[filter] transition and observation runtime change support

include/fcarouge/internal/kalman.tpp

@@ -224,6 +224,16 @@ kalman<Filter>::s() const -> const innovation_uncertainty & {
   return filter.s;
 }
 
+template <typename Filter>
+inline constexpr void kalman<Filter>::transition(const auto &callable) {
+  filter.transition = callable;
+}
+
+template <typename Filter>
+inline constexpr void kalman<Filter>::observation(const auto &callable) {
+  filter.observation = callable;
+}
+
 template <typename Filter>
 inline constexpr void kalman<Filter>::update(const auto &...arguments) {
   filter.update(arguments...);

include/fcarouge/kalman.hpp

@@ -494,6 +494,34 @@ class kalman final : public internal::conditional_member_types<Filter> {
   inline constexpr auto s() const -> const innovation_uncertainty &;
   //! @}
 
+  //! @brief Sets the extended state transition function f(x).
+  //!
+  //! @param callable The copied target Callable object (function object,
+  //! pointer to function, reference to function, pointer to member function, or
+  //! pointer to data member) that will be called to compute the next state X on
+  //! prediction steps of expression `state(const state &, const input &, const
+  //! PredictionTypes &...)`. The default function `f(x) = F * X` is suitable
+  //! for linear systems. For non-linear system, or extended filter, implement a
+  //! linearization of the transition function f and the state transition F
+  //! matrix is the Jacobian of the state transition function.
+  //!
+  //! @complexity Constant.
+  inline constexpr void transition(const auto &callable);
+
+  //! @brief Sets the extended state observation function h(x).
+  //!
+  //! @param callable The copied target Callable object (function object,
+  //! pointer to function, reference to function, pointer to member function, or
+  //! pointer to data member) that will be called to compute the observation Z
+  //! on update steps of expression `output(const state &, const UpdateTypes
+  //! &...arguments)`. The default function `h(x) = H * X` is suitable for
+  //! linear systems. For non-linear system, or extended filter, the client
+  //! implements a linearization of the observation function hand the state
+  //! observation H matrix is the Jacobian of the state observation function.
+  //!
+  //! @complexity Constant.
+  inline constexpr void observation(const auto &callable);
+
   //! @name Public Filtering Member Functions
   //! @{