be carried by a user is queried in order to verify an authorization for access to the requested function, wherein the query of the ID transmitter takes place after the time t1, preferably before the time t4. [7] Method for calibrating a sensor arrangement for an actuation query for a function on a motor vehicle (1), wherein the sensor arrangement has at least two spaced-apart sensor devices (2, 3) for detecting approaches of a body part, comprising the steps, Monitoring the signals of a first sensor device (2) from the sensor devices (2, 3) until a characteristic first signal response s1 is detected by the first sensor device (2) and detecting an associated time t1, Monitoring the signals of a second sensor device (3) from the sensor devices (2, 3) until a characteristic second signal response s2 is detected by the second sensor device (3) and detecting an associated time t2, Monitoring the signals of the second sensor device (3) until a characteristic third signal response s3 is detected by the second sensor device (3) and detecting an associated time t3, Monitoring the signals of the first sensor device (2) from the sensor devices (2, 3) until a characteristic fourth signal response s4 is detected by the first sensor device (2) and detecting an associated time t4, Determining at least two time differences, formed by subtracting two different values from t1, t2, t3 and t4, Saving the time differences as comparison parameters for subsequent evaluations of an actuation request, wherein the method can be carried out multiple times and wherein the comparison parameters generated in previous runs are combined with the stored comparison parameters from a current run to form a new comparison parameter. [8] Method according to claim 7, wherein during each run of the calibration process a counter is incremented, which counter is used for a concurrent averaging of the newly stored comparison parameter with the averaged comparison parameters from previous runs. [9] Method according to one of claims 7 to 8, wherein additionally as further comparison parameters characteristic values of at least one of the characteristic signal responses s1, s2, s3 and s4 are stored.

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