Method, computer program product, storage medium and array sensor

Abstract

A method for estimating a current position (9) of a sensor element in an array sensor (3) comprises the steps of: providing an initial position estimate of the sensor element by an electronic computing device (8) of the array sensor (3); transmitting a sensor signal (14) to the surroundings (15) of the array sensor (3) by means of a transmitting device (6) of the array sensor (3) and receiving a reflected sensor signal (16) by means of a receiving device (7) of the array sensor (3), the sensor signal (14) being reflected on at least one calibration target in the surroundings (15); determining, by the electronic computing device (8), at least the direction of arrival of the reflected signal (16); determining, by the electronic computing device (8), a virtual position of each sensor element according to the direction of arrival analysis on the basis of a minimum residual method; and estimating, by the electronic computing device (8), the current position (9) from the virtual position and the initial position estimate. In addition, the invention also relates to a computer program product, a computer readable storage medium and an array sensor.

Description

Technical Field

[0001] The invention relates to a method for estimating the current position of a sensor element in an array sensor. Furthermore, the invention relates to a computer program product, a computer-readable storage medium and an array sensor. Background Art

[0002] A well-known technical problem is to estimate the current position of a sensor element in a sensor array. In particular, for example at least for partially automated vehicles, the position of the sensors must be known very precisely in order to better capture the surroundings and thus to be able to control the vehicle very safely within the surroundings.

[0003] GP2002344223A1 provides a method for determining the position of an array antenna element to improve its output performance. The array antenna unit is equipped with a STAP controller and has a first function obtained by adding the expected value of the square of the absolute value of the autocorrelation function of the general impulse response of all desired waveforms; the second function is obtained by adding the expected value of the square of the absolute value of the cross-correlation function between the general impulse response of all desired waveforms and the general impulse response of all interference waveforms; the third function is the sum of the first and second functions, and the coefficient of the fourth function is the position of (n-1) other antennas determined relative to the position of one antenna element; the antenna position design unit determines the position of n antenna elements by calculating the positions of other (n-1) antenna elements so as to minimize the fourth function.

[0004] US11,005,580B2 discloses an array antenna calibration method and device for real-time calibration of an array antenna in an open calibration environment. The method comprises: determining an initial beam weight vector matrix of the array antenna to be calibrated according to a preset direction angle of each preset beam direction, and transmitting a first calibration signal to the array antenna to be calibrated along a standard beam direction by a test antenna; determining an amplitude phase arrow between the first calibration signal received by a center channel and the first calibration signal received by a channel according to the first calibration signal received by each channel of the array antenna to be calibrated; and calibrating an initial beam weight arrow matrix using the amplitude phase arrow, thereby obtaining a compensation beam weight vector matrix.

[0005] According to US Pat. No. 7,714,776B2, an antenna array includes a surface consisting of a replicated pattern of conductive tracks, the tracks defining a plurality of ports. A plurality of antennas are located at the ports distributed around the surface. A plurality of radiating transceivers are electrically connected to respective antennas. A plurality of reference transceivers are electrically connected to non-radiating impedances located at respective ports, so that each reference transceiver is surrounded by a group of antennas and electrically coupled to the group of antennas through the tracks. At least one antenna in at least one group of antennas belongs to another group of antennas. The calibration circuit includes a controller associated with each reference transceiver, each controller being arranged to transmit a calibration signal through an associated reference transceiver, and to receive and store a calibration signal received from a selected transceiver of a group of antennas coupled to the reference transceiver. Each controller is further used to receive and store a calibration signal from a selected transceiver for a group of antennas coupled to the reference transceiver. The calibration circuit also includes a circuit for adjusting the phase and amplitude of a signal transmitted and received by the radiating transceiver relative to the stored calibration signal of the selected radiating transceiver for each other transceiver of the antenna group. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method, a computer program product, a computer-readable storage medium and an array sensor, so as to improve the estimation of the current position of a sensor element in the array sensor.

[0007] The method, computer program product, computer readable storage medium and array sensor according to the present invention can solve this problem.

[0008] One aspect of the present invention relates to a method for estimating the current position of a sensor element in an array sensor. An initial position estimate of the sensor element is provided by an electronic computing device of the array sensor. A sensor signal is transmitted to the surrounding environment of the array sensor by a transmitting device of the array sensor, and a reflected sensor signal is received by a receiving device of the array sensor, wherein the sensor signal is reflected on at least one calibration target in the surrounding environment. At least a direction of arrival of the reflected signal is determined by the electronic computing device. A virtual position of each sensor element is determined by the electronic computing device based on a minimum residual method according to the direction of arrival analysis. The current position is estimated by the electronic computing device based on the virtual position and the initial position estimate.

