Method and apparatus for processing data, for example sensor data, and / or data derivable from sensor data

Abstract

Method, for example a computer-implemented method, for processing data, for example sensor data and / or data derivable from sensor data, comprising: providing first data associated with a first data source, providing second data associated with a second data source, fusing the first data with the second data using a first Kalman filter and a second Kalman filter.

Description

State of the art

[0001] The disclosure relates to a method for processing data, for example sensor data, and / or data derivable from sensor data.

[0002] The disclosure further relates to a device for processing data, for example sensor data, and / or data derivable from sensor data. Disclosure of the invention

[0003] Some examples relate to a method, such as a computer-implemented method, for processing data, such as sensor data, and / or data derivable from sensor data, comprising: providing first data associated with a first data source, providing second data associated with a second data source, and fusing the first data with the second data using a first Kalman filter and a second Kalman filter. In some examples, the principle according to the disclosure enables flexible and comparatively precise data processing, for example, for positioning (e.g., in the sense of determining a position) of mobile devices.

[0004] In some examples, providing the first data includes at least one of the following elements: a) determining the first data (for example, in or by means of an apparatus for carrying out the method according to the disclosure), or b) receiving the first data, for example, from at least one other device. In some examples, providing the second data includes at least one of the following elements: a) determining the second data (for example, also in or by means of the apparatus for carrying out the method according to the disclosure), or b) receiving the second data, for example, from at least one other device.

[0005] For example, the first data characterize at least a distance, for example, of a device for carrying out the method according to the disclosure, to at least one device having a reference position, for example, an "anchor device", for example, anchor, wherein, for example, determining the first data comprises at least one of the following elements: a) receiving signals, for example, reference signals, for example, for positioning, from several, for example, three, devices, each having a reference position, or b) determining at least one distance value based on the received signals, for example, based on determining an arrival time associated with the signals, for example, time of arrival (ToA).

[0006] For example, the received signals may be signals from a wireless, e.g. cellular, communication system, e.g. reference signals of the communication system, and / or signals usable for positioning or localization (e.g. positioning).

[0007] In some examples, the signals may be signals from a communication system of the following type: 5G, or B5G (beyond 5G), or 6G, or WiFi, or Ultra-WideBand (UWB), or another type where one or more signals are receivable, for example, for a time-of-flight measurement.

[0008] In some examples, it is provided that the second data comprises motion and / or environmental data of a device, for example for carrying out the method according to at least one of the preceding claims, wherein, for example, the determination of the second data comprises: determining the second data by means of at least one device for determining motion and / or environmental data, wherein, for example, the at least one device comprises at least one of the following elements: a) inertial sensor, for example inertial measurement unit, IMU, for example comprising at least one of the following elements: a1) accelerometer, or a2) gyroscope, or a3) magnetometer, or b) LIDAR.

[0009] In some examples, the procedure includes: determining distance values ​​filtered by the first Kalman filter based on at least one distance value and based on information associated with a constant velocity model; determining time-delayed first data and time-delayed second data, for example, using a delay device; and determining position information based on the time-delayed first data, the time-delayed second data, and the filtered distance values, using the second Kalman filter, where, for example, the delay device is configured to synchronize the time-delayed first data and the time-delayed second data. This allows for the determination of comparatively precise position information in some examples.

[0010] For example, the first Kalman filter and / or the second Kalman filter is of one of the following types: a) linear, or b) nonlinear, e.g. extended Kalman filter or unscented Kalman filter.

[0011] Further examples relate to a device for carrying out the method according to the disclosure, wherein, for example, the device is configured to carry out the method according to the disclosure.

[0012] Other examples relate to a product, for example a) terminal equipment, for example for a wireless communication system (e.g. mobile phone, e.g. user equipment (“UE”), or data modem), or b) vehicle, for example autonomous vehicle or driverless transport system, or c) robot, comprising a device according to the disclosure.

[0013] Other examples relate to a computer-readable storage medium comprising instructions which, when executed by a computer, cause it to perform the procedure according to the disclosure.

[0014] Other examples relate to a computer program, comprising instructions which, when the program is executed by a computer, cause it to perform the procedure according to the disclosure.

