FI123761B - Method for determining a measurable object size and corresponding system - Google Patents

Abstract

The invention relates to a method for determining a target variable, such as speed or altitude, to be measured, in a mobile device (such as a wrist-mounted computer or mobile telephone), and a corresponding mobile system. In the method, a first physical variable is measured with the aid of a first sensor and a second physical variable with the aid of a second sensor. The second physical variable is typically different to the first physical variable, or at least is measured using a different technique. With the aid of the measurements, the value of the target variable is calculated with the aid of the measurement of the first and second physical variables, in such a way that an estimate for the target variable is determined with the aid of at least the measurement of the first physical variable (step 32), at least a first error estimate is determined (33a, 33b), which depicts the accuracy of the measurement of the first physical variable, and the estimate of the target variable is filtered (step 35) at a strength that depends on both the first error estimate and the measurement of the said second physical variable.

Description

A method for determining a measurable target variable and a corresponding system

FIELD OF THE INVENTION The invention relates to sensor fusion technology for mobile devices, more particularly to the processing of information provided by multiple sensors. In particular, the invention relates to improving the accuracy of a target quantity measured by a mobile device or system. The sensors may be, for example, a GPS sensor or a pressure sensor as well as an acceleration sensor and the speed or altitude of the target variable measured with them, respectively. The mobile device may be, for example, a wristop computer, a mobile phone, other portable device or sensor unit, or any functional combination thereof.

Prior art

GPS on the wrist or elsewhere in the body has a lot of noise but has a very small bias error, or systematic error. That is, in the frequency domain, represented by GPS

the speed accuracy is good near DC and deteriorates rapidly with increasing frequency as shown in Fig. 1 by curve 1. Measurement accuracy can be improved by traditional signal filtering methods or GPS doppler measurement. Still, at a typical 1 Hz measurement frequency and when a person is walking, the noise from a GPS-based speed measurement 20 can be up to 20-30% of the signal, co 5 On the other hand, the velocity sensor from the wrist or other body

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ivL contains less noise so that its accuracy with frequency increases up to a certain limit cp (Y). However, there may be a large bias error in the speed so measured. This o x means that the best accuracy of the acceleration estimated velocity is worse than

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25 With GPS. The accuracy of the speed measured by the accelerometer in the frequency domain is typically o as shown in curve 2 of Fig. 1, m o However, in practice, the objective would be to obtain the velocity accuracy of curve 3 of Fig. 1 in the frequency domain. The aim is also to provide a measurement 2 that responds reasonably well to changes in the Business Area, but poorly to the sources of error associated with the measurement event itself.

Similar problems arise when determining GPS-based altitude and vertical velocity 5. Further, a somewhat similar problem also relates to the determination of altitude data by means of a pressure transducer, although in this case the error is caused by slow (low frequency) air pressure variations.

Summary of the Invention

It is an object of the invention to provide a novel method for determining a measurable target variable 10 in a mobile device and a corresponding system. It is a particular object of the invention to improve the accuracy of vertical and / or horizontal velocity and / or altitude calculated using position sensor data or altitude or vertical velocity calculated using pressure data under varying motion and environmental conditions.

The invention utilizes sensor fusion in a novel way, that is, more precisely, the information provided by at least two different sensors measuring the same or different physical quantities is utilized in the calculation of the desired quantity.

The method and system of the invention are more precisely defined in the independent claims.

According to one embodiment, the method measures first and second physical quantities by means of first and second sensors, respectively. The value of the target variable co 5 is calculated by means of the first and second measurements, such that c i j i 1 ^ cp - an estimate of the target variable is determined by at least the first measurement of the physical quantity co 0,

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- determining at least a first error estimate describing the error of the first physical measurement of 0 01 25, m ° - determining an error estimate of the calculation model that estimates how well the calculation model corresponds to the actual situation, at least by a second physical measurement; both the measurement error and the calculation model error mentioned.

