Method and device for detecting a signal characteristic of a sensor signal originating from a sensor

Abstract

Method and apparatus for detecting a signal characteristic of a sensor signal originating from a sensor (2) comprising the steps of: sampling (S1) the sensor signal (SIG) at a sampling rate to generate samples (x); calculating (S2) a correlation measure (d1) for the sensor signal ((SIG)) by summing the absolute differences between two successive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples; and determining (S3) a signal characteristic (SC) of the sensor signal (SIG) based on the calculated correlation measure (d1).

Description

[0001] The invention provides a method and a device for detecting a signal characteristic of a sensor signal originating from a sensor, in particular a signal originating from a MEMS sensor, and in particular a method and a device for extending the service life of a sensor by switching to a special operating mode after a sensor malfunction has been detected. State of the art

[0002] EP2399099A1 describes a MEMS gyroscope with tunable drive frequency.

[0003] In microelectromechanical systems (MEMS), leakage currents typically arise from a short circuit at the electrodes when a particle is chipped off the moving body and adheres to the underlying electrode. Such a short circuit at the electrodes can manifest as significant noise in the sensor's output characteristics. The impact energy on the sensor plays a crucial role in particle detachment. Chipping due to high impact energy results in polysilicon particles of varying sizes and distributions, for example, in MEMS inertial sensors. Fragments from the suspended mass, which can be several micrometers in size, adhere to the stopper. However, due to the high impact energies, these fragments can also be carried several micrometers away from the point of impact.Kinetic impact energies of up to 40 nJ (1-3 m / s² for most inertial measurement units) can correlate with silicon fracture and further particle spalling, leading to leakage current and high noise. To avoid spalling particles, conventional considerations include modifying the design of the various shock absorber shapes in the fixed / moving parts. Several shock absorber geometries are suitable, such as sharp, flat, or round. Sharp features result in less spalling than round and flat features because the test mass is deflected by the sharp geometry after impact, and the energy transfer between the mass, suspension springs, and feature occurs differently. The kinetic energy is transferred more readily to the lateral deflection of the springs than to the deformation of the impact / mass (and potential failure).While the design of shock absorbers can in principle improve the robustness of inertial sensors against large impacts, real devices often have distributed shock absorber arrangements rather than a single shock absorber.

[0004] Another conventional approach to avoiding noise without requiring a design change to the MEMS sensor is to use positive electromechanical feedback (EMF) at the pull-in point, which is essentially defined as the voltage at which electrostatic attraction causes a pull-in effect between two capacitor plates of the MEMS structure. However, with standard integrated electronics, the intrinsic measurement resolution of capacitive MEMS sensors is difficult to achieve. This is typically due to a mismatch between the MEMS device and the readout electronics. This mismatch occurs because standard readout devices generally prefer small or medium source impedances, whereas MEMS devices have a high impedance. Furthermore, CMOS circuits are known to contribute a large 1 / f noise.One way to reduce impedance mismatch and 1 / f noise is to use RF readout techniques that utilize a small-signal model for noise and stability analysis.

[0005] The capacitance of a MEMS sensor, described by the moving mass m and the spring constant k, can be monitored using an RF capacitance bridge. The output signal of the capacitance bridge is downconverted, amplified, and fed back to the MEMS device. Since the carrier frequency is far above the mechanical frequency band of the MEMS, the equivalent force noise of any readout amplifier at the pull-in pin can be completely eliminated. The contribution of the readout noise increases with frequency at the pull-in point, leading to a limitation of the noise-matched bandwidth. Consequently, particle shedding can cause leakage currents, which can be reduced by hardware modifications, such as changes to the bumper shape, or by 1 / f noise reduction. However, the conventional approach, which requires hardware modifications, necessitates the implementation of complex noise reduction circuitry.

[0006] Conventional noise detection methods typically use reference signals for correlation with the signal in question. However, such a reference signal is often unavailable and can only be generated by providing a dedicated reference signal generator, thus increasing the complexity of the circuit. With regard to noise detection, conventional approaches generally involve higher computational costs. Either the calculation requires more processing steps, or the signal of interest must be compared to a reference signal. Disclosure of the invention

[0007] According to a first aspect, the invention provides a method for recognizing a signal characteristic of a sensor signal originating from a sensor, comprising the steps of: sampling the sensor signal at a sampling rate to generate sample values; calculating a correlation measure for the sensor signal by summing the absolute differences between two successive sample values ​​of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a specific number of sample values; and determining a signal characteristic of the sensor signal based on the calculated correlation measure.

