Noisy acceleration data
- Thread starter Achronia
- Start date
Help Support The Rocketry Forum:
- Joined
- Jan 23, 2009
- Messages
- 5,980
- Reaction score
- 4,091
Acceleration data should not be noisy. What problems are you having or are you just antipating?
Most accelerometer chips allow you to specify a low pass digital filter when you configure it. There are many options. Most default at a cutoff frequency about 1/4 the sampling frequency you select.
If you are getting noise from a stationary circuit, then you have an electrical issue in your circuit.
Most accelerometer chips allow you to specify a low pass digital filter when you configure it. There are many options. Most default at a cutoff frequency about 1/4 the sampling frequency you select.
If you are getting noise from a stationary circuit, then you have an electrical issue in your circuit.
Thank you. I haven't actually got my accelerometer yet. I have an experience with an HMR magnetometer being too noisy. I thought maybe accelerometer was similar. But just in case the noise was large, what would you suggest I do?
- Joined
- Jan 23, 2009
- Messages
- 5,980
- Reaction score
- 4,091
Cross that bridge later...if needed
- Joined
- Sep 5, 2009
- Messages
- 3,532
- Reaction score
- 1,655
Is this accelerometer data considered noisy? Sure looks noisy compared to the realistic smooth profile from simulation. If you want to do anything useful with the measured data (integrate, get thrust or drag profiles), then some filtering or smoothing is needed.
Last edited:
Thank you for sharing. But what would you suggest for smoothing, a kalman filter or high-pass - low-pass filters
UhClem
Well-Known Member
- Joined
- Feb 6, 2009
- Messages
- 2,052
- Reaction score
- 443
An integrator is a low pass filter all on its own.
- Joined
- Sep 5, 2009
- Messages
- 3,532
- Reaction score
- 1,655
True.
- Joined
- Sep 5, 2009
- Messages
- 3,532
- Reaction score
- 1,655
I don't know much about filters. I may simply fit a least-squares curve through the data of interest. For example, I want to compute drag coefficient, so only the coast phase of the flight is needed. In my example above, from t = 2 s to 16 s, a logarithmic fit may work.
Last edited:
You definitely do not want a high pass filter. This would simply give you all of the noise and none of the true signal.
Depending on your goal, it is useful to divide filters into two categories: causal, and non-causal. I suspect you want non-causal to compute drag coefficient after the fact.
A causal filter is based only upon past and present time and is useful for something happening in real time. If you have a need to know at this instant what is the best estimate of acceleration, you have only the data up to the present instant to filter. Both the Low pass filter and Kalman filter are examples of causal filters. If you want to do things with data in real time, such as decide to deploy parachutes at apogee, you must have a causal filter because you don't know the future.
There are many ways to implement a low-pass filter depending on the properties that you want: what frequency you want to roll off at and how aggressive you want to roll off.
The Kalman filter is an interesting creature. Essentially it starts with a dynamic model of what you are interested in. In this case it would be F = mA applied to a rocket. Maybe with some kind of linearized atmospheric drag model. The Kalman filter assumes that there are two noises: in this case there is a random force noise applied to the rocket, which I suppose is thrust noise and air turbulence noise. There is also a random measurement noise in the acceleration sensor. If you know these two noises and the dynamic equations governing the rocket motion you can come up with a filter that determines the absolute best guess for acceleration at that moment.
A low-pass causal filter will always produce a result with a phase lag, also known as time delay, compared to the original signal.
A non-causal filter is based upon past, present, and future time. It is useful when you recorded something and then want to filter it afterwards. I think this is what you want. An example is an FIR filter, finite impulse response. For example: at an instance in time you may take 0.25 * previous sample value, 0.5 * present sample, 0.25 * future sample. This is a nice smoothing filter. The cool thing is that there can be no time lag. The filter can produce a filtered version with no phase delay so you can have a good estimate of filtered acceleration at that instant, even with a very aggressive filter.
There are many different FIR filter implementations based upon what frequency you want to roll off at and how aggressively you want to roll off.
Another common non-causal filter is called a filt-filt algorithm. You filter the data with a regular causal like a low-pass filter. Then you flip the data end-for-end and filter it again backwards.
Things like least squared fitting and many different types of nonlinear fits based on the full time record would fall into the category of non-causal filters.
Depending on your goal, it is useful to divide filters into two categories: causal, and non-causal. I suspect you want non-causal to compute drag coefficient after the fact.
A causal filter is based only upon past and present time and is useful for something happening in real time. If you have a need to know at this instant what is the best estimate of acceleration, you have only the data up to the present instant to filter. Both the Low pass filter and Kalman filter are examples of causal filters. If you want to do things with data in real time, such as decide to deploy parachutes at apogee, you must have a causal filter because you don't know the future.
There are many ways to implement a low-pass filter depending on the properties that you want: what frequency you want to roll off at and how aggressive you want to roll off.
The Kalman filter is an interesting creature. Essentially it starts with a dynamic model of what you are interested in. In this case it would be F = mA applied to a rocket. Maybe with some kind of linearized atmospheric drag model. The Kalman filter assumes that there are two noises: in this case there is a random force noise applied to the rocket, which I suppose is thrust noise and air turbulence noise. There is also a random measurement noise in the acceleration sensor. If you know these two noises and the dynamic equations governing the rocket motion you can come up with a filter that determines the absolute best guess for acceleration at that moment.
A low-pass causal filter will always produce a result with a phase lag, also known as time delay, compared to the original signal.
A non-causal filter is based upon past, present, and future time. It is useful when you recorded something and then want to filter it afterwards. I think this is what you want. An example is an FIR filter, finite impulse response. For example: at an instance in time you may take 0.25 * previous sample value, 0.5 * present sample, 0.25 * future sample. This is a nice smoothing filter. The cool thing is that there can be no time lag. The filter can produce a filtered version with no phase delay so you can have a good estimate of filtered acceleration at that instant, even with a very aggressive filter.
There are many different FIR filter implementations based upon what frequency you want to roll off at and how aggressively you want to roll off.
Another common non-causal filter is called a filt-filt algorithm. You filter the data with a regular causal like a low-pass filter. Then you flip the data end-for-end and filter it again backwards.
Things like least squared fitting and many different types of nonlinear fits based on the full time record would fall into the category of non-causal filters.
- Joined
- Sep 5, 2009
- Messages
- 3,532
- Reaction score
- 1,655
Yes, I am interested in non-causal filtering after the fact, so I can post-process the acceleration to derive other data.
Not sure what the OP is trying to do.
Not sure what the OP is trying to do.
Similar threads
