I noticed a really puzzling behavior on iPhone:
If I hold the phone in the vertical, and tilt it, the compass change.
I already figured the amount it changes is the same amount it would change for the same amount of tilting if it was in horizontal (ie: suppose that a vector coming from the screen is called Y, turning around Y does not matter the attitude of the iPhone results in a compass change).
I want to compensate that, my app was not made to you hold the phone in the horizontal (although I do plan also to allow some tilting in the X axis let's call it, from like 10 degrees to 135)
But I really could not figure how iPhone calculate the heading, thus where the heading vector actually points...
After some scientific style experiments, I found:
The iPhone has magnetometer, it has 3 axis, X, that goes from left to right from the screen. Y, that goes from bottom to up. And Z, that comes from behind the phone and comes to the front.
Earth magnetic field is as expected by the laws of physics not a sphere, in the location I am (brazil), it is slanted about 30 degrees. (meaning that I have to hold the phone in a 30 degrees angle to zero 2 axis).
One possible technique to calculate north, is use cross product of a vector tangential to the magnetic field (ie: the vector the magnetometer reports to you), and gravity. The result will be a vector that points east. If you wish you can make another cross product between east and gravity, resulting in a vector that points north.
Know that iPhone sensors are quite good, and every minor fluctuation and vibration is caught, thus it is good idea to use a lowpass filter, to remove the noise from the signal.
The iPhone itself, has a complex routine to determine the "true heading", I don't figured it completely, but it uses the accelerometer in some way to compensate for tilt. You can use the accelerometer and compensate back if that is your wish, for example if the phone is tilted 70 degrees, you can change the true heading by 70 degrees too, and the result will be the phone ignoring tilting.
Also the routine of true heading, verify if the iPhone is upside down or not. If we consider it in horizontal, in front of you as 0, then more or less at 135 degrees it decides that it is upside down, flipping the results.
Note the same coordinate system also apply to the accelerometer, allowing the use of vectors operations between accelerometer and magnetometer data without much fiddling.
Related
I am writing an iPhone/iPad app. I need to compute the acceleration and deceleration in the direction of travel of a vehicle traveling in close to a straight horizontal line with erratic acceleration and deceleration. I have the sequence of 3 readings from the X,Y,Z orthogonal accelerometers. But the orientation of the iphone/ipad is arbitrary and the accelerometer readings include vehicle motion and the effect of gravity. The result should be a sequence of single acceleration values which are positive or negative depending on whether the vehicle is decelerating or accelerating. The positive and negative direction is arbitrary so long as acceleration has the opposite sign to deceleration. Gravity should be factored out of the result. Some amount of variable smoothing of the result would be useful.
The solution should be as simple as possible and must be computationally efficient. The answer should be some kind of pseudo-code algorithm, C code or a sequence of equations which could easily be converted to C code. An iPhone specific solution in Objective C would be fine too.
Thanks
You will need some trigonometry for this, for example to get the magnitude you need
magn = sqrt(x*x+y*y+z*z);
to get the angle you will need atan, then c function atan2 is better
xyangel = atan2(y,x);
xymagn = sqrt(x*x+y*y);
vertangle = atan2(z,xymagn)
no how you get negative and positive magnitude is arbitrary, you could for example interpret π/2 < xyangle < 3π/2 as negative. That would be taking the sign of x for the sign of magn, but it would be equally valid to take the sign from y
It is really tough to separate gravity and motion. It's easier if you can analyze the data together with a gyroscope and compass signal.
The gyroscope measures the rate of angular rotation. Its integral is theoretically the angular orientation (plus an unknown constant), but the integral is subject to drift, so is useless on its own. The accelerometer measures angular orientation plus gravity plus linear acceleration. With some moderately complex math, you can isolate all 3 of those quantities from the 2 sensors' values. Adding the compass fixes the XY plane (where Z is gravity) to an absolute coordinate frame.
See this great presentation.
Use userAcceleration.
You don't have to figure out how to remove gravity from the accelerometer readings and how to take into accont the orientation: It is already implemeted in the Core Motion Framework.
Track the mean value of acceleration. That will give you a reference for "down". Then subtract the mean from individual readings.
You'll need to play around with the sensitivity of the mean calculation, since, e.g., making a long slow turn on a freeway will cause the mean to slowly drift outwards.
If you wanted to compensate for this, you could use GPS tracking to compute a coarse-grained global acceleration to calibrate the accelerometer. In fact, you might find that differentiating the GPS velocity reading gives a good enough absolute acceleration all by itself (I haven't tried, so I can't say).