[0008] A significant advantage of the method according to the invention is that only the sensor signal originating from the sensor is processed and no separate reference signal is required.

[0009] This represents a significant simplification of the circuit design and reduces the complexity of signal processing. Furthermore, it expands the range of possible applications.

[0010] The method according to the invention does not require any modifications to an existing circuit and is preferably based on appropriately adapted software.

[0011] The method according to the invention offers an efficient way to detect noise in a sensor signal.

[0012] The method according to the invention is therefore suitable for all sensors that provide an analog sensor signal, in particular for MEMS sensors.

[0013] Monitoring of the sensor signal using the method according to the invention can be performed continuously in the background. The data processing of the monitored sensor signal preferably takes place in real time.

[0014] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the calculated correlation measure is compared with a threshold value.

[0015] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the sensor signal is classified as noise-like if the calculated correlation measure is above the threshold value.

[0016] The threshold can be predefined. Alternatively, the threshold can be set via an interface.

[0017] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the sensor signal is classified as regular if the calculated correlation measure is below the threshold value.

[0018] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the sensor signal is classified as constant if the calculated correlation measure is zero.

[0019] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the sampled values ​​of the sampling sequence are temporarily stored and the correlation measure is calculated on the basis of the temporarily stored sampled values ​​of the sampling sequence.

[0020] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the correlation measure is calculated iteratively without intermediate storage of the sampled values ​​of the sampling sequence. This allows for a lower latency.

[0021] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, a further correlation measure for the sensor signal is calculated by summing the products of two consecutive samples from a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a specific number of samples. The number N of samples in the sampling sequence can be fixed. Alternatively, the number N of samples in the sampling sequence can be adjustable for the respective application. Dynamic adjustment of the number N of samples during the ongoing operation of the sensor is also possible. The number N of samples in the buffered sampling sequence can, for example, be set as a function of a tolerable latency.

[0022] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the two correlation measures (d1, c1) calculated for the sensor signal are stored in pairs. This allows a more specific determination of the sensor signal's signal characteristic.

[0023] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, a ratio between the two correlation measures (d1, c1) is calculated. This allows a more specific determination of the sensor signal's signal characteristic.

[0024] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the signal characteristic of the sensor signal is further specified depending on the calculated ratio of the two correlation measures (d1, c1).

[0025] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, it is assessed, based on the determined signal characteristic of the sensor signal, whether the sensor is working without interference or has an interference.

[0026] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, an operating mode for dealing with the detected disturbance is automatically activated after detection of a disturbance of the sensor.

[0027] The handling of a detected sensor malfunction can vary depending on the application. The sensor can be deactivated and / or its signal ignored. Furthermore, switching to a redundant, identical sensor is possible. Additionally, an error or warning message can be sent to a higher-level control unit of an assistance system and / or a specially designed operating mode can be activated.

[0028] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor, the sensor signal is output by a MEMS sensor of a gyroscope.

[0029] According to a further aspect, the invention provides a program for recognizing the signal characteristics of a sensor signal originating from a sensor, including program commands for carrying out the method according to the invention. Alternatively, the process steps of the method according to the invention can also be implemented using hardwiring.

[0030] The invention further provides a device for detecting a signal characteristic of a sensor signal originating from a sensor, comprising a sampling unit that samples the sensor signal at a sampling rate to generate sample values; a calculation unit that calculates a correlation measure for the sensor signal by summing the absolute differences between two successive sample values ​​of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a specific number of sample values; and a determination unit that determines a signal characteristic of the sensor signal based on the calculated correlation measure.

[0031] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the determination unit has a comparator that compares the correlation measure calculated by the calculation unit with an adjustable or fixed threshold value, wherein the sensor signal is classified as noisy by the determination unit if the calculated correlation measure is above the threshold value, wherein the sensor signal is classified as regular by the determination unit if the calculated correlation measure is below the threshold value, and wherein the sensor signal is classified as constant by the determination unit if the calculated correlation measure is zero.

[0032] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the device has a buffer memory for temporarily storing the sample values ​​of the sampling sequence generated by the scanning unit.

[0033] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the calculation unit of the device calculates a further correlation measure for the sensor signal by summing the products of two successive samples of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number of samples.

[0034] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the two correlation measures (d1, c1) calculated by the calculation unit for the sensor signal are stored in pairs in a data memory of the device.

[0035] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the calculation unit of the device calculates a ratio between the two correlation measures.

