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Choose the sample rate from the fastest motion your application must preserve, not from a customary IMU setting. Estimate the highest frequency that matters, sample at more than twice that frequency, and leave room for a real filter’s transition band and the waveform fidelity you need. Then attenuate unwanted energy before the analog-to-digital converter or before any digital decimation. The correct values depend on the motion, sensor bandwidth, filter response, timing, and processing architecture.
Nyquist’s limit is a mathematical floor, not a safe default. A practical design also checks passband loss, stopband attenuation, phase delay, noise, output data rate, and synchronization with cameras or other sensors. The workflow below shows how to make those choices and how to verify them on the actual device.
What sampling rate means in a motion tracker
A sensor’s sampling rate is the frequency at which its measurement is captured or delivered. It is expressed in samples per second (hertz). An IMU may have several different rates: an internal sensing or conversion rate, a digital filter rate, an output data rate, and the rate at which your host reads the interface. These numbers are not automatically identical.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe frequency content of motion is also different from the motion’s visible speed. A slow arm movement can contain a sharp impact when the hand stops. A rotating part can have a fundamental frequency plus harmonics caused by imbalance, gear teeth, or impacts. A tracking algorithm may need only the low-frequency pose, or it may need short transients for contact detection. Define the fastest event or periodic component that affects the estimate before choosing hardware settings.
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Frequency of interest versus maximum sensor bandwidth
Use the highest frequency your application must preserve as the design target. Do not treat the sensor’s advertised maximum bandwidth as a requirement unless the application truly needs all of it. Preserving unnecessary bandwidth increases data volume and exposes the system to more noise and aliasing risk.
For a roughly periodic motion, frequency in hertz is cycles per second. For an event, estimate how quickly the signal changes and inspect representative recordings if available. A short pulse requires more bandwidth than a smooth movement with the same overall duration. When the motion is not known precisely, design around the fastest credible event, then validate with representative use rather than relying on a nominal setting.
Use Nyquist as a lower bound, not a finished design
For a band-limited signal whose highest relevant frequency is fmax, the theoretical sampling requirement is:
fs > 2fmax
Sampling at exactly twice the frequency leaves no room for a filter transition band and gives poor representation of waveform shape. A real anti-alias filter does not switch from full transmission to complete rejection at one frequency. It rolls off across a transition region. Any energy in that region must be accommodated between the passband you want to keep and the Nyquist frequency of the chosen sample rate.
National Instruments explains that sampling at around five times the signal frequency is often desirable for practical waveform acquisition. That is useful general guidance, not a universal prescription for motion tracking. A narrow-band measurement with a carefully specified filter may work at a lower ratio, while a transient-sensitive application may need substantially more. Select the ratio from the required fidelity, filter characteristics, and error tolerance.
A numerical way to think about the margin
Suppose the application must preserve content to 20 Hz. A 40 Hz sample rate is only the theoretical boundary. If the anti-alias filter needs a transition from 20 Hz to 30 Hz, the sample rate must place the Nyquist frequency above the end of that transition; a 60 Hz rate gives a 30 Hz Nyquist frequency, but it still leaves little guard band for attenuation. A higher output rate provides more room for the filter to roll off and for timing or frequency-estimation errors. The exact choice depends on the filter’s measured or documented response.
A practical selection workflow
- Describe the motion to preserve. Write down the fastest event, oscillation, impact, or change that affects the estimate. State whether you need waveform shape, event timing, peak amplitude, or only a low-frequency pose.
- Estimate the highest frequency of interest. Call it fmax. Include harmonics or short transients if they carry information. Exclude bandwidth that the algorithm will intentionally discard.
- Choose a rate above 2fmax. Add headroom for the filter transition band and for the fidelity needed by the estimator. Treat the mathematical minimum as a warning boundary, not an operating target.
- Specify the retained passband. Decide how much amplitude loss and phase distortion are acceptable at the top of the band. A cutoff frequency by itself is incomplete; you also need the filter order or response, passband ripple, attenuation, and delay.
- Suppress unwanted energy before rate reduction. For an analog signal, place an appropriate low-pass anti-alias filter before the ADC. For a digital-output IMU, configure the internal filter and output data rate so frequencies above the new Nyquist limit are attenuated before samples are discarded.
- Check the actual sensor documentation. Confirm internal bandwidth, selectable filter modes, decimation behavior, group delay, timestamping, and data-ready timing. A setting labelled “low data rate” does not by itself prove that aliasing is controlled.
- Validate on representative motion. Record normal operation and the fastest expected events. Inspect spectra, time-domain peaks, timestamps, and cross-sensor alignment. Validation is an engineering step; it is not implied by a nominal sample-rate number.
How to choose a low-pass cutoff
Set the cutoff high enough to preserve the fastest meaningful motion and low enough to attenuate irrelevant noise and vibration. The cutoff is therefore a compromise between motion bandwidth, aliasing rejection, noise reduction, delay, and phase behavior.