I want to use the iPhones's accelerometer to detect motions while driving. I'm a bit confused what the accelerometer actually measures, especially when driving a curve.
As you can see in the picture, a car driving a curve causes two forces. One is the centripetal force and one is the velocity. Imagine the iPhone is placed on the dashboard with +y-axis is pointing to the front, +x-axis to the right and +z-axis to the top.
My Question is now what acceleration will be measured when the car drives this curve. Will it measure g-force on the -x-axis or will the g-force appear on the +y axis?
Thanks for helping!
UPDATE!
For thoses interested, as one of the answers suggested it measures both. The accelerometer is effected by centrifugal force and velocity resulting in an acceleration vector that is a combination of these two.
I think it will measure both. But don't forget that the sensor will measure gravity as well. So when your car is not moving, you will still get accelerometer readings. A nice talk on sensors in smartphones http://www.youtube.com/watch?v=C7JQ7Rpwn2k&feature=results_main&playnext=1&list=PL29AD66D8C4372129 (it's on android, but the same type of sensors are used in iphone).
Accelerometer measures acceleration of resultant force applied to it (velocity is not a force by the way). In this case force is F = g + w + c i.e. vector sum of gravity, centrifugal force (reaction to steering centripetal force, points from the center of the turn) and car acceleration force (a force changing absolute value of instantaneous velocity, points along the velocity vector). Providing Z axis of accelerometer always points along the gravity vector (which is rare case for actual car) values of g, w and c accelerations can be accessed in Z, X and Y coordinates respectively.
Unless you are in free fall the g-force (gravity) is always measured. If I understand your setup correctly, the g-force will appear on the z axis, the axis that is vertical in the Earth frame of reference. I cannot tell whether it will be +z or -z, it is partly convention so you will have to check it for yourself.
UPDATE: If the car is also going up/downhill then you have to take the rotation into account. In other words, there are two frames of reference: the iPhone's frame of reference and the Earth frame of reference. If you would like to deal with this situation, then please ask a new question.
I am struck with a problem. I want to convert the CMAttitude information of an iPhone to Altitude (0 to 90deg) and Azimuth (0 to 360 deg). I have googled around and hit some threads which discuss about it, but none of threads turn out with a positive answer and most of the articles discussing Quaternion and Euler angles are too much mathematics to stuff into my brain!
Is there some open source material which does this task easy? Or someone has written code to perform this conversion?
Edit:
First off, sorry for being so abstract!
Azimuth is the direction on the surface of the earth towards which the device is pointing. Like North = 0 deg, North East = 45deg, East = 90 deg, South = 180 deg and so on. Ranges between 0 deg to 360 deg:
Altitude is the angle made from the plane of the earth to an object in the sky:
Thanks,
Raj
Using CMDeviceMotion, you can get a CMAttitude object with "roll, pitch and yaw" - where for example, given a phone held in portrait mode "yaw" is "azimuth", "pitch" is the tilt of the phone with respect to ground, or zenith, and "roll" is about the vector pointing through the screen and not what you're interested in.
Things get a bit tricky because "azimuth" is a projection of the 3D magnetic vector (pointing towards the magnetic north pole) on to the flat "ground" plane, which changes depending on device orientation, but given this understanding of the terms, threads like this one should be much more understandable. If you only need your application to work in one orientation things get much simpler.
P.S. "altitude" is almost exclusively used to refer to elevation or height about a given reference (sea level, geodetic height etc). "Zenith" or "pitch" are preferable, and since you're on iOS, you should stick to their coordinate scheme: (lat, lon, alt), (pitch, yaw, roll).
I know how to get the coordinates of the magnetic heading: heading.x, heading.y, heading.z
The thing is that I'd need the (x, y, z)-vector of the trueHeading. How can I create this vector?
Thank you!
Edit: I have changed my answer quite a bit...
Basically you need to rotate the magnetic north vector in the opposite direction to the Magnetic Declination angle.
The hard part is that you need to rotate the vector on a horizontal plane. For that you need to know the orientation of the phone.
Here is what you need to do:
Get the magnetic north vector.
Get the gravity vector from the accelerometer.
Now calculate / look up the Magnetic Declination (it depends where you are in the world and it also varies slowly with time).
Rotate the magnetic north vector X degrees about the gravity vector (where -X = Magnetic Declination). This will be the tricky part, you will need to brush up on some 3d trig.
Thank's for the edit...funny, that's exactly what I did then. I took the magnetic north vector and rotated it with a rotation matrix around the gravity vector with the variation between the magneticHeading and the trueHeading.