[0036] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the determination unit of the device further specifies the signal characteristic of the sensor signal on the basis of the ratio between the two correlation measures (d1, c1) calculated by the calculation unit.

[0037] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, an evaluation unit of the device assesses, based on the determined signal characteristic of the sensor signal, whether the sensor functions without interference or has an interference.

[0038] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal originating from a sensor, the evaluation unit outputs an activation signal to activate an operating mode for treating a sensor malfunction if a sensor malfunction is detected based on the specific signal characteristic.

[0039] The invention further provides a system with at least one sensor and a device for detecting a signal characteristic of a sensor signal originating from the sensor, comprising: a sampling unit that samples the sensor signal at a sampling rate to generate sample values; a calculation unit that calculates a correlation measure for the sensor signal by summing the absolute differences between two successive sample values ​​of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a specific number of sample values; and a determination unit that determines a signal characteristic of the sensor signal based on the calculated correlation measure.

[0040] In one possible embodiment of the system, the sensor is a MEMS sensor.

[0041] Possible embodiments of the method and device according to the invention will be described in more detail below with reference to the accompanying figures.

[0042] They show: Fig. 1 a flowchart to illustrate a possible embodiment of the method according to the invention; Fig. 2 a block diagram to illustrate a possible embodiment of the device according to the invention; Fig. 3 a further flow diagram to illustrate a possible embodiment of the method according to the invention; Fig. 4A, Fig. 4B Signal diagrams to explain the functioning of the method and device according to the invention; Fig. 5 a diagram to represent a correlation measure calculated for signal characterization; Fig. 6 a diagram to illustrate another correlation measure calculated for signal characterization; Fig. 7 a diagram to illustrate a correlation measure calculated for signal characterization.

[0043] The accompanying drawings are intended to provide a further understanding of the embodiments of the invention. They illustrate embodiments and, in conjunction with the description, serve to explain the principles and concepts of the invention. Other embodiments and many of the advantages mentioned will become apparent with reference to the drawings. The elements of the drawings are not necessarily shown to scale.

[0044] In the figures of the drawings, identical, functionally equivalent and similarly acting elements, features and components - unless otherwise stated - are each provided with the same reference symbols.

[0045] As in Fig. Figure 1 shows that the inventive method for detecting a signal characteristic of a sensor signal SIG originating from a sensor 2 comprises several main steps.

[0046] In a first step S1, the sensor signal SIG is sampled at a sampling rate R to generate sample values ​​(x). The sampling rate R can be fixed or set by an internal controller.

[0047] In a further step S2, a correlation measure (d1) for the sensor signal SIG is calculated by summing the absolute differences between two consecutive samples (xi+1 ; xi) of a sampling sequence of the sensor signal SIG sampled in step S1, where the sampling sequence comprises a certain number, N, of samples.

[0048] In a further step S3, a signal characteristic (SC) of the sensor signal SIG is determined based on the correlation measure (d1) calculated in step S2.

[0049] In one possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, the sensor signal is classified as noise-like (SC1) in step S3 if the calculated correlation measure (d1) is above an adjustable or predefined threshold value (SW), as shown in the flowchart according to Fig. 3 is shown.

[0050] In one possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal originating from a sensor 2, the sensor signal SIG is classified as regular (SC2) if the calculated correlation measure (d1) is below the threshold value (SW).

[0051] In one possible embodiment of the inventive method for detecting a signal characteristic SIG of a sensor signal originating from a sensor 2, the sensor signal SIG is classified as constant (SC3) if the calculated correlation measure (d1) is zero.

[0052] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor 2, the sampled values ​​of the sampling sequence are temporarily stored and the calculation of the correlation measure (d1) in step S2 is carried out on the basis of the sampled values ​​of the sampling sequence temporarily stored in a buffer memory.

[0053] In a possible alternative embodiment of the inventive method for detecting a signal characteristic of a sensor signal SIG originating from a sensor 2, the correlation measure d1 is calculated iteratively in step S2 without intermediate storage of the sampled values ​​of the sampling sequence.

[0054] It is also possible to switch between iterative and non-iterative calculation. For example, in time-critical applications requiring low latency, iterative calculation of the correlation measure d1 can be activated.

[0055] In one possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, in step S3 a further correlation measure (c1) for the sensor signal SIG is calculated by summing the products of two successive samples (xi+1; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples.

[0056] In another possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, the two correlation measures (d1, c1) calculated for the sensor signal SIG in step S3 are stored in pairs.

[0057] In one possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, a ratio v between the two correlation measures (d1, c1) is also calculated in step S3.