[0009] Thus, an analysis method for estimating the current position of a sensor element is provided. In particular, determining at least the direction of arrival of a signal reflected from the electronic computing device may be provided by the electronic computing device itself or other target localization devices / techniques.

[0010] In particular, the invention aims to estimate 3D antenna position and complex channel imbalance for a measured ranging / Doppler / channel map, e.g. using at least four non-adjacent calibration targets. In particular, when restricted to one subspace, a d-dimensional position estimate requires d targets, and if channel imbalance also needs to be estimated, an additional target is required.

[0011] The target can be in the near field, but the position is preferably "known". In the far field, only the direction of arrival (DoA) information is sufficient. In principle, even the sensor itself can obtain an estimate of the target position information. The signal-to-noise ratio (SNR) is high enough that noise does not significantly interfere with the phase.

[0012] To accomplish this successfully, the antenna position needs to be known in advance, and ideally the three-dimensional error accuracy should be better than half the operating wavelength, λ / 2.

[0013] The range peak shift due to cable delay can also be compensated, but this task is independent of the antenna position and the estimation of channel imbalance. In dynamic scenarios with moving platforms, the range shift on slopes must be compensated first.

[0014] In particular, the method is analytical, so no expensive numerical optimization is required.

[0015] Particularly in the near-field case, it relies on the choice of “fuzziness factors” that are determined based on a priori knowledge of the antenna position and the target position. They are used to pre-transform the data to remove unwanted terms in the bistable range-trailing sequence that could interfere with the analysis process.

[0016] The derivation of this method assumes that the data samples are noise-free. Of course, this method can still be used on noisy data, although its performance may be more sensitive to noise than other methods.

[0017] In particular, at least four reflected signals are received from at least four calibration targets. Less than four calibration targets may also be used. In particular, a d-dimensional position estimate requires d targets, and if channel imbalance is also to be estimated, an additional target is required. Therefore, if a two-dimensional position estimate is calculated, only two calibration targets need to be used. If a two-dimensional estimate is used and channel imbalance is used at the same time, at least three calibration targets need to be used. Three targets must be used for a three-dimensional estimate. If channel imbalance is estimated at the same time, four calibration targets need to be used. Therefore, an accurate estimate of the current position can be made.

[0018] According to another embodiment, a distance to at least one calibration target is also provided, and the virtual position is determined based on the provided distance. In particular, the distance can be provided externally. Therefore, the current position of the sensor can be accurately estimated based on the distance.

[0019] In another embodiment, the channel imbalance of the antenna can also be determined based on the estimated current position. In particular, the channel imbalance of the array sensor can also be determined based on the estimated current position. Therefore, through the provided method, the channel imbalance can also be estimated, so as to determine the countermeasures for the channel imbalance.

[0020] In another embodiment, the channel imbalance is determined using a singular value decomposition algorithm, thereby providing an accurate method for determining channel imbalance.

[0021] In another embodiment, the channel imbalance is determined using a high-order singular value decomposition algorithm, thereby providing an accurate method for determining channel imbalance.

[0022] According to another embodiment, the initial position is a nominal position. Therefore, a normalization of the data can be performed. Therefore, a normalization is performed to decouple the position estimation from the imbalance.

[0023] In another embodiment, the estimated virtual position is guided as the virtual position. Thus, further calculation / estimation of the position can be achieved, especially calculation / estimation with higher accuracy.

[0024] According to a further embodiment, the received signal is compensated for phase influences due to the geometry of the received signal.Thereby, a more accurate method for estimating the current sensor position is provided.

[0025] In a further embodiment, the sensor device is provided as a radar device and / or a communication device. Thus, the sensor device can be used as a radar device and / or a communication device. This has the advantage that the sensor device can be used not only for communication, for example with a satellite, but also for capturing the environment around the motor vehicle.

[0026] In particular, the method is a computer-implemented method. Therefore, another aspect of the invention relates to a computer program product comprising program code means for executing the method described above.

[0027] A computer-readable storage medium comprises at least the computer program product described in the present invention.