The second physical quantity may be different from the first physical quantity, or at least measured by a different technique. The difference in technology can be, for example, a different sensor placement or 5 different measurement patterns that convert the measurement to a target quantity. Thus, the first and second sensors are typically based on different operating principles, even when they measure the same physical magnitude. For example, horizontal velocity (propagation velocity) can be measured by a satellite position sensor and by an accelerometer. Similarly, altitude or ascent rate (vertical velocity) can be measured by a satellite position sensor, 10 accelerometer, and a pressure sensor. Preferably, the first and second sensors are based on measurement techniques which have a substantially different error profile as a function of the measurement frequency. However, the data provided by both sensors can be used to derive, either directly or through a mathematical model and / or preliminary data, an estimate of the target variable or its change.

According to one central embodiment, the method measures velocity by means of a satellite position sensor (such as a GPS sensor) and acceleration by means of an accelerometer. The final velocity value to be reported to the user is calculated from the velocity and acceleration measurements by: - determining an estimate of velocity by satellite positioning and / or 20 acceleration measurements, - determining a first error estimate describing the error in ig error, x o-25 - determining the first calculation error of the calculation model by satellite positioning measurement, 2 - determining the second calculation error of the calculation model by means of acceleration measurement, and O)! £ - filtering the velocity estimate with intensity depending on said measurement error estimate and said calculation.

According to a preferred embodiment, the velocity estimate is determined, with at least 30 predetermined measurement quality criteria, by both satellite positioning measurement 4 and acceleration measurement, with weighting as desired. Error values can also be determined which illustrate the magnitude of errors in the speed measurements. These error estimates can still be used to determine the weights for calculating the velocity estimate. Further, another error estimate can be determined which describes the magnitude of the error in the calculation model 5. The calculation model error and the rate estimate error can still be used to determine the filter estimate of the rate estimate.

The speed can be either horizontal or vertical speed, or the sum of these speeds.

The method according to the invention may be performed in whole or in part in a wristop computer. If performed only partially on a wristop computer, some of it may be performed on a remote sensor, which may be located on a separate device unit or may belong to another device unit such as a mobile phone. The part may be the measurement and / or calculation of the first and / or second physical quantities, i.e., for example, in the case of the speed measurement described above, the measurement and / or calculation of speed and / or acceleration.

Particularly preferred is an embodiment in which the acceleration measurement is performed by means of an accelerometer in the wrist unit 15, since the acceleration measurement from the wrist is very reliable due to the natural movement of the hand.

The method according to the invention can also be performed in whole or in part in a mobile phone. If performed only partially on a mobile phone, some of it may be performed on a remote sensor, which may be located on a separate device unit or be part of another 20 device unit such as a wristop computer. Such a part may be the measurement and / or calculation of the first and / or second physical quantities, e.g., in the case of the velocity measurement co described above, the measurement and / or calculation of velocity and / or acceleration.

In general, it is noted that the method of the invention may also be performed on a fully cp c or partially screened portable device adapted to display a calculated value of o x 25 to a user.

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On the other hand, the method according to the invention can also be performed in whole or in part on a δ displayless handheld device such as a satellite positioning module which is wirelessly connected to a display handheld device such as a wrist unit or a mobile phone. The advantage of this embodiment is the reduction of the power consumption of the display portable device.

The system according to the invention comprises corresponding device units, possibly necessary wireless communication means between them, and is adapted to implement the method according to the invention. The various exemplary alternatives and their advantages will be described in more detail later.

The invention provides considerable advantages. As the target variable changes rapidly, this can be detected and the filtering level used to calculate the target variable value can be further changed. Similarly, if the target variable estimates provided by the sensors differ significantly from one another, it can be concluded that the second measurement must have some source of error that can be explained. This is the case, for example, when placing a GPS sensor under a large bridge without a GPS signal. If the accelerometer still indicates that the device is in motion, the velocity calculated from the accelerometer can be weighted to determine the final velocity.

