Computer-implemented method for determining a user's position, computer-readable medium, and inertial navigation system

Abstract

The invention relates to a computer-implemented method for determining a user's position, wherein in a first step (S1) a first sensor signal (Se1), representing an acceleration (Acc) acting on the user's position, is detected by means of at least one inertial sensor (11), wherein in a second step (S2) at least one frequency-filtered sensor signal (Sef1, Sef2) is determined by means of bandpass filters of the first sensor signal (Se1), wherein in a third step (S3) a correction signal (Ko), representing a step speed (PS) of the user, is determined on the basis of the at least one frequency-filtered sensor signal (Sef1, Sef2);wherein in a fourth step (S4) navigation data (N) representing the user's position and / or speed are determined using Kalman filters based on the correction signal (Ko) and the first sensor signal (Se1), according to the step speed (PS) represented by the correction signal (Ko). The invention further relates to a computer-readable medium containing instructions for carrying out the method and an inertial navigation system (10) configured accordingly.

Description

[0001] The invention relates to a computer-implemented method for determining a user's position, a computer-readable medium with instructions for carrying out the method using an evaluation device, and an inertial navigation system configured for carrying out the method. State of the art

[0002] An inertial navigation system (INS) provides 3D orientation, velocity, and position information from a given initial state using inertial measurement units (IMUs), particularly those based on microelectromechanical systems (MEMS). Theoretically, a precise estimation of the user's orientation, velocity, and position could be achieved based on error-free sensor signals. However, inertial sensors, especially MEMS sensors, are susceptible to several sources of error, including offsets, scale factors, non-orthogonality of the inertial sensors used, and similar factors.

[0003] A well-known method for correcting navigation data provided by an inertial navigation system is to link the navigation data output to consistency with external data. For example, global navigation satellite systems (GNSS) can be used to correct the position and speed of an inertial navigation system. These approaches require a good GNSS signal, which may not be the case when navigating indoors, in cities, or in other locations.

[0004] One method for correcting navigation data from an inertial navigation system involves using pedometric data, such as the speed of users. For example, US 2014 / 0194138 A1 discloses how to determine the position of the inertial navigation system using an external pedometer. Disclosure of the invention

[0005] The object of the invention is to provide an improved method for determining a user's position using an inertial navigation system. In particular, the object of the invention is to provide an inertial navigation system that enables precise position determination even in areas without sufficient connection to a satellite navigation system, such as indoor areas of buildings or the like.

[0006] This problem is solved by a computer-implemented method for determining a user's position with the features of claim 1, a computer-readable medium with the features of claim 12, and an inertial navigation system with the features of claim 13. Advantageous embodiments of the invention are the subject of the dependent claims.

[0007] One aspect of the invention relates to the precise estimation of a user's walking speed, particularly that of a pedestrian, using step detection based on sensor signals from an inertial navigation system, wherein first sensor signals corresponding to an acceleration are detected by at least one inertial sensor at the user's position. An error parameter associated with the walking speed is provided to indicate the strength of the correction to the position determination of the inertial navigation system based on the solution of equations of motion.

[0008] The computer-implemented procedure for determining the user's position includes the following steps: - Capturing an initial sensor signal representing an acceleration acting at the user's position, using at least one of the inertial sensors: - Determining at least one frequency-filtered sensor signal using bandpass filters of the first sensor signal; - Determining a correction signal representing the user's walking speed based on at least one frequency-filtered sensor signal; - Determining navigation data representing the user's position and / or speed, depending on the correction signal and the first sensor signal, using Kalman filters according to the walking speed represented by the correction signal.

[0009] This approach is precise and robust, and in particular provides reliable step detection regardless of the positioning of the at least one inertial sensor on the user's body. This is achieved by considering a frequency spectrum or frequency band of the first sensor signal provided by the at least one inertial sensor that is relevant for locomotion activities. Any other type of movement, such as a hand swing, therefore does not impair the performance of the step detection. Precise step detection thus makes it possible to reliably determine a stepping speed corresponding to the user's movement.