[0015] Further examples relate to a data carrier signal that transmits and / or characterizes the computer program according to the disclosure.

[0016] Further examples relate to the use of the method according to the disclosure and / or the device according to the disclosure and / or the product according to the disclosure and / or the computer-readable storage medium according to the disclosure and / or the computer program according to the disclosure and / or the data carrier signal according to the disclosure for at least one of the following elements: a) positioning or localization, for example in an indoor space, for example indoor positioning, or b) increasing precision, or c) combining data, for example sensor data, from different data sources, or d) providing a system for positioning, for example comparatively precise and / or robust, for example positioning, for example localization of an end device.

[0017] Further features, applications, and advantages will become apparent from the following description of examples illustrated in the figures of the drawing. All described or illustrated features, individually or in any combination, constitute the subject matter of the disclosure, irrespective of their aggregation in the claims or their cross-reference, and irrespective of their formulation or representation in the description or in the drawing.

[0018] The drawing shows: Fig. 1. A simplified flowchart (schematical). Fig. 2. A simplified block diagram (schematically). Fig. 3. A simplified flowchart (schematically). Fig. 4. A simplified flowchart (schematically). Fig. 5. A simplified block diagram (schematically). Fig. 6. A simplified flowchart (schematically). Fig. 7. A simplified block diagram (schematically). Fig. 8 schematic examples of uses.

[0019] Some examples, e.g. Fig. 1, Fig. 2, refer to a procedure, for example a computer-implemented procedure, for processing data D ( Fig. 2), for example, sensor data and / or data derivable from sensor data, comprising: providing 100 ( Fig. 1) first data SD-1, which is connected to a first data source DQ-1 ( Fig. 2) are associated, provide 102 second data SD-2, which are associated with a second data source DQ-2, fuse 104 the first data SD-1 with the second data SD-2 using a first Kalman filter KF-1 and a second Kalman filter KF-2, whereby output data OD can be obtained. In some examples, the principle according to the disclosure enables flexible and comparatively precise processing of data SD-1, SD-2, for example for positioning mobile equipment. In particular, the use of the two Kalman filters KF-1, KF-2 together with the first and second data SD-1, SD-2 can increase robustness compared to some conventional methods in some examples.

[0020] In some examples, the first Kalman filter KF-1 can be used to process the first data SD-1, and the second Kalman filter KF-2 can be used to further process the data processed by the first Kalman filter KF-1, for example by filtering it, for example using the second data SD-2.

[0021] In some examples, Fig. 2. At least one aspect of the method according to the disclosure can be carried out by a device 200. Examples of possible realizations or implementations of the device 200 are given below with reference to Fig. 7 described.

[0022] In some examples, the order of blocks 100, 102 is arbitrary; for example, at least partial temporal overlap is possible.

[0023] In some examples, the term "merging" is used in accordance with Block 104. Fig. 1. This also includes the merging of data that can be derived from or are derived from the first data SD-1 and / or the second data SD-2. In other words, aspects of merging according to the disclosure are not limited to the data SD-1 and SD-2 themselves.

[0024] In some examples, Fig. 1, the provision of 100 of the first data SD-1 comprises at least one of the following elements: a) Determining 100a of the first data SD-1 (for example, in or by the device 200), or b) Receiving 100b of the first data SD-1, for example, from at least one further device 20. In some examples, the device 20 may comprise at least the data source DQ-1, e.g., in the form of at least one sensor device.

[0025] In some examples, Fig. 1, the provision 102 of the second data SD-2 comprises at least one of the following elements: A) Determining 102a of the second data (for example, also in or by the device 200), or B) Receiving 102b of the second data SD-2, for example, from at least one further device 20'. In some examples, the device 20' may comprise at least the data source DQ-2, e.g., in the form of at least one sensor device.