According to one preferred embodiment, said estimate for the target quantity is computed by measuring the first and second physical quantities, and further determining a second error estimate describing the accuracy of the second physical quantity measurement. Finally, the value of the target variable is calculated by filtering the estimate of the target variable with a intensity that depends on both the first and second error estimates. In this way, the accuracy of both the first and second sensors can be considered before the final result is presented to the user of the device.

According to one embodiment, in the computation of the target variable, the filtering intensity δ ^ is increased as the accuracy of the first and / or second physical large measurements decreases, and vice versa. Thus, the variations in the target variable due to a measurement error are not perceptibly transmitted to the user of the device, but when the measurement error is small,

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However, the time resolution remains good, o 5) According to one preferred embodiment, during the measurement, the rate of change of the target variable m 2 is detected, either on the basis of its estimate or final value or directly from the measurement data of the first or second sensor. If it is found that the rate of change of the target variable 30 exceeds a predetermined limit or increases, the intensity of the filtration 6 in the calculation of the target variable is reduced. In this case, the method reacts faster to changing conditions and provides the user with more real-time information.

As noted above, one important practical embodiment of the invention provides a solution wherein the target variable is a speed and the first sensor is a satellite positioning sensor, such as a GPS receiver. In this case, the first physical quantity is the velocity or the absolute / relative position. If location is measured, speed can be calculated from location and time information. If, on the other hand, GPS speed is directly measured using, for example, the Doppler phenomenon, the speed is obtained directly from GPS data. A combination of these measuring methods is also possible.

As a working embodiment of the invention, there is mentioned a solution wherein the target variable is height or vertical velocity ("rising velocity"), the first sensor is an air pressure sensor and the first physical quantity is atmospheric pressure, respectively. Altitude and vertical velocity, or at least an estimate for these, can be calculated by atmospheric pressure, when the atmospheric pressure at sea level or other reference plane is known.

In all of the above embodiments, the second sensor is preferably an acceleration sensor and the second physical quantity is an acceleration. Accelerometer measures the movement of the device and can be used to further calculate an estimate of speed. An acceleration measurement can also be determined, if desired, to describe a measurement error that can be utilized in addition to, or instead of, Motion alone to filter the estimate of the 20 target variables calculated from the first sensor data at the desired intensity in accordance with the invention.

One possible embodiment of the invention can also be mentioned is the solution in which the first physical quantity, which is fy S5, for example, height or vertical velocity is measured by satellite positioning δ ^. The air pressure sensor measures another physical co ° 25 that is barometric pressure. In this case, a measurement of atmospheric pressure can be used to determine a vertical motion value of x £ that can be used to control the filtering strength of a physical quantity measured by GPS.

The practice and advantages of the invention will now be explained in more detail with reference to the accompanying drawings.

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Brief Description of the Drawings

Figure 1. Accuracy of GPS velocity (1), velocity sensor (2) estimated from wrist or other body acceleration sensor, and composite velocity (3) as a function of frequency.

Figure 2. An object model of a sensor fusion system according to one embodiment which combines data from several different speed sources and then adaptively filters the combined speed data.

Figure 3. Flow diagram of a method according to the invention according to one embodiment.

Figure 4. Exemplary velocity measurement data (blue line with squares is 10 wrist velocity calculated speed and red line with circles is GPS measurement velocity), conventional filter corrected speed (dashed line) and velocity calculated using the method of the invention (solid thick line).

Figures 5a-5f show embodiments of a measurement system according to various embodiments of the invention.

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Detailed Description of Embodiments

The basic principle of the invention is first examined by means of the object model shown in Figure 2.

It illustrates, by way of example, three different speed measurement methods, acceleration sensor measurement 15 on wrist 14, GPS based speed measurement co 20 16 and shoe sensor (typically based on foot accelerations) o cvJ 18. The determined speeds are centrally linked 22. Combined | 4.

The cp rate filtering 20 utilizes, in the example of Figure 2, the acceleration measurement 15c0 information from a person's motion state.

