Implements a Kalman filter for regularly sampled time series data with automatic parameter selection based on sampling rate. The filter handles missing values (NA) and provides noise reduction while preserving real signal changes.
Usage
filter_kalman(
measurements,
sampling_rate,
base_Q = NULL,
R = NULL,
initial_state = NULL,
initial_P = NULL
)Arguments
- measurements
Numeric vector containing the measurements to be filtered.
- sampling_rate
Numeric value specifying the sampling rate in Hz (frames per second).
- base_Q
Optional. Process variance. If NULL, automatically calculated based on sampling_rate. Represents expected rate of change in the true state.
- R
Optional. Measurement variance. If NULL, defaults to 0.1. Represents the noise level in your measurements.
- initial_state
Optional. Initial state estimate. If NULL, uses first non-NA measurement.
- initial_P
Optional. Initial state uncertainty. If NULL, calculated based on sampling_rate.
Details
The function implements a simple Kalman filter with a constant position model. When parameters are not explicitly provided, they are automatically configured based on the sampling rate:
base_Q scales inversely with sampling rate (base_Q ≈ 0.15/sampling_rate)
R defaults to 0.1 (assuming moderate measurement noise)
initial_P scales with sampling rate uncertainty
Missing values (NA) are handled by relying on the prediction step without measurement updates.
Note
Parameter selection guidelines:
Increase R or decrease base_Q for smoother output
Decrease R or increase base_Q for more responsive output
For high-frequency data (>100 Hz), consider reducing base_Q
If you know your sensor's noise characteristics, set R to the square of the standard deviation
Examples
# Basic usage with 60 Hz data
measurements <- c(1, 1.1, NA, 0.9, 1.2, NA, 0.8, 1.1)
filtered <- filter_kalman(measurements, sampling_rate = 60)
# Custom parameters for more aggressive filtering
filtered_custom <- filter_kalman(measurements,
sampling_rate = 60,
base_Q = 0.001,
R = 0.2)