[0026] For example, Fig. 2, the first data SD-1 characterize at least one distance, for example, of the device 200 for carrying out the method according to the disclosure, to at least one device having a reference position (not in Fig. 2 shown), for example “anchor”, e.g. “anchor device” (see below for Fig. 5), for example anchor, where for example determining 100a ( Fig. 1) the first data SD-1 contains at least one of the following elements, see. Fig. 3: a) Receiving 100a-1 signals SIG, for example reference signals, for example for positioning, from several, for example three, devices each having a reference position (see anchor devices ANC1, ANC2, ... according to Fig. 5), or b) Determine 100a-2 at least one distance value d1, d2, d3, ... based on the received signals SIG, for example, based on a determination of an arrival time associated with the signals SIG, for example, time of arrival (ToA) determination. In other words, in some examples, the first data SD-1 can be determined based on travel times of reference signals emitted from multiple reference positions and received, for example, by the device 200 or a receiver associated with the device 200 (see below, e.g., element E2a according to Fig. 5) be received.

[0027] For example, Fig. 2. The received signals can be SIG signals of a wireless, for example cellular, communication system, for example reference signals of the communication system, and / or signals usable for positioning or localization (e.g. positioning) (e.g. positioning reference signals).

[0028] In some examples, the signals SIG can be signals from a communication system of the following type: 5G, or B5G (beyond 5G), or 6G, or WiFi, or Ultra-Wideband (UWB), or another type where one or more signals can be received, for example, for time-of-flight measurement. Alternatively or additionally to radio signals as signals SIG, other types of signals, such as optical or acoustic signals, can also be used as signals SIG in other examples.

[0029] In some examples, Fig. 2, it is provided that the second data SD-2 contain movement and / or environmental data of a device 200, wherein, for example, the determination 102 ( Fig. 1) of the second data SD-2: Determine 102a-1 of the second data SD-2 using at least one device 30 ( Fig. 2) for determining motion and / or environmental data, wherein, for example, the at least one device 30 comprises at least one of the following elements: a) inertial sensor 30a, for example, an inertial measurement unit (IMU), for example, comprising at least one of the following elements: a1) accelerometer, or a2) gyroscope, or a3) magnetometer, or b) LIDAR 30b. In further examples, alternatively or additionally, other devices, e.g., sensor devices, are conceivable for providing the second data SD-2 in the form of motion and / or environmental data of the device 200.

[0030] In some examples, Fig. 2, at least one of the elements 20, 20', 30, 30a, 30b can be integrated into the device 200.

[0031] In some examples, Fig. 4, Fig. 5, it is provided that the procedure includes: Determining 110, using the first Kalman filter KF-1 ( Fig. 2, see also element E3d according to Fig. 5), filtered distance values ​​a6 based on at least one distance value d1, d2, d3, ... ( Fig. 3), see also arrow a1 according to Fig. 5, and based on information a5, which is associated with a model MOD ( Fig. 5) are associated with a constant speed, Determine 112 ( Fig. 4) time-delayed first data a4 and time-delayed second data a3, for example by means of a delay device E3b ( Fig. 5), Determine 114 ( Fig. 4), using the second Kalman filter KF-2 ( Fig. 2, see also element E3c according to Fig. 5), of position information a7 based on the time-delayed first data a4 and the time-delayed second data a3 and the filtered distance values ​​a6, wherein, for example, the delay device E3b ( Fig. 5) is designed to synchronize the time-delayed first data a4 and the time-delayed second data a3. This allows for the determination of comparatively precise position information a7 in some examples. In some examples, the delay for at least some of the data a1, a2 can be zero, at least temporarily. For example, in some examples it is conceivable to delay only one element of the data a1, a2, at least temporarily.

[0032] For example, the first Kalman filter KF-1, E3d and / or the second Kalman filter KF-2, E3c has one of the following types: a) linear, or b) nonlinear, e.g. extended Kalman filter or unscented Kalman filter.

[0033] Below are further examples and aspects according to the revelation with reference to Fig. 5, Fig. 6 described.

[0034] Fig. Figure 5 schematically shows a scenario in which several transmitters ANC1, ANC2, ANC3, ANC4, ANC5, ANC6 of reference signals (e.g., radio signals), each with a known position, e.g., "anchors," are present. A device E1, for example, an end device (e.g., user equipment, UE) for a mobile communication system, is configured to perform signal processing E2 in order to obtain the first data SD-1 ( Fig. 2), see also arrow a1 according to Fig. 5. For this purpose, the terminal device E1 has a receiver E2a for the reference signals, as well as a device E2b for ToA determination based on the received reference signals, which is configured, for example, to determine the respective distance values ​​d1, d2, d3, d4, d5, d6, see block E2c, based on the ToA determination E2b, where the respective distance values ​​d1, d2, ... each indicate, for example, a distance between the terminal device E1 and a respective transmitter ANC1, ANC2, ...