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Figure 2 also illustrates that the rigid system can be used to autocalibrate measurements oa, 25 through steps 28, 24, when measurement or measurements with an error value less than the error value of the measurement being calibrated are available. For example, it is possible to use a composite quantity that does not contain the measurement data to be calibrated, and to calibrate with this combined quantity the measurement model to obtain more accurate measurement values in the future.

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The necessary calculation is performed in the data processing unit 11.

There are many ways to combine speeds. One way is to construct a relative number (Rj) for each individual speed measurement i (corresponding velocity v;) that describes how much error there is in the speed measurement. The higher the number, the higher the 5 measurement error estimate, and the lower the value, the smaller the measurement error estimate. For GPS, the number can be generated, for example, using the GPS Horizontal Dilution of Precision (GPS) and the number of satellites seen. The acceleration and shoe determinations may use a relative or absolute predetermined error estimate or a dynamically determined runtime error estimate.

10 The combined rate vCOmbined is then given by the formula v - y (v IL5c £ l \ combtiwd "i. J *

The combined rate measurement error RCOmbmed is obtained from the formula n _ (Σ ~ isst m [!; T ^ combined v »'where R s is the smallest of the errors R \.

15 Combined rate filtering can be done, for example, using a Kalman filter

{Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition, R. Grover and P

Hwang, John Wiley & Sons, 1997). The Kalman filter builds a linear model which takes into account the measurement error and the system model £ 2 error. In the case of the example, the Kalman filter consists of only one state, which is o ^ 20 resulting in the desired filtered rate Vfiitered · Since the Kalman filter filters r ».

? combined speed, the measurement error is obtained from the above formula, which combines the measurement error values of the various co ° velocities into a single number, Rcombined- x

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The system model error Wsystem can be obtained by combining the O) system error estimates calculated from different measurements. For example, in the following formula g ^ system ~ fwrtstAcceleratton ^ I'wristAcceleration) fgpsil'gps') ffootpod (j'footpod) c \ j 25, where the functions / represent system error estimates for each measurement. The calculation of the system error estimate for a measurement depends on the filtering model used. In the case of Example 9, the Kalman filter consists of only one state, which is velocity. In this case, the system error estimate should describe the changes in speed.

Subsequently, using the Kalman filter formulas, an adaptive filter is formed which eases the filtering when the measurements are accurate and in turn increases the 5 filters as the measurement error increases. Modeling a system model error allows filtering to be lightened if rapid changes are found in the system. On the other hand, if the frequency band is narrow, that is, there is no change in speed, the filtering can be tightened.

Figure 3 is a flow chart illustrating a method according to the invention according to one embodiment. The determination of velocity begins in step 30. Thereafter, the first and second physical magnitudes in steps 31a and 31b are started to be measured. When enough data has been collected, the combined rate in step 32 is calculated using, for example, the formula above. The system model error is calculated in step 34. The combined speed is then filtered in step 35 to obtain a more noisy rate estimate. Here, the error estimate of the first measurement determined in step 33a and either the second measurement or the error estimate determined in step 33b is utilized. Once the filtered rate is calculated, the result is typically stored in the device memory and / or notified to the user in step 36.

Figure 4 shows the velocity (squares) calculated from the wrist with the accelerometer and the velocity (circles) obtained from the GPS-20 measurement. Both measurements are of the correct order of magnitude, but relatively noisy.

w The conventionally combined filtered (median) filtering rate (dashed line) is δ 00 much less noisy but still contains relatively sharp variations.

h-? Particularly noteworthy is the slowness of the change in estimation that is evident with the change of speed, i.e. the high rate of speed coo 25 due to the use of an unadaptive

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£ a filtering function that filters data over a constant time.

0 01 The combined speed (solid line) filtered according to the invention is

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^ remarkably smooth at the beginning of the movement and responds quickly to real speed change.

