Kalman v0.1.0 Kalman View Source

Implements the Kalman Filter as filter/1.

filter/1 is meant to be used recursively

Link to this section Summary

Functions

filter/1 takes a tuple {measurement, current_state_estimate, current_prop_estimate}, applies the prediction step, observation step and update step of the Kalman Filter.

Link to this section Functions

filter(arg)

filter(input_tuple :: {number(), number(), number()}) :: map()

filter/1 takes a tuple {measurement, current_state_estimate, current_prop_estimate}, applies the prediction step, observation step and update step of the Kalman Filter.

It then returns the updated values in a map on the form %{current_state_estimate: current_state_estimate, current_prop_estimate: current_prop_estimate}.

The returned map can then be used with a new measured value, recursively.

Examples

iex> l = [-77, -76, -63, -74, -66, -75, -77, -63, -77, -63]
iex> Enum.scan(l, %{current_prop_estimate: 1, current_state_estimate: -55}, fn measurement, %{current_prop_estimate: cpe, current_state_estimate: cse} -> Kalman.estimate({measurement, cse, cpe})  end)
[%{current_prop_estimate: 0.5012468827930174, current_state_estimate: -66.02743142144638}, %{current_prop_estimate: 0.33609821110752397, current_state_estimate: -69.37919388084535}, %{current_prop_estimate: 0.25434245477505607, current_state_estimate: -67.75669404970513}, %{current_prop_estimate: 0.20593481446742765, current_state_estimate: -69.04240810224249}, %{current_prop_estimate: 0.17419171696719063, current_state_estimate: -68.51244581119798}, %{current_prop_estimate: 0.15196147868818213, current_state_estimate: -69.49830413879805}, %{current_prop_estimate: 0.1356669876910271, current_state_estimate: -70.51603661886156}, %{current_prop_estimate: 0.12331994281325659, current_state_estimate: -69.58915941284121}, %{current_prop_estimate: 0.1137265574632268, current_state_estimate: -70.43196880072755}, %{current_prop_estimate: 0.10612652097260095, current_state_estimate: -69.64323980792942}]