The thing is that I'm dependent on the magnetic vector in this case.
In some situations I noticed that the magnetic vector was going absolutely crazy and the sensor delivered weird values.
So what I wanted is to get the vector of the trueHeading which is independent from the magnetic vector. Ok, what a silly thought - the true heading is most probably anyway dependent on the magnetic heading already.
However - thank's for the answer :)
I have an accelerometer and magnetometer each producing raw X, Y and Z readouts. From this I need to determine the magnetic heading of an object.
I'm not that great at trig, but I've put together a formula that does respond pretty well to the rotation of the device, but also responds to movement that one would not think is relevant, such as angling the device in such a way that has no impact on the direction it is pointed. Such as laying it flat and "rolling" the device.
I think the formula I have for calculating the magnetic heading is fine, but I think my pitch and roll radians for input are wrong.
So I guess the core of my question (unless someone actually has a formula around that does this), is how do you calculate angles, in radians, using an accelerometer for pitch and roll.
Then secondly, any info on the heading formula itself would be great.
Depending on the accuracy your application requires, you may need to solve several problems:
Are the accelerometer axes calibrated? I've seen MEMs accelerometers that had axes that were not mutually perpendicular, and had significantly different response characteristics for each axis (typically X and Y would match, and Z would differ). You will need to synthesize ideal XYZ axes from whatever physical reading your device provides. (Google 'accelerometer calibration'.)
Are the magnetometer axes calibrated? Similar problem as above, except much harder to check: It is very difficult to generate uniform calibrated magnetic fields. If you use the ambient geomagnetic field, you will need to carefully control the local magnetism of your work environment and your tools. (Google 'magnetometer calibration'.)
After the accelerometer and magnetometer have been individually calibrated, their axes need to be calibrated relative to each other. Since both of these devices are typically soldered to a PCB, there is almost guaranteed to be significant misalignment. In many cases, the board layout and device parameters may not even permit the XYZ axes to correspond with each other! This may be the hardest part to do from a lab perspective, so I'd recommend you do a direct comparison using other hardware that has both kinds of sensors and is already calibrated (such as an iPhone or Android phone - but verify the device before use). Normally, this is accomplished by adjusting the prior two calibration matrices until they generate vectors that are correctly aligned relative to each other.
Only after you are generating mutually calibrated magnetic and accelerometer vectors can you apply the solutions suggested by the other respondents.
I've only described the static solution, where both the magnetometer and accelerometer are motionless relative to the local gravitational and magnetic fields. If you need to generate responses in real-time while the system is rapidly moving, you will need to account for the time behavior of each sensor. There are two basic ways to do this: 1) Apply time-domain filters to each sensor so that their outputs share a common time domain (generally adding some delay). 2) Use predictive modeling to modify the sensor outputs in real-time (less delay, but more noise).
I've seen Kalman filters used for such applications, but applying them in a vector domain may require using quaternions instead of Euler matrices. Quaternions are easier to use computationally (fewer operations needed compared to matrices), but I found them to be much more difficult to comprehend and get right.
Or, you may choose a completely different path, and use statistics and data fitting to do all the above work in one giant step. Consider the problem as follows: Given 6 input values (XYZ each from uncalibrated magnetometer and accelerometer) and a reference to the device (assuming it is hand-held, and there is an arrow painted on the case), output a single angle representing the magnetic bearing toward which the arrow on the case is pointing, and the elevation of the arrow relative to the gravity vector (tilt of the case).
Using a calibrated reference device (as mentioned above), pair it with the device to be calibrated, and take a several hundred data points, with the device at different orientations. Then use a powerful math package such a Matlab, MathCAD, R or SciPy to setup and solve the nonlinear equations to create the transformation matrices.
I would point to Euler Angles and Roll Pich Yaw.
You're not thinking in enough dimensions. This would be the answer in only 2 dimensions, and it works great if you can find a way to ensure "Z" always aligns with gravity.
int heading=180-atan2(mag_datX, mag_datY)/0.0174532925; // 0/359=N, 90=E, 180=S, 270=W
(if you're reading directly form the device - beware that it probably returns X, Z, Y - not X, Y, Z !)
However - this is not a 2D compass problem - imagine you take the needle out of the compass, balance it so that gravity plays no part in keeping it "level", and you'll find that "north" will point a bit up or down - depending where on earth you are (or, if at the poles, directly up or down!).
So you need to try and compute the THREE DIMENSIONAL vector from all 3 values - which is a matrix operation.