[0010] A frequency band relevant for locomotion activities lies particularly between 1.3 Hz and 3.2 Hz. Depending on the type of locomotion, frequency bands between 1.6 Hz and 2.1 Hz and / or 2.3 Hz and 2.8 Hz, for example, have proven suitable for step detection.

[0011] The presented algorithm has very low power consumption because it uses only the acceleration vector, particularly the three-dimensional one, as an input signal and employs relatively simple signal processing techniques. In particular, the use of external pedometers is unnecessary, as the correction is based on the evaluation of the sensor signals provided by the inertial sensors. This also eliminates the need to qualify the inertial navigation system with pedometers of different designs. Furthermore, this results in reduced data transmission overhead, since sharing data via Bluetooth can lead to increased power consumption.

[0012] In a preferred embodiment, the user's locomotion activity is classified based on the sensor signals provided by the inertial sensors. This classification is preferably performed using a first and a second frequency-filtered sensor signal, which are determined by means of bandpass filters on the first sensor signal such that the first frequency-filtered sensor signal contains components of the first sensor signal from a first frequency band, whereas the second frequency-filtered sensor signal contains components of the first sensor signal from a second frequency band. To ensure the significance of the classification, it has proven advantageous if the first and second frequency bands do not overlap. In particular, the first frequency band can be defined by the frequency range between 1.6 Hz and 2.1 Hz, and the second frequency band by the frequency range between 2.The frequency can be specified as 3 Hz to 2.8 Hz.

[0013] Another aspect of the invention relates to a computer-readable medium with instructions for carrying out the method presented here for determining the user's position and a correspondingly designed inertial navigation system.

[0014] Further details and advantages of the invention are explained in more detail below with reference to the exemplary embodiments shown in the drawings. The drawings show: Fig. 1 a schematic representation of an inertial navigation system according to a possible embodiment of the invention; Fig. 2 a process engineering design of a step detection based on sensor signals from inertial sensors; Fig. 3 a frequency spectrum of a sensor signal that corresponds to a movement on foot; Fig. 4 a frequency spectrum of a sensor signal that corresponds to a movement of foot and hand; Fig. 5 the course of a walking speed determined from the sensor signals in comparison to a corresponding GNSS speed and average GNSS speed; Fig. 6 a process engineering design of a classification of locomotion activity based on the sensor signals of the inertial sensors; Fig. 7 the relative error of the position determination according to a process engineering embodiment of the invention compared to a conventional approach for various applications;

[0015] Identical or corresponding elements are marked with the same reference symbols in all figures.

[0016] It is understood that the diagrams contained herein illustrate the basic concept of the present invention. It is further understood that flowcharts, process diagrams, pseudocodes, and the like represent various processes that are essentially represented in a computer-readable medium and can be executed in a computer, a processor, a microprocessor, in particular an integrated circuit, or more generally in an evaluation unit 20 configured for data processing, regardless of whether such an evaluation unit 20 is shown or not.

[0017] Fig. Figure 1 illustrates the architecture of the proposed approach for optimizing the solution of an inertial navigation system 10 during walking. The presented method for determining the position of a user of the inertial navigation system 10 includes step detection, estimation of step speed, and updating of the step speed measurement using pedometric data acquired by at least one inertial sensor 11 of the inertial navigation system 10 itself. In this sense, the pedometric data used here can also be considered intrinsic data of the inertial navigation system 10.

[0018] The inertial navigation system 10 comprises, in addition to at least one inertial sensor 11 for detecting the acceleration Acc acting on the user's position, an evaluation unit 20, which includes a first module 21 for step detection, a second module 22 for estimating a step velocity SL assigned to the user, a third module 23 for updating the step velocity measurement, and a fourth module 24 for generating navigation data N reflecting the user's position and / or velocity. In some configurations, at least one control signal is generated depending on the navigation data N. In some configurations, the navigation data N can be output via an interface not shown.

[0019] In a first step S1, a first sensor signal Se1, which represents the acceleration Acc acting at the user's position, is detected using inertial sensors 11.