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Define passband and stopband separately
Instead of saying “use a 20 Hz filter,” specify a passband edge, a transition band, and a stopband requirement. For example, you might require little attenuation through 15 Hz, allow the response to roll off from 15 to 25 Hz, and demand a stated attenuation above 25 Hz. The numbers are application-specific; the important point is to describe the full response rather than one label.
A narrower cutoff can reduce high-frequency sensor noise and vibration, but it can also remove real acceleration peaks or delay an event. A wider cutoff preserves more detail but requires a higher sample rate and may pass noise that destabilizes the tracker. Compare candidate filters using the same metrics: retained bandwidth, attenuation outside the band, delay, phase distortion, and computational cost.
Account for delay and phase
Filtering changes timing. A causal finite impulse response (FIR) filter introduces group delay; an infinite impulse response filter can have frequency-dependent phase; an offline zero-phase operation is not available to a real-time controller and may be unsuitable for closed-loop control. Delay matters when detecting impacts, estimating velocity, or aligning an IMU with a camera. Record or calculate the filter latency and include it in the sensor-fusion timing model.
Do not confuse anti-alias filtering with estimation
Analog Devices distinguishes an FIR decimation filter from a Kalman filter. An FIR decimation filter limits bandwidth and reduces the data rate before samples are discarded. A Kalman filter combines a motion model with noisy measurements to estimate state. A Kalman filter does not replace the anti-aliasing function required before sampling or decimation. Use each for the job it performs.
Prevent aliasing in analog and digital sensors
Analog-output sensors
An analog input must be filtered before the ADC. National Instruments describes adding a low-pass filter before the sampler and ADC to limit the input frequency content. If energy above the ADC’s Nyquist frequency reaches the converter, it can fold into the measured band and appear as a false, lower-frequency motion. Once folded, that error cannot be removed reliably by a digital filter.
Place the filter in the analog signal path, verify its cutoff against the selected ADC rate, and account for component tolerances and sensor output impedance. If the ADC has a built-in front-end filter, include its documented response rather than assuming it is an ideal brick-wall filter.
Digital-output IMUs
A digital IMU has already sampled internally, so inspect how its internal anti-alias filter, bandwidth setting, and output data rate interact. Some devices oversample internally and then decimate; others expose several filter paths. Read the register descriptions and response plots for the exact mode. If the device offers only an output-rate setting, do not assume that lowering it automatically attenuates frequencies above the new Nyquist limit.
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Aliasing is especially important in vibration or condition-monitoring applications, where high-frequency mechanical energy can fold into the low-frequency band used by a controller. Analog Devices’ discussion of digital MEMS accelerometers emphasizes checking internal filtering and output-rate configuration rather than relying on the nominal data rate alone.
Decimate only after low-pass filtering
Decimation keeps every Nth sample to reduce data volume. Before that reduction, a low-pass filter must attenuate content above the new Nyquist frequency. Otherwise, those components fold into the retained band. Confirm whether the sensor’s firmware applies the filter before decimation, what passband it uses, and how much delay it adds. If you decimate in your own software, perform the filtering at the higher input rate and document the resulting latency.
Internal rate, output rate, and host-loop timing
A high internal sensor rate does not guarantee coherent samples in your application. The sensor may capture at one clock, package data at another, and be read by an asynchronous host loop. A camera, encoder, and IMU can each have valid timestamps while still being misaligned if the clocks drift or if one stream is buffered.
Questions to answer for every device
- What is the actual conversion or sensing rate?
- What output data rates are available, and are they exact or nominal?
- Does a data-ready signal identify each sample, or does the host poll?
- Are timestamps generated at capture time or packet-delivery time?
- What is the filter group delay at the selected mode?
- Can samples be dropped, repeated, or averaged when the host is late?
- How are sensor and camera clocks synchronized, and how is drift corrected?
Analog Devices’ guidance on MEMS IMU data coherence describes the gap that can arise between sensor capture and slower or asynchronous processing loops. Design the acquisition and synchronization path together with the filter and sample rate. A mathematically adequate rate can still produce poor tracking if samples are stale or misaligned.
Device-specific example: ADIS16576 configuration
Analog Devices gives a concrete ADIS16576 example in which a Bartlett FIR filter with 64 taps per stage reduces the cutoff to approximately 20 Hz. In the same example, averaging 80 sequential samples for each update produces a 50 Hz output data rate. Those values describe that sensor and configuration; they are not a general motion-tracking recommendation.
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The example illustrates why a data sheet must be read as a signal-chain description. The filter bandwidth, averaging or decimation factor, output rate, and resulting delay are linked. Before using a similar mode, verify the current device documentation, confirm the response in the selected register configuration, and determine whether the approximately 20 Hz cutoff preserves the motion your estimator needs.