[0028] Another aspect of the invention relates to an array sensor, in particular a sensor device and / or a communication device for a motor vehicle or any type of vehicle, comprising at least one transmitting device, a receiving device and an electronic computing device, wherein the array sensor, in particular the sensor device and / or the communication device is configured to perform the method according to the previous aspect.

[0029] In particular, the method is performed by a sensor device and / or a communication device.

[0030] Another aspect of the invention relates to a motor vehicle, which comprises at least the aforementioned sensor device and / or communication device.

[0031] Advantageous embodiments of the method are to be regarded as advantageous embodiments of a computer program product, a computer-readable storage medium, a sensor device and / or a communication device and a motor vehicle. Thus, the sensor device and / or the communication device and the motor vehicle may comprise a device for carrying out the method.

[0032] The invention also includes a control device / electronic computing device for a motor vehicle. The control device may include a data processing device or a processor device, which is suitable for executing an embodiment of the method according to the invention. To this end, the processor device may include at least one microprocessor and / or at least one microcontroller and / or at least one FPGA (field programmable gate array) and / or at least one DSP (digital signal processor). In particular, in each case, a CPU (central processing unit), a GPU (graphics processing unit) or an NPU (neural processing unit) can be used as a microprocessor. In addition, the processor device may have a program code, which, when the processor device is executed, is configured to execute an embodiment of the method according to the invention. The program code may be stored in a data memory of the processor device. For example, the processor device may be based on at least one circuit board and / or at least one SoC (system on chip).

[0033] The motor vehicle designed according to the invention is preferably a motor vehicle, in particular a passenger car or a truck, or a passenger car or a motorcycle.

[0034] As a further solution, the present invention also includes a computer-readable storage medium, which contains program code, and when the program code is executed by a computer or a computer network, the computer or computer network can be made to perform an embodiment of the method according to the present invention. The storage medium can be provided at least in part as a non-volatile data storage (e.g., flash memory and / or solid-state hard disk) and / or at least in part as a volatile data storage (e.g., random access memory). The storage medium can be arranged in a computer or a computer network. However, the storage medium can also be operated as a so-called application store server and / or cloud server on the Internet. For example, a computer or a computer network can provide a processor circuit such as at least one microprocessor. The program code can be provided in the form of binary code and / or assembly code and / or programming language source code (e.g., C language) and / or program script (e.g., Python).

[0035] The present invention also includes combinations of features of the described embodiments. Therefore, the present invention also includes implementations that each have a combination of features of more than one described embodiment, as long as these embodiments are not described as mutually deviating. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In the attached picture:

[0037] Figure 1 schematically illustrates a top view of an embodiment of a motor vehicle including an array sensor; and

[0038] Figure 2 A flow chart according to one specific embodiment of the method is shown.

[0039] In the figures, identical elements are provided with identical reference signs. DETAILED DESCRIPTION

[0040] Figure 1 The schematic diagram shows a top view of an embodiment of a motor vehicle 1. The motor vehicle 1 may include an assistance system 2 for at least partially assisting the operation of the motor vehicle 1. In addition, the motor vehicle 1 may also include an array sensor 3, in particular an array sensor 3 for the assistance system 2. The array sensor 3 may be configured as follows: Figure 1 The sensor device 4 and / or the communication device 5 are shown. The array sensor 3 may include at least one transmitting device 6, one receiving device 7 and one electronic computing device 8.

[0041] According to one embodiment of the method, the initial position estimates 9 of the sensor elements 10, 11, 12, 13 of the array sensor 3 are provided by an electronic computing device 8. Figure 1 As shown, the array sensor 3 may include a first sensor element 10, a second sensor element 11, a third sensor element 12 and a fourth sensor element 13. It is obvious to those skilled in the art that the array sensor 3 may also be composed of less than four sensor elements 10, 11, 12, 13 or more than four sensor elements 10, 11, 12, 13.

[0042] A sensor signal 14 is transmitted via a transmitting device 6 to a surrounding 15 of the array sensor 3 , and a reflected sensor signal 16 is received via a receiving device 7 , wherein the sensor signal 14 is reflected at at least one calibration target 17 , 16 , 18 , 19 in the surrounding 15 .

[0043] At least the direction of arrival of the reflected signal 16 is determined by the electronic computing device 8. The virtual position of each sensor element 10, 11, 12, 13 is determined by the electronic computing device 8 based on the minimum residual method according to the direction of arrival analysis. The current position 9 is estimated by the electronic computing device 8 based on the virtual position and the initial position estimate.