[0035] Element E3 according to Fig. 5 symbolizes aspects of a data fusion according to the disclosure, which e.g. the distance values ​​d1, d2, d3, d4, d5, d6, collectively with the arrow a1, corresponding e.g. to the first data SD-1 according to Fig. 1, Fig. 2, from the signal processing E2, and based on this, among other things, using the two Kalman filters E3c, E3d, determines position information a7, which characterizes, for example, the position of the terminal device E1, more accurately than is possible based solely on the information or data a1.

[0036] Element E3a according to Fig. 5 symbolizes as an example of a data source for the second data SD-2 ( Fig. 2), see arrow a2 according to Fig. 5, an IMU facility, which, for example, according to element 30 of Fig. 2 is trained, thus exhibiting, for example, aspects of inertial sensing.

[0037] Element E3b synchronizes the data a1, a2 supplied to it as input data, for example by using at least one corresponding delay, thereby obtaining the time-delayed first data a4 and the time-delayed second data a3.

[0038] Out of Fig. 5 shows that the data a1, a2 and / or derived data a3, a4 are fused in block E3 using the two Kalman filters E3c, E3d, which in some examples allows comparatively precise position information a7 to be obtained.

[0039] Further aspects and examples are described below, which – in the case of further examples – can each be combined individually or in any combination with at least one of the aspects and / or examples described above.

[0040] The principle according to the disclosure can be used, for example, to design a positioning system with at least one wireless signal receiver (e.g., receiver E2a according to Fig. 5. This can be achieved, for example, by providing a receiver component of a transceiver device for the terminal device E1) and an IMU sensor E3a, whose data a1, a2 are combined, for example, using the two Kalman filters E3c, E3d, in the sense of data fusion. In other words, in some examples, the two Kalman filters E3c, E3d can be used to calculate the position of the terminal device E1 (e.g., user equipment, e.g., "UE") based on the data a1, a2.

[0041] For example, it may be provided that one of the two Kalman filters, e.g. element E3d, filters the, e.g. comparatively coarse distance values ​​a1 and outputs the filtered distance estimates a6, e.g. associated with a current time “t”, e.g. using the MOD model for constant velocity, e.g. to the second Kalman filter E3c.

[0042] Furthermore, for example, it is provided that the second Kalman filter E3c is designed to estimate the position of the UE E1 at time “t-1” (i.e. at a time preceding time “t”), based on the input data a3, a4, a6.

[0043] As from Fig. As can be seen in Figure 5, in some examples the time-delayed data, e.g. measurements, a3 from the IMU sensor E3a are used as a further input (e.g. in addition to the arrow a4) for the second Kalman filter E3c, for example to check using the second Kalman filter E3c whether the distance values ​​a1 are within or outside a predefinable range.

[0044] In some examples, one or more of the following assumptions can be made: a) The arrival time (“ToA”) of an incoming signal, e.g., a radio signal (or optical or other signal) from the signal sources (e.g., Anker ANC1, ANC2, ...) can be determined, for example, measured, by the receiver, e.g., radio signal receiver, E2a in the terminal device E1. This ToA measurement can then be converted into a distance measurement, e.g., by multiplying it by the speed of light, e.g., according to distance (d) = ToA of the incoming signal (t) * speed of light (c) (Equation 1); b) At least three wireless signal sources (Anker ANC1, ANC2, ANC3, ...) are used; c) The positions of the anchors ANC1, ANC2, ANC3, ... are known to the terminal E1.

[0045] In some examples, aspects of revelation can be distinguished in several steps, e.g., an “initial processing”, e.g., associated with element E2 according to Fig. 5, and a data fusion (or “sensor fusion” or “sensor data fusion”), see element E3.

