feat: Scale backward filtering covariance (#1147)

Add an option to scale up the covariance matrix before performing the backward filtering for the Kalman fitter. Scaling this covariance might be necessary to obtain a reasonable smoothed covariance at the end of the backward filtering and shouldn't affect the performance.

Core/include/Acts/TrackFitting/KalmanFitter.hpp

@@ -104,16 +104,16 @@ struct KalmanFitterOptions {
   /// @param rSurface The reference surface for the fit to be expressed at
   /// @param mScattering Whether to include multiple scattering
   /// @param eLoss Whether to include energy loss
-  /// @param rFiltering Whether to run filtering in reversed direction as
-  /// smoothing
+  /// @param rFiltering Whether to run filtering in reversed direction as smoothing
+  /// @param rfScaling Scale factor for the covariance matrix before the backward filtering
   KalmanFitterOptions(const GeometryContext& gctx,
                       const MagneticFieldContext& mctx,
                       std::reference_wrapper<const CalibrationContext> cctx,
                       KalmanFitterExtensions extensions_, LoggerWrapper logger_,
                       const PropagatorPlainOptions& pOptions,
                       const Surface* rSurface = nullptr,
                       bool mScattering = true, bool eLoss = true,
-                      bool rFiltering = false)
+                      bool rFiltering = false, double rfScaling = 1.0)
       : geoContext(gctx),
         magFieldContext(mctx),
         calibrationContext(cctx),
@@ -123,6 +123,7 @@ struct KalmanFitterOptions {
         multipleScattering(mScattering),
         energyLoss(eLoss),
         reversedFiltering(rFiltering),
+        reversedFilteringCovarianceScaling(rfScaling),
         logger(logger_) {}
   /// Contexts are required and the options must not be default-constructible.
   KalmanFitterOptions() = delete;
@@ -152,6 +153,12 @@ struct KalmanFitterOptions {
   /// ReverseFilteringLogic
   bool reversedFiltering = false;
 
+  /// Factor by which the covariance of the input of the reversed filtering is
+  /// scaled. This is only used in the backwardfiltering (if reversedFiltering
+  /// is true or if the ReverseFilteringLogic return true for the track of
+  /// interest)
+  double reversedFilteringCovarianceScaling = 1.0;
+
   /// Logger
   LoggerWrapper logger;
 };
@@ -273,6 +280,9 @@ class KalmanFitter {
     /// Whether run reversed filtering
     bool reversedFiltering = false;
 
+    // Scale the covariance before the reversed filtering
+    double reversedFilteringCovarianceScaling = 1.0;
+
     /// @brief Kalman actor operation
     ///
     /// @tparam propagator_state_t is the type of Propagagor state
@@ -470,9 +480,11 @@ class KalmanFitter {
       auto st = result.fittedStates.getTrackState(result.lastMeasurementIndex);
 
       // Update the stepping state
-      stepper.resetState(state.stepping, st.filtered(), st.filteredCovariance(),
-                         st.referenceSurface(), state.stepping.navDir,
-                         state.options.maxStepSize);
+      stepper.resetState(
+          state.stepping, st.filtered(),
+          reversedFilteringCovarianceScaling * st.filteredCovariance(),
+          st.referenceSurface(), state.stepping.navDir,
+          state.options.maxStepSize);
 
       // For the last measurement state, smoothed is filtered
       st.smoothed() = st.filtered();
@@ -1080,6 +1092,8 @@ class KalmanFitter {
     kalmanActor.multipleScattering = kfOptions.multipleScattering;
     kalmanActor.energyLoss = kfOptions.energyLoss;
     kalmanActor.reversedFiltering = kfOptions.reversedFiltering;
+    kalmanActor.reversedFilteringCovarianceScaling =
+        kfOptions.reversedFilteringCovarianceScaling;
     kalmanActor.extensions = std::move(kfOptions.extensions);
 
     // Run the fitter
@@ -1173,6 +1187,8 @@ class KalmanFitter {
     kalmanActor.multipleScattering = kfOptions.multipleScattering;
     kalmanActor.energyLoss = kfOptions.energyLoss;
     kalmanActor.reversedFiltering = kfOptions.reversedFiltering;
+    kalmanActor.reversedFilteringCovarianceScaling =
+        kfOptions.reversedFilteringCovarianceScaling;
     kalmanActor.extensions = std::move(kfOptions.extensions);
 
     // Set the surface sequence

Tests/UnitTests/Core/TrackFitting/KalmanFitterTests.cpp

@@ -106,6 +106,7 @@ BOOST_AUTO_TEST_CASE(ZeroFieldWithSurfaceForward) {
 
   // reverse filtering instead of smoothing
   kfOptions.reversedFiltering = true;
+  kfOptions.reversedFilteringCovarianceScaling = 100.0;
   expected_reversed = true;
   expected_smoothed = false;
   tester.test_ZeroFieldWithSurfaceForward(kfZero, kfOptions, start, rng,
@@ -125,6 +126,7 @@ BOOST_AUTO_TEST_CASE(ZeroFieldWithSurfaceBackward) {
 
   // reverse filtering instead of smoothing
   kfOptions.reversedFiltering = true;
+  kfOptions.reversedFilteringCovarianceScaling = 100.0;
   expected_reversed = true;
   expected_smoothed = false;
   tester.test_ZeroFieldWithSurfaceBackward(
@@ -190,6 +192,7 @@ BOOST_AUTO_TEST_CASE(ZeroFieldWithReverseFiltering) {
         .connect<&TestReverseFilteringLogic::operator()>(&trfl);
 
     kfOptions.reversedFiltering = reverse;
+    kfOptions.reversedFilteringCovarianceScaling = 100.0;
 
     tester.test_ZeroFieldWithReverseFiltering(
         kfZero, kfOptions, start, rng, expected_reversed, expected_smoothed);