[0020] In a second step S1, at least one frequency-filtered sensor signal Sef1, Sef2 is determined using bandpass filters of the first sensor signal Se1.

[0021] In a third step S3, a correction signal Ko, which represents a stepping speed of the user, is determined based on at least one frequency-filtered sensor signal Sef1, Sef2.

[0022] In a fourth step S4, the navigation data N, representing the user's position and / or speed, is determined using Kalman filters based on the correction signal Ko and the first sensor signal Se1, according to the stepping speed represented by the correction signal Ko. Optionally, in the fourth step S4, additional second sensor signals Sef2, representing rotation rates Ang, are also considered when determining the navigation data N.

[0023] The approach proposed here for step detection is schematically shown in Fig. 2 shown. The concept of step detection is based on filtering out frequencies that do not correspond to the user's stepping movements, such as hand movements.

[0024] For step detection, the acceleration magnitude (Acc) is first determined as the initial sensor signal (Se1) in an intermediate step (ZS1). This signal is then filtered in a second intermediate step (ZS2) using a bandpass filter, specifically a second-order Butterworth bandpass filter, to generate a first and a second frequency-filtered sensor signal (Sef1, Sef2). The first frequency-filtered sensor signal (Sef1) contains the signal components of the first sensor signal (Se1) in a frequency band between 1.6 Hz and 2.1 Hz.

[0025] The first frequency band can be associated with a walking motion of the user. The second frequency-filtered sensor signal, Sef2, contains the signal components of the first sensor signal, Se1, in a second frequency band between 2.3 Hz and 2.8 Hz. This second frequency band can be associated, in particular, with a running motion such as jogging. The frequency bands relevant for the various locomotion activities were determined using a proprietary dataset comprising over 500 case studies with approximately 20 different users. The inertial navigation system 10, which includes the inertial sensor 11, was worn on the wrist, on the arm, in a trouser pocket, or in the hand.

[0026] Fig. Figure 3 shows the frequency spectrum of a first sensor signal Se1, which corresponds to the acceleration Acc standard when the inertial sensor 11 is held in the hand and no other movement is performed, apart from walking. In this case, the frequency spectrum shows a single frequency peak. On the other hand, the frequency spectrum shows multiple frequency peaks when the user moves their hand while walking or jogging, as is the case when walking or jogging. Fig. 4 is visible.

[0027] Identifying steps based on the unfiltered first sensor signal Se1 is generally not possible, as the corresponding signatures in the unfiltered signal cannot be distinguished from other movement patterns, especially those caused by hand movements. However, by using bandpass filters, the frequency-filtered sensor signal Sef1, Sef2 can be made to essentially resemble a sine wave, the amplitude and frequency of which are determined by the user's steps. Therefore, reliable step detection can be implemented based on the frequency-filtered sensor signal Sef1, Sef2.

[0028] Step detection (see below) Fig. 2) can be carried out in the third intermediate step ZS3 by means of edge detection of the filtered sensor signal Sef1, Sef2, in particular by means of positive edge detection of the filtered sensor signal Sef1, Sef2, whereby in particular a control signal corresponding to the step detection can be generated.

[0029] In a fourth intermediate step ZS4, step frequencies SF are calculated according to the filtered sensor signals Sef1, Sef2. Such a step frequency SF can be determined, in particular, from the filtered sensor signals Sef1, Sef2 as the inverse of the time difference between two detected steps. The step frequency SF represents a first correction parameter KP1, with the correction signal Ko being determined as a function of the first correction parameter KP1. Step detection is preferably performed for the first and the second filtered sensor signals Sef1, Sef2 in order to monitor the signals provided by the inertial sensors 11 with respect to step frequencies SF, which correspond to the movement types walking and running (jogging).

[0030] In order to correctly select the step frequency SF and a step length SL depending on the application case at hand, a classification of the user's locomotion activity is carried out in a fifth intermediate step ZS5.