[0044] like Figure 1 As shown, at least four reflected signals 16 are received from at least four calibration targets 17 , 18 , 19 , 20 . Figure 1In particular, a first calibration target 17, a second calibration target 18, a third calibration target 19 and a fourth calibration target 20 are shown. Additionally, a distance to at least one calibration target 17, 18, 19, 20 is also provided and the virtual position is additionally determined as a function of the provided distance.

[0045] Furthermore, the channel imbalance of sensor elements 10 , 11 , 12 , 13 is additionally determined as a function of estimated current position 9 .

[0046] In another embodiment, the channel imbalance is determined using a singular value decomposition algorithm. Alternatively, the channel imbalance is determined using a high-order singular value decomposition algorithm.

[0047] Figure 2 A flow chart according to an embodiment of the method is shown. In particular, Figure 2 The first step S1 shows the normalization of the data. In the second step S2, the virtual position is estimated. In the third step S3, the actual position is estimated. In the fourth step S4, the estimated position is directed to the first step S1. From the third step S3, a fifth step S5 is performed, in which the data is compensated. In the sixth step S6, the channel imbalance is estimated.

[0048] In particular, Figure 2 It is shown that in a first step S1, an element-by-element division is performed using the data of the selected reference channel and the reference target, and the unnecessary terms are compensated using a "fuzzy factor". In a second step S2, a virtual position is estimated. Each unpacking hypothesis that may lead to the measured phase is analytically solved. The virtual position is estimated based on the minimum residual method. In a third step S3, the actual position is estimated. In particular, a pseudo-inversion and a fixed origin determined based on prior knowledge of the sensor element position are used. A fourth step S4 is optionally performed, in which the current estimate is referenced as prior knowledge. In a fifth step S5, the data is compensated. Using the estimated position and a possible acceptable target model, the phase effects caused by the geometric shape are eliminated from the data. In a sixth step S6, the channel imbalance is estimated. SVD or HOSVD is implemented

[0049] The following specifically introduces steps S1 to S6. According to the first step S1, a normalization process of data is provided.

[0050] The array includes rows Txm=1,...,N Tx and column Rxn=1,…,N Rx The corresponding position (3D Cartesian coordinate system) is Radar operates at a wavelength of λ

[0051] Range / Doppler quick find data model: for Cartesian target position p k ,

[0052]

[0053] where k = 0, ..., K targets and l = (m, n) = 0, ..., L virtual channels with positions The complex channel imbalance is γ l , and s k is the target amplitude. Note that the virtual position is defined here as the average of the Tx and Rx positions, not just the sum of the two. Also note that there is no additive Gaussian noise in the model.

[0054] A virtual "reference channel" (wlog,l=0) is selected, that is to say it will be used as the origin of the coordinate system v0=0 in the following.

[0055] Now, divide element-wise by the signal in the reference channel and compensate with the following (estimated) residual phase

[0056]

[0057] Where (ignore the exponents k, l, etc.)

[0058]

[0059] and Similarly, the antenna positions (and in a potential extended version of the method also the target positions) are replaced by their a priori values.

[0060] The target direction (DoA) is denoted as u. Here, a “fuzzy factor” f is introduced, which is usually set to

[0061]

[0062] in

[0063]

[0064] and is a priori or virtual position.

[0065] Regarding the choice of fine-tuning coefficients. There may be more than one good reason to set the error factor. Here, we minimize the (first-order) root mean square error of the ideal Δ and its actual available estimate under Gaussian perturbation, that is,

[0066]

[0067] in, in

[0068] Results and error variance for f has nothing to do with it, while RMSE changes linearly in proportion

[0069]

[0070] Here, is the position of v.

[0071] It should be noted that Δ can be estimated fairly accurately even with little prior knowledge of the antenna and target locations. For far-field targets, the RMSE approaches zero.

[0072] Another reasonable choice of fuzziness factor is

[0073]

[0074] This is based on

[0075]

[0076] , where O() here represents Landau's Big O notation (read: if the target is far enough from the array, the term can be ignored). Therefore, this choice aims to eliminate the lowest order term.

[0077] It can also be observed that the performance of both options is very similar, with the former possibly having a slight advantage over the latter in the near field.

[0078] To avoid numerical instabilities in virtual channels near the origin (except the reference channel), in one possible implementation, f is set to zero for these channels if f exceeds a certain maximum value. (Possible criteria: where σ pos Usually position errors) This is not usually necessary because the array is designed to be positioned so that it is spread across the entire aperture.