[0058] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal SIG originating from a sensor 2, the signal characteristic SC of the sensor signal SIG is further specified as a function of the ratio v of the two correlation measures (d1, c1) calculated in step S3. Preferably, the signal characteristic is further specified as a function of the ratio of the two correlation measures to each other for a specific value of one of the two correlation measures.

[0059] In one possible embodiment of the inventive method for detecting a signal characteristic of a sensor signal originating from a sensor 2, the determined signal characteristic SC of the sensor signal SIG is used to assess whether the sensor 2 is operating without interference or exhibits a malfunction. After detecting a malfunction of the sensor 2, an operating mode for addressing the detected malfunction can be automatically activated.

[0060] In one possible embodiment of the inventive method for detecting a signal characteristic SC of a sensor signal originating from a sensor 2, the sensor signal SIG is output by a MEMS sensor 2 of a gyroscope. However, the method is suitable for a variety of other sensors that provide an analog sensor signal.

[0061] Fig. Figure 2 shows an embodiment of a device 1 according to the invention for detecting a signal characteristic (SC) of a sensor signal SIG originating from a sensor 2, comprising a scanning unit 1A that samples the sensor signal SIG at a sampling rate R to generate sample values ​​(x). The sampling rate R can be predefined for the sensor 2 to be monitored. The sampling rate can also be set by a local control of the device 1 depending on a configuration, for example, depending on the sensor type and / or the application. In one possible embodiment, the scanning unit 1A can be integrated into the sensor 2.

[0062] The device 1 also includes a computing unit 1B, which calculates a correlation measure (d1) for the sensor signal SIG by summing the absolute differences between two consecutive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal SIG, wherein the sampling sequence comprises a specific number, N, of samples. The computing unit 1B can comprise a processor, an FPGA, or an ASIC.

[0063] The device 1 further comprises a detection unit 1C, which determines a signal characteristic SC of the sensor signal SIG based on the correlation measure d1 calculated by the computation unit 1B. In a possible embodiment of the device 1 according to the invention for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, the detection unit 1C has a comparator that compares the correlation measure (d1) calculated by the computation unit 1B with a threshold value SW. The sensor signal SIG is classified as noisy by the detection unit 1C if the calculated correlation measure (d1) is above the threshold value SW. The sensor signal SIG is classified as regular by the detection unit 1C if the calculated correlation measure (d1) is below the threshold value SW.The sensor signal SIG is classified as constant by the determination unit 1C, provided that the correlation measure (d1) calculated by the computation unit 1B is zero.

[0064] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal SIG originating from a sensor 2, the device 1 further comprises a buffer memory for temporarily storing the sample values ​​of the sampling sequence generated by the scanning unit 1A. The sampling sequence comprises a specific number, N, of sample values. This number N can be fixed. Alternatively, the number N of sample values ​​can also be dynamically adjusted during the operation of the sensor 2, for example, in response to detected events or depending on a required maximum latency of the device 1.

[0065] In a possible embodiment of the device according to the invention for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, the calculation unit 1B of the device 1 calculates a further correlation measure (c1) for the sensor signal SIG by summing the products of two successive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples.

[0066] In one possible embodiment of the device according to the invention for detecting a signal characteristic SC of a sensor signal SIG originating from a sensor 2, the two correlation measures (d1, c1) calculated by the calculation unit 1B for the sensor signal SIG are stored in pairs in a data memory of the device 1.

[0067] The processing unit 1B of the device 1 can calculate a ratio v between the two correlation measures (d1, c1) by dividing the two correlation values ​​(v = d1 / c1). In one possible embodiment of the device 1, a signal characteristic SC of the sensor signal SIG can be further specified for a value of one of the two correlation measures based on the ratio v between the two correlation measures (d1, c1) calculated by the processing unit 1B.

[0068] In one possible embodiment of the device according to the invention for detecting a signal characteristic of a sensor signal SIG originating from a sensor 2, an evaluation unit assesses, based on the specific signal characteristic SC of the sensor signal SIG, whether the sensor 2 is functioning correctly or exhibits a malfunction. The evaluation unit can output an activation signal to activate an operating mode for addressing a malfunction of the sensor 2 if a malfunction of the sensor 2 is detected based on the specific signal characteristic SC.