00 This is because the filter takes into account a sharp change in speed from the GPS and / or acceleration data and the filtering is fine-tuned. Thus, despite the effective filtering of the noise 10, the rate calculated in the manner of the invention responds faster to the true rate change than the median filtered rate.

The principle described above can be applied not only to speed measurement but also to altitude measurement. Acceleration measurement and / or pressure measurement can then identify a person's Motion mode and adjust the temporal filtering of altitude or vertical velocity estimated by pressure measurement or gps measurement so that the motion is more relaxed when moving, especially when moving.

According to one embodiment, the system comprises the ability to select the type of sport to be performed and / or the location of the sensor through suitable interface elements from at least two different options. For example, in the case of an accelerometer, the type of the signal (e.g., running / walking) and the location of the accelerometer (e.g., ankle / thigh / wrist / shoulder) will affect the signal strength, quality and characteristics. Thus, the device may be programmed with a variety of signal processing algorithms, of which according to the genre or location of the sensor 15, the one best suited to the conditions is selected. In certain situations, the genre selection and thus the algorithm switching can also be accomplished by automatic identification, for example, by utilizing a specific step frequency.

More generally, the system may comprise a condition parameter which may receive different values and which further influences the method of calculating the target variable used.

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Fig. 5a shows an example of a co o measuring system utilizing the method of the invention, comprising a wrist computer 50a capable of receiving iT satellite positioning information from the satellite positioning system 51a and an acceleration data cp co from, for example, an accelerometer mounted on a shoe. Based on the measurement data, the o x 25 wristop computer is adapted to calculate the filtered speed in accordance with the invention.

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Fig. 5b shows a system similar to Fig. 5a, but in which accelerometer 52b is integrated directly with wristop computer 50b. The satellite positioning system 51c is connected wirelessly to the wristop computer 50b. In the case of altitude or vertical speed measurement, the sensor 52b may also be a pressure sensor.

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Fig. 5c shows a modification of the system of Fig. 5a, in which a mobile phone or tablet 50c which receives information from the satellite positioning system 51c and from the accelerometer 52c 'is used instead of a wristop computer.

Figure 5d shows a system using both a mobile phone 50d and a 5 wristop computer 5d. The mobile phone 5d can receive and store satellite positioning information from the system 5d and transmit the position or velocity information to the wristop computer 53d. The accelerometer 52d 'may be directly connected to a wristop computer 53d or a mobile phone 50d.

Figure 5e shows a system using a wristop computer 53e and a separate GPS-10 measuring device 50e ("GPS pod") that communicates wirelessly with the wristop computer 53e. The GPS measuring device 50e can thus further transmit position or velocity information to the wristop computer 53e. A separate accelerometer 52e 'may be directly connected to a wristop computer 53e or a GPS measuring device 50e.

Figures 5c-5e also show variations in which the latch sensor 52c ", 52d", 15 52d '", 52e" or 52e' "is located in a GPS device or wrist unit instead of a shoe.

As a particularly interesting embodiment, Fig. 5f further shows a rigid system similar to Fig. 5e using a wristop computer 53f and a separate satellite positioning module, such as a GP S measuring device 50f ("GPS pod"), which wirelessly communicates with wristop computer 53f and also includes acceleration sensor 52f. Thus, there is no need for a separate accelerometer device, but acceleration information can also be wirelessly transmitted from the £ 2 GPS measuring device 50f to the wrist unit. The GPS gauge may have a pressure sensor in addition to or instead of an δ 00 accelerometer so that it is also suitable for altitude measurement according to method rk 9.

co o x 25 Some embodiments of Figures 5c-5f have the advantage that GPS measurement is performed by its own power supply and thus improves the operating time of the wrist unit or mobile phone.

0 01 In these cases, the wrist unit can also be designed for battery operation

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^ 2 instead of battery power.

