[sample] simplify vehicle location

FrancoisCarouge · Oct 5, 2024 · 9f08e18 · 9f08e18
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diff --git a/README.md b/README.md
@@ -70,14 +70,13 @@ Example from the [2-dimension vehicle location, velocity, and acceleration vehic
                          {0., 0., 0., 500., 0., 0.},
                          {0., 0., 0., 0., 500., 0.},
                          {0., 0., 0., 0., 0., 500.}},
-    process_uncertainty{[]() -> matrix<6, 6> {
-      return 0.2 * 0.2 * matrix<6, 6>{{0.25, 0.5, 0.5, 0., 0., 0.},
+    process_uncertainty{0.2 * 0.2 *
+                        matrix<6, 6>{{0.25, 0.5, 0.5, 0., 0., 0.},
                                       {0.5, 1., 1., 0., 0., 0.},
                                       {0.5, 1., 1., 0., 0., 0.},
                                       {0., 0., 0., 0.25, 0.5, 0.5},
                                       {0., 0., 0., 0.5, 1., 1.},
-                                      {0., 0., 0., 0.5, 1., 1.}};
-    }()},
+                                      {0., 0., 0., 0.5, 1., 1.}}},
     output_uncertainty{{9., 0.}, {0., 9.}},
     output_model{{1., 0., 0., 0., 0., 0.},
                  {0., 0., 0., 1., 0., 0.}},

diff --git a/sample/kf_6x2x0_vehicle_location.cpp b/sample/kf_6x2x0_vehicle_location.cpp
@@ -60,14 +60,13 @@ using state = fcarouge::state<vector<6>>;
                            {0., 0., 0., 0., 0., 500.}},
       // The process uncertainty noise matrix Q, constant, computed in place,
       // with  random acceleration standard deviation: σa = 0.2 m.s^-2.
-      process_uncertainty{[]() -> matrix<6, 6> {
-        return 0.2 * 0.2 * matrix<6, 6>{{0.25, 0.5, 0.5, 0., 0., 0.},
-                                        {0.5, 1., 1., 0., 0., 0.},
-                                        {0.5, 1., 1., 0., 0., 0.},
-                                        {0., 0., 0., 0.25, 0.5, 0.5},
-                                        {0., 0., 0., 0.5, 1., 1.},
-                                        {0., 0., 0., 0.5, 1., 1.}};
-      }()},
+      process_uncertainty{0.2 * 0.2 *
+                          matrix<6, 6>{{0.25, 0.5, 0.5, 0., 0., 0.},
+                                       {0.5, 1., 1., 0., 0., 0.},
+                                       {0.5, 1., 1., 0., 0., 0.},
+                                       {0., 0., 0., 0.25, 0.5, 0.5},
+                                       {0., 0., 0., 0.5, 1., 1.},
+                                       {0., 0., 0., 0.5, 1., 1.}}},
       // The output uncertainty matrix R. Assume that the x and y measurements
       // are uncorrelated, i.e. error in the x coordinate measurement doesn't
       // depend on the error in the y coordinate measurement. In real-life