[0069] The invention further provides a system SYS with at least one sensor 2 and a device 1 for detecting a signal characteristic SC of a sensor signal SIG originating from the sensor 2, comprising a scanning unit 1A that samples the sensor signal SIG at a sampling rate R to generate sample values ​​(x), a calculation unit 1B that calculates a correlation measure (d1) for the sensor signal SIG by summing the absolute differences between two successive sample values ​​(xi+1; xi) of a sampling sequence of the sampled sensor signal SIG, wherein the sampling sequence comprises a specific number, N, of sample values, and a determination unit 1C that determines a signal characteristic SC of the sensor signal SIG based on the calculated correlation measure d1. In a possible embodiment of the Fig. In the system SYS 2 shown, sensor 2 is a MEMS sensor. In one possible implementation, the device 1 can also be integrated into a housing of the sensor 2 to be monitored.

[0070] In one possible embodiment, a decision is also made as to whether an enhanced coverage mode (EC) should be activated to achieve high robustness of the signal sensor 2. To decide whether or not to trigger the EC mode, the signal SIG of the sensor 2, for example, a gyroscope, is analyzed. The gyroscope signal exhibits certain characteristics that indicate that the EC mode should be activated. These signal characteristics are recognized by an algorithm according to the inventive method in order to automate the triggering of the EC mode.

[0071] Essentially, two different signal characteristics (SC) can be distinguished from a normal gyroscope signal. The main characteristic of the first signal characteristic, SC1, is significant noise. This noise is broadband and is described as white. The main characteristic of the second signal characteristic, SC2, is that the signal from one of the three gyroscope axes is fixed or constant.

[0072] A gyroscope typically provides three sensor signals (SIG) that measure the angular velocity around the three orthogonal axes. These axes are usually labeled the x, y, and z axes. Each of these SIG signals represents the rate of rotation around one of the three axes. The three main signals provided by a gyroscope include: Angular velocity around the x-axis: This measures the rotational movement around the x-axis (roll). Angular velocity around the y-axis: This measures the rotational movement around the y-axis (pitch). Angular velocity around the z-axis: This measures the rotational movement around the z-axis (yaw).

[0073] These three sensor signals (SIG) of the gyroscope together allow for a complete characterization of the rotational movements of an object to which the gyroscope is attached. Modern gyroscopes, such as those used in smartphones, drones, and vehicles, continuously provide these three sensor signals, which can then be used for controlling, navigating, or stabilizing the respective system. SIG signals exhibiting unusual signal characteristics, particularly the first signal characteristic SC1 (noise) and the second signal characteristic SC2 (steady or constant signal), must be distinguished from a normal gyroscope signal.

[0074] In one possible embodiment, a corresponding device 1 is provided for each of the three sensor signals SIG of the gyroscope for detecting a signal characteristic SC of the sensor signal SIG. Alternatively, the sensor signals SIG can be sequentially switched to the signal input of the processing unit 1 B after sampling by means of a multiplexer. As soon as a disturbance in one of the three sensor signals is detected, appropriate handling of the disturbance takes place automatically.

[0075] Since the noise level is high in the case of the first signal characteristic (SC1), the variance VAR of the gyroscope signal represents a possible criterion that can be used for differentiation. However, a disadvantage of using the variance VAR is that the variance VAR of a signal contains no information about the correlation of the subsequent sampling of this signal. This is important here, however, because the differentiation should also work when sensor 2 is in motion.

[0076] In this case, it is possible to use a third signal feature (SC3) that also has a high variance VAR, e.g., a signal SS that has the shape of a sine wave. However, in these cases, the algorithm should not choose the first signal feature SC1, but rather the third signal feature SC3.

[0077] In the method according to the invention, a measure d1 is used that encompasses the correlation between successive samples within a timeframe of N samples. In the case of a sinusoidal signal SS, the subsequent samples are highly correlated. A noise signal RS, for example white noise, leads, on the other hand, to a low correlation value d1 between successive samples, since ideally each sample of the noise signal RS is statistically independent and therefore also uncorrelated.

[0078] In the Fig. 4A, Fig. 4B shows two signals SS and RS. In the upper graphic ( Fig. 4A) shows a sinusoidal signal SS with a frequency of 100 Hz. In the lower diagram ( Fig. Figure 4B) shows a random white noise signal RS. The x-axis represents time t in seconds (s), and the y-axis represents the signal value in arbitrary units. The sampling rate R of the two in Fig. 4A, Fig. The frequency of the signals shown in 4B is 6.4 kHz. The signals in Fig. 4A, Fig. 4B both have a variance VAR of approximately one. Therefore, a distinction between these two signals SS and RS based on the variance VAR is not possible. If one calculates the correlation between successive samples for both signals SS and RS in Fig. 4A, Fig. 4B, the correlation values ​​are 6.4 · 104 and -204, respectively. The absolute value therefore differs by a factor of more than 312, so that a clear distinction between the in Fig. 4A shown sinusoidal signal SS and the one in Fig. The noise signal RS shown in 4B is possible.