[0046] In some examples, the data a1 usable for sensor fusion E3 is prepared during initial processing (e.g., E2). For instance, at least one wireless signal source (e.g., anchor ANC1) initiates a positioning process by sending one or more reference signals, e.g., in a sequential order, e.g., one after the other. The terminal device E1, located at a previously unknown or not precisely known position, receives the reference signals from several anchors ANC1, ANC2, ... In some examples, the wireless signal receiver E2a of the terminal device E1 estimates the ToA of the reference signal received from each of the anchors ANC1, ANC2, ... and stores it, at least temporarily, e.g., as "ToA measurements."

[0047] The ToA measurements stored in the terminal E1 are, in some examples, converted into, for example, comparatively coarse (e.g., measured against a required accuracy for the position information a7), “distance measurements” d1, d2, d3, ... using (equation 1), see also the arrow a1 of Fig. 5, converted. The calculated, comparatively coarse, distance measurements a1 are used, for example, as input for the sensor fusion E3, as already described above.

[0048] In further examples, in sensor fusion E3, the data a1 from the initial processing E2 are combined with data a2 from the IMU sensor, see elements E3b, E3c, e.g., to calculate the position of the UE E1, see arrow a7.

[0049] In some examples, a data fusion, such as sensor fusion, E3 has the following three elements. The first element is the Kalman filter E3d, which can be, for example, a linear Kalman filter or a nonlinear Kalman filter (e.g., extended Kalman filter / unscented Kalman filter). The Kalman filter E3d receives the input a1 as, for example, relatively coarse distance values ​​or distance measurements from block E2 at a current time "t". The Kalman filter E3d then outputs the filtered distance measurements at time "t", as shown by arrow a6, by, for example, assuming the MOD model for a constant speed of the terminal device E1.

[0050] For example, the MOD model can be characterized by dfinal = dinitial + vinitial * t, where dfinal characterizes a resulting distance, dinitial characterizes an initial distance, and vinitial characterizes an initial velocity (which, for the MOD model, can be simplified to be constant). A corresponding state vector could, for example, have the form x t = [d1, d2, d3, d4, d5, d6] T exhibit, and a state transition matrix, for example, has the form of an identity matrix. Furthermore, a measurement matrix, which is used, for example, to generate a filtered current distance measurement a6 through the Kalman filter E3d, can have the form of an identity matrix.

[0051] The second element of sensor fusion E3 ( Fig. 5) In some examples, this is the delay device or delay block E3b. For example, the relatively coarse distance values ​​or distance measurements a1 at time "t" from the first processing step E2 are also used as input for the delay block E3b. The purpose of the delay block E3b in some examples is to delay at least some parts of the data a1, a2, for example, to store them for a specific duration. If, for example, the first processing step E2 provides data a1 every 3 seconds, the hold time for the delay block E3b is also 3 seconds in some examples.

[0052] As from Fig. As can be seen in Figure 5, data a2 from the IMU sensors, which consist, for example, of measured values ​​for accelerations and / or angular velocities of the terminal device E1, are also used as input for the delay block E3b. Therefore, the delay block E3b generates or provides "delayed" data a1, a2, i.e., delayed measurements or values ​​a4 of, for example, comparatively coarse distances, as well as the (possibly, but not necessarily, delayed) IMU data a3, which in some examples can be considered measurements at a previous time "t-1".

[0053] In further examples, the third element of the sensor fusion E3 is the additional Kalman filter E3c, which is used to output a position estimate of the UE E1, see arrow a7. The Kalman filter E3c receives inputs a3, a4 from the delay block E3b, e.g., relatively coarse distance values ​​and IMU measurements (e.g., accelerations) at time "t-1", and from the first Kalman filter E3d it receives filtered distance measurements a6 at time "t". In some examples, a state vector for the additional Kalman filter E3c has the form x t-1 = [x t-1 , y t-1 , vx t-1 , V yt-1 ] T , where x, y characterize, for example, a position of the UE E1, and where vx, vy characterize, for example, a velocity of the UE E1. In some examples, a state transition matrix "A" for the further Kalman filter E3c has the form A=[10t0010t00100001].

