1. Field of the Invention
The present invention relates to a sensor including a detection element, an analog front end and a digital back end; the digital back end is to be connected to a control unit via a digital interface, the sensor providing sampled data in the digital back end. The present invention further relates to a method for assigning a piece of time information to sampled measuring data of a sensor.
2. Description of the Related Art
U.S. Pat. No. 7,382,780 B1 describes retrospective time synchronization using sample value counters and a real-time clock as well as the collection of data in frames. The document does not relate to sub-sampling interval times.
A sensor is typically composed of a detection element, an analog front end and a digital back end, as is apparent from
Sensors typically implement filter functions, for example, a low-pass filter, having a particular bandwidth limit.
The output data rate of the sensor often depends on ambient parameters such as the temperature. If these parameters change, fluctuations of 10% of the output data rate are not uncommon. The control unit, having an independent and typically more precise time reference, reads out the data asynchronously from the sensor at a read-out data rate rdr as shown in
Each sample interval box 10 indicates a new sensor data sample value, which is stored in the digital part; the arrows at the bottom indicate read-outs 30 of the control unit from the sensor, which in this example occur more frequently than the sensor generates output data. Since it is typically assumed that the sensor data sample time and the sensor data read-out time are identical, this means that the delay between the generation of output data and the read-out is not taken into consideration. This assumption introduces time jitter, which is indicated by jitter boxes 20. The average jitter added by the described mechanism is given by
jitteraverage=MIN(1/odr,1/rdr)/2.
Often, data from more than one sensor are used to calculate fusion data, for example, orientation in the space or position of a mobile device. These techniques are often used in inertial measurement units (IMUs). Jitter impairs the accuracy of the calculated data.
In addition to the mismatch between odr and rdr there is latency, which may impact certain applications, for example, real-time game applications.
There are three known approaches to the above-described problem. Increasing the read-out data rate rdr reduces the average jitter according to the equation for jitteraverage. Increasing the output data rate odr reduces the average jitter according to the equation for jitteraverage. The increase in the output data rate is supported by present sensors, for example, the triaxial acceleration sensor BMA255 Digital. Triggering read-outs by interrupts is another technique, which is intended to require the sensor to send an interrupt to the control unit when new data are available. This then initiates the read-out of data from the sensor.
However, these known approaches may have several disadvantages. Increasing the read-out data rate rdr increases the workload for the control unit. If the control unit is implemented as software and the executing processor has only low utilization, this approach increases the number of transitions of the executing processor from inactive to on-states, whereby the power consumption is significantly increased.
Increasing the output data rate odr means that noise on the sensor data is increased when the sampling rate is increased. If only the output data rate is changed independently of the bandwidth of the low-pass filter, the implementation of the digital back end becomes more complex. This increases the required silicon surface and the power consumption of the digital back end. As a result, the costs for the sensor hardware and for operating the sensor increase.
Triggering read-outs by interrupts requires separate lines for the interrupt signal, in addition to the typical interfaces such as I2C, thus adding costs. In addition, processing interrupts by software on the control unit is not very efficient since an interrupt requires a context change in the processor. This impacts the cache hit ratios in the processor and similar performance capability parameters. If a large number of sensors is connected to one processor, this approach does not scale well. Moreover, interrupts are not directly supplied in many modern mobile platforms, and the approach thus deals with the problem only partially.
It is the object of the present invention to create a sensor, whose measuring data are preferably precisely assignable to a measuring point in time. It is also an object of the present invention to create a method for assigning measuring data of a sensor to a measuring point in time.
The sensor according to the present invention includes: a detection element; an analog front end; a digital back end, the digital back end being connected to a control unit via a digital interface, and the sensor providing sampled data in the digital back end; and a timer unit for providing pieces of time information of the sampled data in the digital back end which the control unit is able to access via the digital interface.