[0079] In the following equation (1), a correlation value c1 between successive samples or sample values ​​is given for a frame of length N: c1=∑i=1N−1xi+1⋅xi, where xi is the value of the i-th sample of the signal SIG.

[0080] However, using the correlation function in equation (1) has disadvantages with regard to the signal features to be classified, particularly for identifying the second signal feature SC2. The main feature of the second signal characteristic SC2 is that the signal from one of the three gyroscope axes is fixed or constant. The correlation between successive samples must be maximal, since, in the case of the second signal feature SC2, the successive samples do not differ from each other. This raises a problem, namely the definition of the maximum value.

[0081] When calculating equation (1), it is not known whether the calculated value is the maximum value of the autocorrelation function. To obtain an indication, the following equation (2) must also be calculated. However, this requires an additional calculation. c0=∑i=1Nxi⋅xi

[0082] On the other hand, it can be difficult to identify the first signal characteristic, SC1, if the signal SIG is highly noisy. With noise (RS), the correlation value is lower than, for example, with a sine wave (SS). However, this also depends on the amplitude of the sine wave (SS). This can make it difficult to distinguish between the two classes or signal characteristics, SC1 (noise signal RS) and SC3 (sinusoidal signal SS).

[0083] Overall, it is not possible to differentiate the three signal characteristics mentioned SC1, SC2, SC3 solely using the correlation value c1 calculated according to equation (1), as shown in Fig. 5 is recognizable. In Fig. Figure 5 shows the first correlation value c1 defined in equation (1) plotted for different frames of signals with different signal characteristics SC. The correlation value c1 is plotted on the x-axis, and the correlation value c1 is labeled with different markers depending on the underlying signal characteristic SC.

[0084] How to in Fig. As can be seen in Figure 5, the correlation values ​​c1 for the three different signal characteristics SC1, SC2, and SC3 overlap significantly. Therefore, this calculated correlation measure or correlation value c1 alone is insufficient to successfully differentiate between the three signal characteristics SC1, SC2, and SC3.

[0085] Instead of the correlation value c1 defined in equation (1), the method according to the invention primarily uses a different measure for the correlation between successive samples or sample values.

[0086] This correlation measure d1 is defined in equation (3) as follows: d1=∑i=1N−1|xi+1−xi|

[0087] The second correlation measure d1 in equation (3) is the so-called taxi difference between two vectors x1 and x2 with: x→1=[x2x3⋮xN],x→2=[x1x2⋮xN−1].

[0088] The relationship between equation (1) and equation (3) is in Fig. Six different signal properties or signal characteristics SC are shown. The signals are normalized to have a variance VAR of one, and c1 and d1 are normalized by the number N.

[0089] The first signal in Fig. The signal shown in Figure 5 is noise, i.e., a noise signal RS that can be generated with a random number generator. The noise signal RS fulfills the first signal characteristic SC1.

[0090] The second signal is a sinusoidal signal SS. The sinusoidal signal SS fulfills the third signal characteristic SC3.

[0091] The third signal is a mixed signal MS, which is mixed from random noise RS and a sinusoidal signal SS.

[0092] To generate signals with different correlation values, the randomly generated noise can be low-pass filtered with different cutoff frequencies. For example, ten signals are generated for each cutoff frequency, and correlation measures c1 and d1 are calculated for them. Each marker thus represents the correlation value for one signal.

[0093] As in Fig. As can be seen in Figure 6, there is an inverse relationship between the first correlation measure c1 and the second correlation measure d1. If c1 = 1, then d1 = 0. The subsequent course then depends on the underlying signal, as shown in Figure 6. Fig. Figure 6 illustrates this. The course of the various correlation values ​​(c1,d1) between successive samples differs between a noise signal RS and a sinusoidal signal SS. The course of the correlation values ​​(c1,d1) for a mixed signal MS, which is a mixture of a noise signal RS and a sinusoidal signal SS, lies between the other two courses, i.e., between the course of the correlation values ​​(c1,d1) for a sinusoidal signal SS and for a noise signal RS.