[0031] The logic of this classification is schematically presented in Fig. Figure 6 shows how to classify locomotion activity, determining classification results E1, E2, and E3. In the first classification step (KS1), if a step is detected, the range or amplitude of the first and second filtered sensor signals Sef1 and Sef2 is calculated. Then, in the second classification step (KS2), the squared deviation of these amplitudes is calculated. A third classification step (KS3) compares this squared deviation to a threshold value to determine whether locomotion activity is present. If the user is neither walking nor running / jogging, the corresponding amplitudes of the first and second filtered sensor signals Sef1 and Sef2 are small. This means that the squared deviation of these amplitudes is below a predefined or predefinable threshold value.In this way, it can be determined that the user is not performing any relevant walking activity. This corresponds to the first classification result, E1. On the other hand, the squared deviation increases as soon as the user starts walking or running. A relevant walking activity can therefore be identified by the squared deviation exceeding the predefined or predefinable threshold.

[0032] The classification results E2 and E3, which relate to the type of movement (such as walking or running), are assigned in a fourth classification step, KS4, based on the frequency-filtered sensor signal Sef1 and Sef2 with the largest amplitude. If the amplitude of the first frequency-filtered sensor signal Sef1 is greater than the amplitude of the second frequency-filtered sensor signal Sef2, the second classification result E2, corresponding to a walking movement, is assigned to the movement activity. If the amplitude of the first frequency-filtered sensor signal Sef1 is less than the amplitude of the second frequency-filtered sensor signal Sef2, the third classification result E3, corresponding to a running movement, is assigned to the movement activity.

[0033] Based on the assigned locomotion activity or the classification result E2, E3, the correct step frequency SF is then determined in a sixth intermediate step ZS6 (see below). Fig. 2) and step length SL is selected in a seventh intermediate step ZS7. The step length SL represents a second correction parameter KP2, whereby the correction signal Ko is determined as a function of the second correction parameter KP2. According to possible embodiments, the step length SL is assumed to be constant for a given mode of locomotion. For example, the step length SL corresponds to a predetermined value of 0.85 m when walking and a predetermined value of 1.10 m when jogging.

[0034] Optionally, the step length SL can be determined or optimized using an external GNSS signal from a satellite-based navigation system 30, wherein the external GNSS signal is transmitted via an interface 31 of the inertial navigation system 10. In this case, the step length SL can be determined by the following relationship: stepLength=gnssSpeed / stepFrequency

[0035] Here, “stepLength” refers to the step length SL, “gnssSpeed” to the speed determined by the satellite-based navigation system 30, and “stepFrequency” to the step frequency SL determined on the basis of the first or second frequency-filtered sensor signal Sef1, Sef2.

[0036] The walking speed is determined by the relationship pedestrianSpeed=stepFrequency*stepLength determined, where "pedestrianSpeed" denotes the walking speed PS and "stepLength" denotes the step length SL.

[0037] In an alternative version, this linear relationship can be expressed by a nonlinear function of the form dependent on the step frequency SF and the step length SL. pedestrianSpeed=f(stepFrequency,stepLength) be replaced.

[0038] Fig. Figure 5 shows an example of the walking speed (PS) estimated using this approach. For comparison, in Fig. Figure 5 shows the instantaneous GNSS velocity (VG) and the mean GNSS velocity (mVG) recorded by the satellite-based navigation system 30. It is clearly evident that the walking speed (PS) determined from the frequency-filtered sensor signal Sef1, Sef2 essentially corresponds to the mean GNSS velocity.

[0039] The stepping rate error PSE is given by the relationship pedestrianSpeedError=pedestrianSpeedStdev+errorFactor The walking speed error (PSE) is determined by `pedestrianSpeedError`. The walking speed error (PSE) depends primarily on the standard deviation of the walking speed (PS), which is reflected by the factor `pedestrianSpeedStdev`. An additional error parameter, `errorFactor`, was introduced to ensure that the walking speed error (PSE) is always greater than zero. For example, if the walking speed (PS) is nearly constant, the standard deviation is very small, and the walking speed error (PSE) can be close to zero.