[0054] In further examples, the acceleration values ​​provided by the IMU sensor E3a (or the corresponding, possibly delayed, data a3) are used as input into the state model associated with the further Kalman filter E3c, which is represented by a control matrix “B”, e.g. according to: B=[0.5*t*t000.5*t*tt00t]

[0055] In some examples, a measurement vector has the form h(xt−1)=[(x−x1)2+(y−y1)2....(x−x6)2+(y−y6)2] where (x1, y1), ..., (x6, y6) are the positions of the anchors ANC1, ANC2, ..., ANC6 ( Fig. 5) characterize, and where (x, y) characterize the position of the terminal E1, whose comparatively precise position, see element a7, is to be determined according to some examples.

[0056] In some examples, the relatively coarse distance values ​​a1 or d1, d2, ... at time "t-1" are filtered together with the distance values ​​a6 at time "t" by the second Kalman filter E3c, for example, to obtain a "smoother" distance measurement at time "t-1". Furthermore, the IMU sensor E3a is used, for example, to check, based on the acceleration data a2 and a3 respectively, whether the distance measurements a4 and a6 generated at times "t" and "t-1" lie within a predefined range. If, for example, the distance measurements a4 and a6 do not fall within the predefined (e.g., the same) range, a measurement covariance is set to a relatively large value in some examples, which forces the Kalman filter E3d to recalculate the distance values ​​or measurements a6 at time "t".In some examples, the output of the Kalman filter E3c represents a relatively accurate position estimate a7 for the UE E1 at the time “t-1”.

[0057] The following are references to the simplified flowchart according to Fig. Six further aspects and examples are described according to the revelation.

[0058] Element E10 symbolizes the transmission of reference signals by the armatures ANC1, ANC2, ..., e.g., according to a predefined sequence. Element E11 symbolizes the reception of several of the aforementioned reference signals by the UE E1 ( Fig. 5) and determining, for example estimating, a respective arrival time, e.g. ToA, of the several received reference signals. Element E12 symbolizes that the ToA values ​​estimated according to block E11 are converted into, e.g., comparatively coarse, distance values ​​or measurements at time "t" using equation 1 (so). Element E13 symbolizes that the comparatively coarse distance measurements from element E12 are used as input for the first Kalman filter E3d ( Fig. 5) can be used, for example, to determine the filtered distances a6 at a current time "t", for example, to output them to the further Kalman filter E3c. Element E14 according to Fig. Element 6 symbolizes the use of delay block E3b to store at least some of the data a1, a2 for a predefined time, for example, "Δt seconds," thus delaying them. Element E15 symbolizes the use of the second Kalman filter E3c to estimate the position of the UE E1 with comparative accuracy (i.e., more accurately than characterized or determinable by the data a1). Here, for example, the current filtered distances at time "t," see arrow a6, and the previous distances at time "t-1," see arrow a4, are used as inputs. Furthermore, the IMU data a3 at time "t-1" is also used as input for the second Kalman filter E3c. Elements E16, E17 symbolize a check, for example, using the information a3 about accelerations from the IMU sensor E3a, to determine whether the current "t" and previous "t-1" distances are correct.Distances must lie within a predefined, for example, acceptable, range. If not, i.e., if the distances at the current time "t" are outside the predefined range, a measurement covariance matrix can be adjusted to a higher value according to element E18, and the distance values ​​can be recalculated on this basis, for example, by branching to element E13. Otherwise, the process branches from element E17 to element E19, according to which the process continues and, for example, the position of the UE is determined. Optionally, the process given as an example can be carried out according to... Fig. 6 can be executed again, see the arrow from element E19 to element E10.

[0059] In other examples, signals of other types, such as light or optical signals (or acoustic signals), can be used for anchors ANC1, ANC2, etc., instead of radio frequency or high frequency (RF)-based signals (e.g., reference signals of a wireless communication system, e.g., according to or based on 5G, B5G, 6G, WiFi, or UWB). These signals can be of a different frequency, such as light or optical signals (or acoustic signals). For example, light sources associated with anchors ANC1, ANC2, etc., such as LED lights, are provided, e.g., in an environment where the position of the terminal device E1 ( Fig. 5) is to be determined. For example, the environment could be an area within a structure such as a building (e.g., a house or hall, e.g., for a manufacturing facility), where, for example, the LED lights could be mounted on a roof or ceiling of the building. For example, the terminal device E1 could be equipped with at least one optical "receiver" or sensor to receive the optical reference signals and, based on this, determine the respective distance to each optical anchor (e.g., the LED lights mounted on the ceiling), analogous to the example above with reference to Fig. 5 described aspects.