[0094] Fig. Figure 6 shows that the first correlation value c1 calculated according to equation (1) and the second correlation value d1 calculated according to equation (3) are both indicators of the correlation between successive samples of a signal. However, there is no fixed ratio between the first correlation value c1 calculated according to equation (1) and the second correlation value d1 calculated according to equation (3). The ratio between the first correlation value c1 and the second correlation value d1 depends on the signal characteristic SC of the signal SIG.

[0095] The calculation of the first correlation value c1 according to equation (1) and the calculation of the second correlation value d1 according to equation (3) therefore allows a statement about the signal characteristic SC. Depending on the ratio v (v=c1 / d1) of c1 and d1, the signal is more "noise-like" (SC1) or more "sinusoidal" (SC3). If the second correlation value d1 is plotted for the different signals Fig. 5, so the distribution for the different signal properties SC differs from Fig. 5. The distribution of the second correlation value d1 is in Fig. 7 to be seen. In Fig. Figure 7 shows the correlation value d1 based on equation (3) for both the signal characteristic SC1 (noise) and the signal characteristic SC3 (sine wave). The signal characteristic SC2 (fixed signal) is shown in Fig. 7 is not shown because the correlation value d1 for this signal characteristic SC2 is always zero.

[0096] The idea behind the calculation of the correlation value d1 according to equation (3) is that the total change between successive samples is accumulated or summed.

[0097] If the accumulated total change between successive samples is large (d1 large), then the successive samples of the signal are only weakly correlated with each other (i.e., noise SC1).

[0098] If, on the other hand, the accumulated total change between successive samples is small (d1 small), then these successive samples are more strongly correlated with each other (more regular signal SC3).

[0099] When the accumulated total change between successive samples is zero (d1=0), the value of the successive samples is equal and the correlation of the successive samples is highest (fixed or constant signal SC2).

[0100] In the method according to the invention, the sum of all differences is calculated and compared with a reference value or threshold value SW. This results in greater robustness against outliers.

[0101] The detection of noise in general is possible with the method according to the invention. The difference between successive values, which is calculated periodically between feedback signals, varies. As soon as a counter threshold for the number N of samples in the sampling sequence is exceeded, the counter Z is reset.

[0102] Conventional algorithms also consider the duration in relation to the total number of samples, and therefore the differences in duration vary depending on the thresholds mentioned above. However, in the method according to the invention, decision-making occurs periodically, i.e., after each fixed number N of samples or sample values. The algorithm according to the invention is efficient in its decision-making and requires relatively little computational effort.

[0103] The correlation value d1 calculated according to equation (3) can be used to detect noise as follows. First, N samples are collected before the correlation value d1 is calculated according to equation (3). If the calculated correlation value d1 exceeds a certain threshold SW (d1 > SW), the signal is a noise signal RS (SC1); if the calculated correlation value d1 is zero (d1 = 0), the signal is steady (SC2). In all other cases, the signal is considered regular (SC3).

[0104] Since collecting N samples requires storage space, it is also possible to calculate the second correlation value d1 iteratively. The iterative calculation of the second correlation value d1 is given in equation (5): d1,n=∑i=1n−1|xi+1−xi|=|xn−xn−1|+∑i=1n−2|xi+1−xi|︸d1,n−1,n∈[2,3,…,N]

[0105] In the iterative calculation described in equation (5), only the value of the previous length d1,n-1 and the last sample xn-1 need to be stored. Using these two values ​​and the current value xn, the second correlation value d1, can be calculated.

[0106] In one embodiment of the method according to the invention, the correlation value d1 is calculated according to equation (3), wherein all samples or sampled values ​​are first collected and stored in a buffer memory before the correlation value d1 is calculated. However, since this requires storage capacity and consumes a certain amount of storage space, in a possible alternative embodiment of the method according to the invention, the correlation value d1 is calculated iteratively according to equation (5).

[0107] Although the present invention has been fully described above with reference to preferred embodiments, it is not limited thereto, but can be modified in many ways. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] EP 2399099A1

[0002]