[0079] A “reference target” (wlog,k=0) is further selected and partitioned element-wise along the channel dimension to eliminate channel imbalance.

[0080]

[0081] Again, the last equation only holds approximately if we estimate Δ.

[0082] If the channel imbalance has been compensated, this step can be skipped, i.e., γ l = constant. All formulas are still valid after obvious modifications, such as f0 = 0, u0 = 0, K → K + 1.

[0083] In a second step S2, a virtual position is estimated. kl ,Normalization,Regulations

[0084]

[0085] The equation is as follows (since the problem is decoupled in the virtual position, the exponent v can be removed l ),

[0086]

[0087] In vector representation, substitute

[0088]

[0089]

[0090]

[0091]

[0092] (i.e. c→0 is the far-field condition with a fixed fine-tuning factor) and is calculated using the following formula:

[0093]

[0094] or

[0095]

[0096] in (which is about to be removed from ζ as a self-index). As shown in the following proof, this equation can be solved analytically:

[0097] Formula 1) Derivation of the analytical solution (can be skipped during the first reading).

[0098] This time According to the definition of orthogonal matrix, we get as well as

[0099] in get

[0100]

[0101] in

[0102] and

[0103] and

[0104] Convert to,

[0105]

[0106]

[0107] This gives the following equation

[0108]

[0109] Obviously, It must be a meaningful solution because it gives the same result as the far-field case.

[0110]

[0111] and Diverge.

[0112] In summary, the required position estimation formula is

[0113]

[0114] If K = 3, the corresponding ζ gives a valid solution (within the scope of prior knowledge)

[0115] If K>3, the residual

[0116]

[0117] It helps to eliminate "pseudo solutions": a large number of residuals come from the pseudo-inverse transformation (Equation 1), and the true solution is best without residuals.

[0118] How exactly the additional calibration targets increase the error-free units depends on the precise location and seems difficult to describe.

[0119] In any case, the grid of ζ must be retrieved. p The prior knowledge gives the concept of the grid center

[0120]

[0121] This method is "accurate" in the far field

[0122]

[0123] The ambiguity factor can be selected based on appropriate robustness criteria.

[0124] The method can be adapted to the spatial degrees of freedom. For example, if it is known that the antenna position can only vary in two dimensions, or if the target configuration is degenerate and a reasonable position estimate along one axis is not possible, the position variable can be modified to

[0125] t=+a0+e x t x +e y t y

[0126] r=-a0+e x r x +e y r y

[0127]

[0128] And (Equation 1) is transformed

[0129]

[0130]

[0131]

[0132] against The analysis results can be applied to general situations.

[0133] In a third step S3 the actual position is estimated. Once the estimated values ​​of all virtual positions are found It can be converted into The actual position estimate of .

[0134] Array(N Tx N Rx ×N Tx +N Rx )

[0135]

[0136] (in are equivalent arrays, and is an array of 1) to obtain the required solution

[0137]

[0138] Adding more rows to M is good for numerical stability and fixes another degree of freedom (note that the virtual positions do not change when the Rx and Tx arrays are moved relative to each other). They can be used, for example, to set the center of the array based on a priori knowledge.

[0139] At this stage, you can also try to identify virtual position estimates that fall into the ambiguity trap. If there is a Tx / Rx combination that is an outlier compared to the others, then

[0140]

[0141] , which are moderate values, probably larger than the wavelength λ. We can discard these rows and repeat the estimation of the actual position, which may give a better overall result.

[0142] Next, a fourth step S4 is optionally performed, wherein the current estimated value is used as a priori knowledge for guidance. New estimates of can be used as prior knowledge to repeat steps S1 to S3.

[0143] If the performance of the first run is already good, then this guidance has the potential to significantly improve accuracy. On the other hand, a first estimate that falls into the "ambiguity trap" will further consolidate in the wrong location, causing an overall decrease in the quality of estimates for other channels.

[0144] In a fifth step S5, the data is compensated, in particular by using the estimated position and a potential extended target model. While estimating the antenna position, a bistable range estimate for each target of known position is also used. Going back to the original sample, the phase induced by the geometry is compensated based on the antenna position estimate.

[0145]

[0146] The last equation only holds approximately. Even if it has not been used in the previous steps, at least in the last equation it is advisable to use an extended target model to make fine compensations for cylindrical shapes etc. (see Alhazen problem).