[0060] In other examples, alternatively or additionally to IMU sensors, LIDAR can also be used to obtain the second data SD-2 ( Fig. 2), a2 ( Fig. 5) to provide.

[0061] Further examples, Fig. 7, refer to the device 200 for carrying out the method according to the disclosure, wherein, for example, the device 200 is configured to carry out the method according to the disclosure.

[0062] In some examples, Fig. 7, it is provided that the device 200 comprises: a computing device (“computer”) 202 having at least one computing core 202a, a storage device 204 associated with the computing device 202 for at least temporary storage of at least one of the following elements: a) data DAT (e.g. the first data SD-1 and / or the second data SD-2 and / or data derivable therefrom or generally at least some of the information or data a1, a2, a3, a4, a5, a6, a7 mentioned above as examples, and optionally information or data associated with the filters KF-1, KF-2 or E3c, E3d), b) computer program PRG, for example for carrying out the method according to the disclosure.

[0063] For further examples, Fig. 7, the memory device 204 includes volatile memory (e.g., RAM) 204a, and / or non-volatile (NVM) memory (e.g., Flash EEPROM) 204b, or a combination thereof or with other memory types not explicitly mentioned.

[0064] Further examples, Fig. 7, refer to a computer-readable storage medium SM, comprising instructions PRG which, when executed by a computer 202, cause it to execute the procedure according to the disclosure.

[0065] Further examples, Fig. 7, refer to a computer program PRG, comprising instructions which, when the program PRG is executed by a computer 202, cause it to perform the procedure according to the disclosure.

[0066] Further examples, Fig. References to reference 7 refer to a data carrier signal DCS, which characterizes and / or transmits the computer program PRG according to the disclosure. The data carrier signal DCS can be transmitted (e.g., sent and / or received) via an optional data interface 206 of the device 200. In further exemplary embodiments, the optional data interface 206 uses, for example, a wireless communication system.

[0067] In some examples, Fig. 7. The device 200 can also be designed, e.g., purely hardware-based, e.g., as a hardware circuit, or the functionality of the device 200 can be realized by means of a, e.g., purely hardware circuit.

[0068] Further examples, Fig. 7, refer to a product 1, for example a) terminal device (see also element E1 according to Fig. 5), for example, for a wireless communication system (e.g., mobile phone, user equipment (“UE”), or data modem), or b) vehicle, for example, autonomous vehicle or driverless transport system, or c) robot, comprising a device 200 according to the disclosure. This enables efficient and robust determination of the position of the product 1, for example, based on radio signals and / or optical signals, which can also be performed in real time, for example.

[0069] The principle according to the disclosure can be used, for example, for self-driving cars, robotic vacuum cleaners, robotic lawnmowers, and / or other mobile devices.

[0070] Further examples, Fig.8, refer to a use 300 of the method according to the disclosure and / or the device 200 according to the disclosure and / or the product 1 according to the disclosure and / or the computer-readable storage medium SM according to the disclosure and / or the computer program PRG according to the disclosure and / or the data carrier signal DCS according to the disclosure for at least one of the following elements: a) positioning 301 or localization, for example in an indoor space (e.g. factory hall, e.g. for driverless transport system), for example indoor positioning, or b) increasing 302 a precision, or c) combining 303 of data, for example sensor data, SD-1, SD-2 from different data sources DQ-1, DQ-2, or d) providing 304 a, for example comparatively precise and / or robust, system for positioning, for example localization of an end device E1.