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Particularly preferred is an embodiment in which at least one of the measurements, preferably both measurements, and the calculation of the target variable is performed outside the wrist unit or 12 mobile phones. Such a situation is exemplified in the rigid system of Figure 5f, where the GPS measuring device 50f also includes means for performing the necessary calculation. In this case, the wrist unit or the cellular phone will essentially function as a display only, whereby its power consumption is very low compared to performing some or all of these 5 operations there. This is important given that the performance may be long and wrist or mobile phones typically have other power-consuming applications during sporting activities (such as heart rate measurement or music functions), and speed and position measurement functions are typically relatively power-consuming.

10 A satellite positioning system may also use a terrestrial wireless positioning system, such as a base station positioning system.

If the satellite position sensor and / or acceleration sensor are located in a different unit from the terminal itself, for example according to one of the solutions described above, an error estimate describing the accuracy of the positioning and / or acceleration measurement can be transmitted from the sensor units. a goodness number (s) for the terminal wirelessly. The terminal may directly use such a quality index or any derivative derived thereof as the error estimate.

Depending on the quantity being measured, the value of quality may depend on, for example, the type of sensor, the measuring location and / or the values of the sensor.

20 In particular, in the case of satellite positioning, the goodness factor strongly depends on the values of the data provided by the satellite positioning sensor. In the case of the GPS standard, if the GPS provides only standard NMEA (National Marine Electronics Association) information, δ ^ can be calculated from the following values available in the NMEA message: rk o eo 1. number of satellites in solution o £ 25 2. geometry accuracy estimate (HDOP) o

If available, additional SIRF IV value may be used:

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o 3. Estimated Horizontal Position Error (EHPE), which gives an even better QC.

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Thus, in order to calculate the quality ratio, it is necessary to transmit to the terminal either these numbers separately or, alternatively, the value calculated on the basis thereof.

In the case of a shoe acceleration sensor, a cycling acceleration sensor or a wrist acceleration sensor, only the type of sensor typically signifies that, on the other hand, it is sufficient to transmit only the type of sensor as a valid value instead of 5.

The values required for calculating the goodness score or the goodness number itself may be transmitted in accordance with a suitable radio protocol such as the ANT or Bluetooth protocol.

Combinations of the above solutions and non-detailed variations are also possible.

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Claims (30)