The reading of the sampling timer allows the control unit to determine how old the data are at the point in time they are read out from the sensor. Ideally, this sampling time register may be read out atomically (in one data block without interruption) with the sensor data. With regard to its control system time, the control unit is able to reconstruct the point in time in the past at which the sensor data were in fact generated. If there is more than one sensor in a system, for example, an accelerometer, a yaw rate sensor and a magnetometer, synchronized sensor data are important for sensor fusion algorithms. Reduced jitter allows better sensor data synchronization. As a result, the performance capability of the sensor fusion algorithm increases.
A further aspect of the present invention is a method for reducing jitter during the procurement of data from the sensor.
Actual sensor data time=system time during data read-out−sampling timer*sampling period
As an extension, an additional delay for sampling (for example, in the sensor front end), data processing (for example, in the sensor back end), and reading of the data may be subtracted.
A further aspect of the present invention is a method for estimating the real output data rate during the procurement of data from the sensor as shown in
Implementation Example:
The sensor time register has an area rst
Rst<=2bit of the sampling counter
The maximum possible output data rate is odrmax.
A potential overflow may be corrected if the sensor time is read out at points in time t1 and t2 using
t2<t1+rst/odrmax.
The real output data rate rodr (for example, in units of sample value(s)) may be estimated using
rodr=mod(sensor time(t2)−sensor time(t1),rst)/(t2−t1)
the modulo operation of Knuth, Donald, E, The Art of Computer Programming, Addison-Wesley, 1972, being defined.
Applications of the rodr are a reduction of latency or reduction of jitter, for example, when FIFOs are used, before the control unit receives the data.
A further aspect of the present invention is a method for reducing latency during the procurement of data from the sensor as shown in
The rodr may then be used to calculate the future times t(n) at which the next n sample values are ready for the control unit to retrieve from the sensor,
t(n)=t2+(n−sampling timer(t2)/rodr
n>=1 . . . .
The control unit may thus predict when the next data will be generated and is able to retrieve these with low latency, immediately after the data are available in the sensor.
A further aspect of the present invention is a method for reducing jitter using the FIFO memory in the sensor as shown in
tf(n)=t2−sampling timer(t2)+n−1)/rodr
If the odr is used instead of the rodr, an error of 5% results in the odr in a FIFO having 10 elements in 50% of a sampling period jitter. This problem is solved when using rodr instead.
A further aspect of the present invention is a method for extending the measuring interval of the sensor as shown in
The limitation of the measuring interval to
rst/odrmax
may be eased if the minimal possible output data rate odrmin is known by the measuring interval
rst/(odrmax−odrmin)
whereby a clear correlation with the number of sensor time overflows sto with respect to sensor time (t1) is made possible,
rodr=mod(sensor time(t2)−sensor time(t1),rst)+sto*rst/(t2−t1)
where
with
sto=(t2−t1)*odrmin/rst
sto modmin=mod((t2−t1)*odrmin,rst)
realmod=mod(sensor time(t2)−sensor time(t1),rst)
Here, stomin is the minimum number of sensor time overflows, which is based on the minimal odr. A sensor time overflow at point in time t exists when
sensor time(t)=sensor time(t1).
The maximum number of overflows may only be greater by one than the minimum number; otherwise the clear correlation of sensor time overflows with the sensor time is no longer possible. If the modulo value from the measured sensor time data is below the modulo value of the minimum odr, there must be an additional sensor time overflow.
Number | Date | Country | Kind |
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10 2012 207 026 | Apr 2012 | DE | national |
Filing Document | Filing Date | Country | Kind |
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PCT/EP2013/054505 | 3/6/2013 | WO | 00 |
Publishing Document | Publishing Date | Country | Kind |
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WO2013/159972 | 10/31/2013 | WO | A |
Number | Name | Date | Kind |
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7382780 | Moretti et al. | Jun 2008 | B1 |
20110313697 | Staton et al. | Dec 2011 | A1 |
Number | Date | Country |
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1 742 187 | Jan 2007 | EP |
WO 2010056246 | May 2010 | WO |
Entry |
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International Search Report for PCT/EP2013/054505, dated Jan. 3, 2014. |
Number | Date | Country | |
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20150123827 A1 | May 2015 | US |