[0147] In the sixth step S6, the channel imbalance can be estimated by finding the rank-1 of the approximation using SVD or HOSVD. Now, the estimation of the channel imbalance becomes very easy:

[0148] For a "complete" model with no further structure

[0149]

[0150] Singular value decomposition (SVD) can be applied and extract the first column of U (corresponding to the largest singular value) as the estimate of γ (for a single target this is just the "normal" calibration procedure).

[0151] For a structured "Kronecker" model (suitable for MIMO radars)

[0152]

[0153] High-order SVD can be applied to refine the estimate.

[0154] List of Reference Numerals

[0155] 1 Motor Vehicle

[0156] 2 Auxiliary systems

[0157] 3 Array Sensors

[0158] 4 Sensor devices

[0159] 5. Communication equipment

[0160] 6 Transmitter

[0161] 7 Receiving equipment

[0162] 8 Electronic computing equipment

[0163] 9 Current location

[0164] 10 First sensor element

[0165] 11 Second sensor element

[0166] 12 Third sensor element

[0167] 13 Fourth sensor element

[0168] 14 Sensor signal

[0169] 15 Environment

[0170] 16 Reflection sensor signal

[0171] 17 First Calibration Target

[0172] 18 Second calibration target

[0173] 19 Third calibration target

[0174] 20 Fourth calibration target

[0175] Steps of the S1–S6 method

Claims

1. A method for estimating a current position (9) of a sensor element (10, 11, 12, 13) in an array sensor (3), comprising the steps of: - providing an initial position estimate of the sensor elements (10, 11, 12, 13) by means of an electronic computing device (8) of the array sensor (3); - transmitting a sensor signal (14) to the surroundings (15) of the array sensor (3) by means of a transmitting device (6) of the array sensor (3), and receiving a reflected sensor signal (16) by means of a receiving device (7) of the array sensor (3), wherein: The sensor signal (14) is reflected from at least one calibration target (17, 18, 19, 20) in the surrounding environment (15); - determining at least the direction of arrival of the reflected signal (16) by means of the electronic calculation device (8); - determining the virtual position of each sensor element (10, 11, 12, 13) by means of an electronic computing device (8) based on the method of minimum residuals according to the direction of arrival analysis; and - Estimating the current position (9) by means of an electronic computing device (8) based on the virtual position and the initial position estimate.

2. The method according to claim 1, characterized in that It is characterized in that At least four reflected signals (16) are received from at least four calibration targets (17, 18, 19, 20).

3. The method according to claim 1 or 2, characterized in that: It is characterized in that Additionally, a distance to at least one calibration target (17, 18, 19, 20) is provided, and additionally a virtual position is determined based on the provided distance.

4. The method according to any one of claims 1 to 3, It is characterized in that In addition, a channel imbalance of the sensor elements (10, 11, 12, 13) is determined as a function of the estimated current position (9).

5. The method according to claim 4, characterized in that It is characterized in that The channel imbalance is determined using a singular value decomposition algorithm.

6. The method according to claim 4, characterized in that It is characterized in that The channel imbalance is determined using a high-order singular value decomposition algorithm.

7. The method according to any one of claims 1 to 6, It is characterized in that The initial position is a nominal position.

8. The method according to any one of claims 1 to 7, It is characterized in that The estimated virtual position is guided as the virtual position.

9. The method according to any one of claims 1 to 8, It is characterized in that The received signal (16) is normalized before estimating the virtual position.

10. The method according to any one of claims 1 to 9, It is characterized in that The received signal (16) is filtered before estimating the virtual position (9) to exclude abnormal values ​​in the received signal (16).

11. The method according to any one of claims 1 to 10, It is characterized in that The received signal (16) is compensated for phase effects caused by the geometry of the received signal (16).

12. The method according to any one of claims 1 to 10, It is characterized in that The array sensor (3) is provided as a radar device and / or a communication device (5).

13. A computer program product comprising a computer program solution for executing the method according to any one of claims 1 to 12.

14. A computer-readable storage medium comprising at least the computer program product of claim 13.

15. An array sensor (3) designed as a sensor device (4) and / or a communication device (5) for a motor vehicle (1), comprising at least one generating device (6), a receiving device (7) and an electronic computing device (8), wherein: The array sensor (3) is designed to carry out the method according to any one of claims 1 to 12.

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