[0040] The speed determined by the fourth module 24 of the inertial navigation system 10 is given by vk=vk−1+acclin∗dt, where v k the three-dimensional velocity vector of the k-th iteration, v k-1 the three-dimensional velocity vector of the preceding, (k-1)th iteration, acc lindenotes the linear acceleration and dt the time difference between two integration steps.

[0041] The approach presented here focuses on correcting the magnitude or norm of the velocity in the horizontal plane. The norm of the velocity in the horizontal plane is given by vnk=vxk2+vyk2, where with v xk the velocity component in the X direction and v yk denotes the velocity component in the Y-direction. The corrected norm of the velocity v̂ nk is calculated as follows: v^nk=vnk−1+K(pk−vnpred)

[0042] Here, p is used. k the stepping speed PS and v estimated in the k-th iteration in particular according to equation (2) or (3) npred The predicted norm of the velocity is denoted. The quantity (p) k - v npred)is called the measurement residual. K describes the Kalman gain, which is given by K=PkHT(HPkHT+R)−1, where P denotes the state covariance matrix, H the state-to-measurement matrix, and R the measurement covariance matrix. The measurement covariance matrix R is calculated here using equation (4) for the stepping velocity error PSE. As R approaches zero, the Kalman gain K gives greater weight to the measurement residual, resulting in a stronger correction of the velocity norm.

[0043] This measurement update corrects the standard of speed v nk in the horizontal plane, which is used to measure the velocity v k to correct the user. Correcting the speed v kThe user's input results in the position information exhibiting fewer cumulative errors after integration. If sensor errors, preferably all sensor errors, particularly those relating to differing orientations of the inertial sensors 11, are included in the state or as state variables of the probabilistic filter, the correction given in equation (7) also has an effect on these sensor errors, leading to an improved estimation of the sensor orientations and sensor errors.

[0044] Fig. Figure 7 shows a comparison of the positional errors contained in the navigation data N. The cumulative, relative positional errors after 50, 75, and 95 steps are shown for two different cases. One case depicts the navigation data N being determined without boundary conditions, i.e., without correction based on the walking speed PS.

[0045] In contrast, the case is shown (hatched) in which the navigation data N is determined consistently with the walking speed PS as a boundary condition. The relative position errors are given for both walking, corresponding to the second classification result E2, and running (jogging), corresponding to the third classification result E3. When the walking speed PS is considered as a boundary condition for position determination according to the method proposed here, the relative position errors are consistently smaller, suggesting increased navigation accuracy. 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] US 2014 / 0194138 A1

[0004]