1. A method for determining a measurable object quantity, either speed or altitude, in which a first physical quantity is measured by a first sensor, a second physical quantity is measured by a second sensor, the value of the object quantity is calculated by the measurement of the first and the second physical quantities, characterized in that the calculation of the object quantity value comprises the following steps: an estimate of the object quantity is determined at least by the measurement of the first physical quantity, at least a first error estimate is determined, which represents the measurement error of the first physical quantity, and the object quantity estimate is filtered with a strength, which is dependent on both the first error estimate and the measurement of the second physical quantity. Method according to claim 1, characterized in that said object quantity estimation is calculated by means of the measurement of the first and second physical quantities. Method according to claim 1 or 2, characterized in that co a second error estimate is determined which represents the second physical c of the magnitude measurement error, and the cp co ° object quantity estimate is filtered with a strength which is dependent on both the first X oi and the other error estimate. o g Method according to any of the preceding claims, characterized in that the strength of the filtering £ is increased in the calculation of the object quantity while the error estimation of the first C andra J and / or the second physical quantity measurement grows, and vice versa. Method according to any of the preceding claims, characterized in that the rate of change of the object quantity is observed, the strength of the spring changes in the calculation of the object size, depending on the rate of change of the first and / or the second quantity. Method according to any of the preceding claims, characterized in that the estimation of the object quantity is filtered by means of a time-adjustable filter, where the length of the filtering period is arranged to change from the changes observed in the measurement of the first and / or the second physical quantity. Method according to any of the preceding claims, characterized in that the object size is velocity, the first sensor is a satellite location sensor, and the first physical quantity is either velocity, absolute position or relative position. Method according to any of claims 1 to 6, characterized in that the object quantity is height, the first sensor is an air pressure sensor, and the first physical quantity is the air pressure. Method according to any one of claims 1 to 6, characterized in that the object size is altitude, the first sensor is a satellite location sensor, and the first physical quantity is altitude. o 5) Method according to any of the preceding claims, characterized in that the second sensor is an accelerometer and the other physical quantity is acceleration. Method according to any of the preceding claims, characterized in that the second sensor is a pressure sensor and the other physical quantity is the air pressure. Method according to any of the preceding claims, characterized in that it is performed in whole or in part on an arm computer. Method according to any of the preceding claims, characterized in that it is fully or partially carried out in a mobile telephone. Method according to any of the preceding claims, characterized in that it is carried out in whole or in part in a satellite location module or in any other device without a display. Method according to any of the preceding claims, characterized in that it is carried out in whole or in part in a portable satellite location device, in an open-air computer, computer or any other device with display. Method according to any of the preceding claims, characterized in that the object size estimate is fed using a Kalman-fter. Method according to any of the preceding claims, characterized in that the first and second sensors are based on different measurement techniques, which exhibit a substantially different error profile as a function of the measuring frequency. Method according to any of the preceding claims, characterized in that the filtering of the object size estimation is realized in such a way that a calculation model's error estimate is determined, which describes how well the co-calculation model used corresponds to the actual situation, by means of measuring at least one physical magnitude, and in the N-o co object magnitude estimation is filtered with a strength, which is dependent on both the ox ox measurement error estimation and the approximate calculation model error estimation. cr CL A method according to any of the preceding claims, characterized in that at least two σϊϊ of the following are located in separate device units which are arranged to wirelessly communicate with each other: a first sensor, a second sensor, means for calculating the the value of the object quantity, wherein a device unit containing at least one of the aforementioned sensors is arranged to convey necessary information about the measurement error estimation sort is done by means of the sensor in question, or to calculate the same, in a wireless way to a device unit comprising a device unit comprising the value of the object quantity. 20. A method according to any of the preceding claims, characterized in that it comprises a ratio parameter, such as the type of sport, which can obtain different values and whose value further influences the method of calculation of the object size used. A system for determining a measurable object quantity, either speed or height, comprising a first sensor for measuring a first physical quantity, a second sensor for measuring a second physical quantity, means for calculating the value of the object quantity by means of the measurement of the first and second physical quantities, characterized in that the means for calculating the object size value comprises means for determining an estimate of the object size at least by means of the measurement of the first physical quantity, means for determining at least a first error estimate, which represents the first physical magnitude measurement accuracy, and means for filtering the object magnitude estimation with a strength which is dependent on both the first error estimate and the said second physical magnitude co o measurement. (M | 4. S5 System according to claim 21, characterized in that it is arranged to carry out the method according to any of claims 1-18. X DC CL System according to claim 21 or 22, characterized in that at least two of the following are g located in separate device units, which are arranged to wirelessly communicate with each other: a first sensor, a second sensor, means for calculating the value of the object quantity. C \ J System according to claim 23, characterized in that one of the device units is a wristband or some other portable display device. 25. System according to claim 23 or 24, characterized in that the other of the device units is a satellite location module, an outdoor computer, a computer or some other device with display. 26. A system according to any of claims 21-25, characterized in that the second probe or the sensor, and alternatively also the means for calculating the value of the object quantity, are located in a device unit without display. System according to claim 26, characterized in that it also comprises a device unit with display, said device unit without display being arranged to be in wireless communication with the device unit with display, for displaying the value of the object quantity. 28. A system according to any one of claims 23 to 27, characterized in that the device unit containing at least one of said sensors is arranged to convey the measurement error estimate made by the sensor in question, or data needed for the calculation thereof, a wireless way. System according to any one of claims 21-27, characterized in that the first and second sensors and the means for calculating the value of the object quantity are located in the same portable device unit. 30. System according to claim 29, characterized in that said portable device unit consists of a wristband device, a mobile phone, a satellite location device with or without a display, an open-air computer, a tablet computer. co δ c \ j N- o CO o X cc CL o δ m δ c \ j