Compare design choices on the metrics that matter
| Design choice | What it preserves or improves | Primary risk or cost | Checks to perform |
|---|---|---|---|
| Higher sample rate | More bandwidth, better transient representation, and a wider filter transition region | More data, processing, storage, and possibly higher power | Confirm interface throughput, CPU load, buffer depth, and timestamp behavior |
| Lower sample rate with proper low-pass filtering | Lower data volume and reduced out-of-band noise | Real motion above the retained band is removed; inadequate filtering causes aliasing | Verify passband, stopband attenuation, transition width, and pre-decimation filtering |
| Narrower low-pass cutoff | Less irrelevant high-frequency noise | Attenuated motion, greater delay, and possible event smearing | Measure amplitude loss, group delay, and phase at the frequencies used by the estimator |
| Wider low-pass cutoff | More motion detail and sharper events | More noise and a higher required sample rate | Check sensor noise density, alias rejection, and host processing capacity |
| FIR decimation | Controlled bandwidth reduction and predictable linear-phase behavior when designed that way | Filter length can add latency and memory or compute cost | Confirm taps, decimation factor, passband ripple, attenuation, and delay |
| Kalman estimation | Model-based state estimation and noise weighting after measurements are acquired | Incorrect models or covariances can bias or destabilize the estimate | Keep anti-alias filtering separate and tune the estimator for the filtered measurement stream |
Implementation patterns
When you control an analog acquisition chain
- Characterize the sensor output and identify the highest motion frequency to retain.
- Select an ADC rate with Nyquist headroom for the analog filter’s transition band.
- Design or select a low-pass anti-alias filter whose attenuation is specified at and above the ADC Nyquist frequency.
- Check the combined response of the sensor, analog filter, ADC front end, and any digital filter.
- Sample with stable timing and preserve capture timestamps or a deterministic sample counter.
- Inspect recordings for clipping, saturation, mains interference, mechanical resonances, and unexpected high-frequency energy before finalizing the cutoff.
When you use a digital IMU
- List the device’s available output data rates and bandwidth modes from the current data sheet.
- Choose a bandwidth that covers the application’s passband without enabling unnecessary noise.
- Confirm that the internal anti-alias filter is active before any averaging or decimation associated with the selected output rate.
- Read the documented group delay and include it when aligning the IMU with cameras, encoders, or control commands.
- Use the data-ready signal or capture timestamp when possible instead of assuming that host read time equals sample time.
- Log the exact register configuration, output rate, filter mode, firmware version, and dropped-sample count with each test.
When you decimate in software
- Acquire at the higher source rate with a stable clock.
- Design a low-pass filter for the new Nyquist limit and the desired passband.
- Run that filter before taking every Nth sample.
- Measure the filter’s startup transient and steady-state delay.
- Propagate timestamps through the decimation step so downstream code knows the effective sample time.
- Compare the decimated stream with the original stream on motion containing fast events; verify that no unexpected low-frequency components appear.
Validation and troubleshooting
Unexpected low-frequency motion appears
Suspect aliasing if a component appears at a frequency unrelated to the physical motion or changes when you change the sample rate. Check whether the original signal contained energy above the new Nyquist limit and whether filtering occurred before decimation. Increase the source rate, enable the documented anti-alias mode, or move the cutoff lower while preserving the needed motion.
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Fast events look late or smeared
Measure total delay from sensor capture through filtering, packet transport, buffering, and host processing. A long FIR, averaging stage, or asynchronous queue can shift an event even when the sample rate is high. Compensate with documented latency where possible, or choose a lower-delay filter if the application can tolerate its frequency response.
The tracker is noisy at a higher rate
A higher rate does not remove sensor noise; it can expose more of the sensor’s wideband output and increase data volume. Check whether the added bandwidth is needed. If not, use a documented low-pass mode, verify that it does not remove required motion, and tune the estimator for the resulting noise and delay.
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Check capture timestamps, clock drift, data-ready behavior, transport buffering, and filter delay separately. Aligning streams by packet arrival time can create a variable offset. Use a shared timing reference or estimate and correct a stable offset and drift from synchronized events.
Changing output data rate changes the apparent motion
That behavior can indicate a different internal filter, averaging window, or decimation path rather than a simple change in reporting frequency. Compare the device’s bandwidth and delay specifications for each mode, then repeat the same motion while logging the exact configuration.
How to document a defensible setting
Record the application’s highest required frequency, selected sample rate, passband edge, transition band, stopband attenuation, expected delay, output data rate, synchronization method, and validation motion. State whether each number comes from a device specification, a calculated requirement, or an engineering choice. This record makes later changes—such as adding a camera, reducing CPU load, or switching IMUs—auditable instead of guesswork.
The most reliable design is the one that treats sampling, filtering, decimation, and timing as one chain. Start with the motion, preserve only the bandwidth the estimate needs, filter before every rate reduction, and verify the actual device response and timestamps.
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