Claims

[1] Method for detecting a signal characteristic of a sensor signal originating from a sensor (2) comprising the steps; Sampling (S1) of the sensor signal (SIG) at a sampling rate to generate sample values ​​(x); Calculating (S2) a correlation measure (d1) for the sensor signal (SIG) by summing the absolute differences between two consecutive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples; and Determine (S3) a signal characteristic (SC) of the sensor signal (SIG) based on the calculated correlation measure (d1). [2] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 1, wherein the calculated correlation measure (d1) is compared with a threshold value (SW). [3] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 2, wherein the sensor signal (SIG) is classified as noise-like if the calculated correlation measure (d1) is above the threshold value (SW). [4] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 3, wherein the sensor signal (SIG) is classified as regular if the calculated correlation measure (d1) is below the threshold (SW). [5] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 4, wherein the sensor signal (SIG) is classified as constant if the calculated correlation measure (d1) is zero. [6] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to one of claims 1 to 5, wherein the sampled values ​​of the sampling sequence are temporarily stored and the calculation (S2) of the correlation measure (d1) is carried out on the basis of the temporarily stored sampled values ​​of the sampling sequence. [7] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to one of claims 1 to 5, wherein the calculation (S2) of the correlation measure (d1) is performed iteratively without intermediate storage of the sampled values ​​of the sampling sequence. [8] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to one of claims 1 to 7, wherein a further correlation measure (c1) for the sensor signal is calculated by summing the products of two successive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples. [9] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 8, wherein the two correlation measures (d1, c1) calculated for the sensor signal (SIG) are stored in pairs. [10] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 9, wherein a ratio between the two correlation measures (d1, c1) is calculated. [11] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 10, wherein the signal characteristic (SC) of the sensor signal (SIG) is further specified as a function of the calculated ratio of the two correlation measures (d1, c1) to each other for a specific value of one of the two correlation measures. [12] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to one of the preceding claims 1-11, wherein the determined signal characteristic (SC) of the sensor signal (SIG) is used to assess whether the sensor (2) is functioning without interference or has an interference. [13] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 12, wherein after detection of a disturbance of the sensor (2) an operating mode for dealing with the detected disturbance is automatically activated. [14] Method for detecting a signal characteristic of a sensor signal originating from a sensor according to one of the preceding claims 1-13, wherein the sensor signal (SIG) is output by a MEMS sensor of a gyroscope. [15] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) comprising: a scanning unit (1A) that samples the sensor signal at a sampling rate to generate sample values ​​(x); a computation unit (1B) that computes a correlation measure (d1) for the sensor signal by summing the absolute differences between two successive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples; and a determination unit (1C) that determines a signal characteristic (SC) of the sensor signal based on the calculated correlation measure (d1). [16] Device for detecting a signal characteristic of a sensor signal originating from a sensor according to claim 15, wherein the determination unit (1C) has a comparator which compares the correlation measure (d1) calculated by the computation unit (1B) with a threshold value (SW), wherein the sensor signal is classified as noisy by the determination unit (1C) if the calculated correlation measure (d1) is above the threshold value (SW), wherein the sensor signal is classified as regular by the determination unit (1C) if the calculated correlation measure (d1) is below the threshold value (SW), and wherein the sensor signal is classified as constant by the determination unit (1C) if the calculated correlation measure (d1) is zero. [17] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to claim 15 or 16, wherein the device (1) has a buffer memory for temporarily storing the sample values ​​of the sampling sequence generated by the scanning unit (1A). [18] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to one of claims 15 to 17, wherein the computation unit (1B) of the device (1) calculates a further correlation measure (c1) for the sensor signal by summing the products of two successive samples (xi+1 ; xi) of a sampling sequence of the sampled sensor signal, wherein the sampling sequence comprises a certain number, N, of samples. [19] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to claim 18, wherein the two correlation measures (d1, c1) calculated by the computation unit (1B) for the sensor signal are stored in pairs in a data memory of the device (1). [20] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to claim 18 or 19, wherein the computation unit (1B) of the device (1) calculates a ratio between the two correlation measures (d1, c1). [21] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to claim 19 or 20, wherein a determination unit (1C) of the device (1) further specifies the signal characteristic (SC) of the sensor signal on the basis of the ratio between the two correlation measures (d1, c1) to each other calculated by the calculation unit (1B) for a specific value of one of the two correlation measures. [22] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to one of claims 15 to 21, wherein an evaluation unit of the device (1) assesses, on the basis of the determined signal characteristic of the sensor signal, whether the sensor is functioning without interference or has an interference. [23] Device (1) for detecting a signal characteristic (SC) of a sensor signal originating from a sensor (2) according to claim 22 wherein the evaluation unit outputs an activation signal to activate an operating mode for treating a sensor fault if a sensor fault is detected based on the determined signal characteristic (SC). [24] System (SYS) comprising at least one sensor (2) and a device (1) for detecting a signal characteristic (SC) of a sensor signal originating from the sensor (2) according to any one of claims 15 to 23. [25] System (SYS) according to claim 24, wherein the sensor (2) is a MEMS sensor

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