Module filter::gh

Provides implementations of and related to the g-h and g-h-k filter.

Structs

GHFilter	A g-h filter.
GHKFilter	A g-h-k filter.

Functions

benedict_bornder_constants	Computes the g,h constants for a Benedict-Bornder filter, which minimizes transient errors for a g-h filter. Returns the values g,h for a specified g. Strictly speaking, only h is computed, g is returned unchanged. The default formula for the Benedict-Bordner allows ringing. We can "nearly" critically damp it; ringing will be reduced, but not entirely eliminated at the cost of reduced performance.
critical_damping_parameters_order_three	Computes values for g, h and k for a critically damped filter. The idea here is to create a filter that reduces the influence of old data as new data comes in. This allows the filter to track a moving target better. This goes by different names. It may be called the discounted least-squares g-h filter, a fading-memory polynomal filter of order 1, or a critically damped g-h filter.
critical_damping_parameters_order_two	Computes values for g and h for a critically damped filter. The idea here is to create a filter that reduces the influence of old data as new data comes in. This allows the filter to track a moving target better. This goes by different names. It may be called the discounted least-squares g-h filter, a fading-memory polynomal filter of order 1, or a critically damped g-h filter.
least_squares_parameters	An order 1 least squared filter can be computed by a g-h filter by varying g and h over time according to the formulas below, where the first measurement is at n=0, the second is at n=1, and so on:
optimal_noise_smoothing	Returns g, h, k parameters for optimal smoothing of noise for a given value of g. This is due to Polge and Bhagavan.