Claims

[1] Computer-implemented method for determining a user's position, wherein in a first step (S1) a first sensor signal (Se1), which represents an acceleration (Acc) acting at the position of the user, is detected by means of at least one inertial sensor (11), wherein in a second step (S2) at least one frequency-filtered sensor signal (Sef1, Sef2) is determined using bandpass filters of the first sensor signal (Se1), wherein in a third step (S3) a correction signal (Ko), which represents a step speed (PS) of the user, is determined on the basis of at least one frequency-filtered sensor signal (Sef1, Sef2); in a fourth step (S4) navigation data (N) representing the position and / or speed of the user are determined using Kalman filters depending on the correction signal (Ko) and the first sensor signal (Se1) according to the step speed (PS) represented by the correction signal (Ko). [2] Method according to claim 1, wherein the step speed (PS) during the determination of the navigation data (N) in the fourth step is a boundary condition for the speed of the user in a horizontal plane. [3] Method according to claim 1 or 2, wherein in the second step (S2) the first sensor signal (Se1) is filtered in a frequency band between 1.3 Hz and 3.2 Hz, in particular between 1.6 Hz to 2.1 Hz and / or 2.3 Hz to 2.8 Hz. [4] Method according to one of the preceding claims2, wherein in the second step (S2) a first and a second frequency-filtered sensor signal (Sef1, Sef2) is determined by means of bandpass filters of the first sensor signal, wherein the first frequency-filtered sensor signal (Sef1) contains components of the first sensor signal (Se1) of a first frequency band and the second frequency-filtered sensor signal (Sef2) contains components of the first sensor signal (Se1) of a second frequency band, wherein the first and second frequency bands do not overlap. [5] Method according to one of the preceding claims, wherein in the third step (S3) step detection is carried out by means of edge detection of the at least one frequency-filtered sensor signal (Sef1, Sef2), wherein in the fourth step (S4) the navigation data (N) are determined as a function of the correction signal (Ko) when step detection has been carried out. [6] Method according to one of the preceding claims, wherein in the third step S3 a first correction parameter (KP1) representing a step frequency (SF) is determined on the basis of the at least one frequency-filtered sensor signal (Sef1, Sef2), wherein the correction signal (Ko) is determined as a function of the first correction parameter (KP2). [7] Method according to claim 6, wherein step detection is performed on the basis of the first and the second frequency-filtered sensor signal (Sef1, Sef2), wherein the first and the second frequency-filtered sensor signal (Sef1, Sef2) are classified with respect to a present locomotion activity and the first correction parameter (KP1) representing the step frequency (SF) is determined according to a classification result (E1, E2, E3) of the locomotion activity. [8] Method according to claim 7, wherein the classification of the present locomotion activity is carried out by comparing a quantity derived from the first frequency-filtered sensor signal (Sef1) and the second frequency-filtered sensor signal (Sef2), in particular square deviation, with a threshold value. [9] Method according to any one of claims 6 to 8, wherein a second correction parameter (KP2) representing a step length (SL) is specified or determined as a function of an external GNSS signal (GNSS) of a satellite-based navigation system (30) and the correction signal (Ko) is determined as a function of the first and second correction parameters, wherein the step speed (PS) is determined as a function of the first and second correction parameters. [10] Method according to any one of claims 6 to 9, wherein the correction signal is determined iteratively taking into account the first and / or the second correction parameter determined in the previous iteration step and / or a quantity derived as a function of the first and second correction parameter, in particular step speed (PS). [11] Method according to any one of claims 6 to 10, wherein a step speed error (PSE) associated with the correction signal (Ko), which quantifies a sensor error of the at least one inertial sensor (11), is determined by comparing the step speed (PS) with a speed determined by integrating the first sensor signal (Sef1, Sef2). [12] Method according to any one of claims 2 to 11, wherein in the fourth step (S4) the norm of speed in the horizontal plane is corrected according to the first and / or the second correction parameter and / or the quantity derived as a function of the first and second correction parameter, in particular step speed (PS), wherein the norm of speed in the horizontal plane thus determined is used to correct the navigation data (N) representing the position and / or speed of the user, which are determined by means of caiman filters. [13] Method according to one of the preceding claims, wherein in the fourth step sensor errors, in particular with respect to differing orientations of the inertial sensors (11), are taken into account as state variables of the Kalman filter and are determined as a function of the correction signal (Ko) and the first sensor signal (Se1) using Kalman filters according to the step velocity (PS) represented by the correction signal (Ko). [14] Computer-readable medium containing instructions which, when executed by an evaluation device (20), cause it to execute the computer-implemented method according to one of the preceding claims. [15] Inertial navigation system (10) for determining a user's position according to a method of claims 1 to 13 comprising at least one inertial sensor (11) for detecting the acceleration (Acc) acting on the user's position and an evaluation unit (20) configured to determine the frequency-filtered sensor signal (Sef1, Sef2) by means of bandpass filters of the first sensor signal (Se1), to determine the correction signal (Ko) based on the at least one frequency-filtered sensor signal (Sef1, Sef2), and to determine the navigation data (N) representing the user's position as a function of the correction signal (Ko) and the first sensor signal (Se1) by means of Kalman filters. [16] Inertial navigation system according to claim 15, comprising an external interface (31) for transmitting the external GNSS signal (GNSS) of the satellite-based navigation system (20). [17] Inertial navigation system according to claim 15 or 16, wherein the navigation data (N) are additionally determined as a function of a second sensor signal (Se1) representing rotation rates using Kalman filters.

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