Claims

[1] Method, for example a computer-implemented method, for processing data (D), for example sensor data and / or data derivable from sensor data, comprising: providing (100) first data (SD-1) associated with a first data source (DQ-1), providing (102) second data (SD-2) associated with a second data source (DQ-2), fusing (104) the first data (SD-1) with the second data (SD-2) using a first Kalman filter (KF-1) and a second Kalman filter (KF-2). [2] Method according to claim 1, wherein the provision (100) of the first data (SD-1) comprises at least one of the following elements: a) Determining (100a) the first data (SD-1), or b) Receiving (100b) the first data (SD-1), for example from at least one further device (20), and / or wherein the provision (102) of the second data (SD-2) comprises at least one of the following elements: a) Determining (102a) the second data (SD-2), or b) Receiving (102b) the second data (SD-2), for example from at least one further device (20, 20'). [3] Method according to at least one of the preceding claims, wherein the first data (SD-1) characterize at least one distance to at least one device (ANC1, ANC2, ANC3, ANC4, ANC5, ANC6) having a reference position, wherein, for example, determining (100a) the first data (SD-1) comprises at least one of the following elements: a) Receiving (100a-1) signals (SIG), for example reference signals, for example for positioning, from several, for example three, devices (ANC1, ANC2, ANC3, ANC4, ANC5, ANC6) each having a reference position, or b) Determining (100a-2) at least one distance value (d1, d2, d3, ...; a1) based on the received signals, for example based on a determination of an arrival time associated with the signals. [4] Method according to at least one of the preceding claims, wherein the second data (SD-2) comprise motion and / or environmental data of a device (200), for example for carrying out the method according to at least one of the preceding claims, wherein, for example, determining (102a) the second data (SD-1) comprises: determining (102a-1) the second data (SD-1) by means of at least one device (30) for determining motion and / or environmental data, wherein, for example, the at least one device (30) comprises at least one of the following elements: a) inertial sensor (30a), for example, an inertial measurement unit (IMU), for example, comprising at least one of the following elements: a1) accelerometer, or a2) gyroscope, or a3) magnetometer, or b) LIDAR (30b). [5] Method according to at least one of claims 3 to 4, comprising: Determine (110) distance values ​​(a6) filtered by the first Kalman filter (KF-1; E3d) based on the at least one distance value (d1, d2, d3, ...; a1) and information (a5) associated with a model (MOD) for a constant velocity, determine (112) time-delayed first data (a4) and time-delayed second data (a3), for example by means of a delay device (E3b), determine (114) position information (a7) based on the time-delayed first data (a4) and the time-delayed second data (a3) ​​and the filtered distance values ​​(a6) by means of the second Kalman filter (KF-2; E3c), wherein, for example, the delay device (E3b) is configured to synchronize the time-delayed first data (a4) and the time-delayed second data (a3) ​​with each other. [6] Method according to at least one of the preceding claims, wherein the first Kalman filter (KF-1; E3d) and / or the second Kalman filter (KF-2; E3c) is of one of the following types: a) linear, or b) nonlinear, e.g. extended Kalman filter or unscented Kalman filter. [7] Device (200) for carrying out the method according to at least one of the preceding claims, wherein, for example, the device (200) is configured to carry out the method according to at least one of the preceding claims. [8] Product (1; E1), for example a) terminal device (E1), for example for a wireless communication system, or b) vehicle, for example autonomous vehicle or driverless transport system, or c) robot, comprising a device (200) according to claim 7. [9] Computer-readable storage medium (SM) comprising instructions (PRG) which, when executed by a computer (202), cause it to execute the method according to at least one of claims 1 to 6. [10] Computer program (PRG) comprising instructions which, when the program (PRG) is executed by a computer (202), cause it to execute the method according to at least one of claims 1 to 6. [11] Data carrier signal (DCS) that transmits and / or characterizes the computer program (PRG) according to claim 10. [12] Use (300) of the method according to at least one of claims 1 to 6 and / or the device (200) according to claim 7 and / or the product (1; E1) according to claim 8 and / or the computer-readable storage medium (SM) according to claim 9 and / or the computer program (PRG) according to claim 10 and / or the data carrier signal (DCS) according to claim 11 for at least one of the following elements: a) positioning or localization (301), for example in an indoor space, for example indoor positioning, or b) increasing (302) precision, or c) combining (303) data, for example sensor data, from different data sources, or d) providing (304) a system for positioning, for example localization of an end device, for example, a system that is, for example, comparatively precise and / or robust.

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