Add normalisation post constraint function in BernoulliParticleUpdater

stonesoup/updater/particle.py

@@ -529,6 +529,9 @@ def update(self, hypotheses, **kwargs):
         if self.constraint_func is not None:
             part_indx = self.constraint_func(updated_state)
             updated_state.log_weight[part_indx] = -1*np.inf
+            if not any(hypotheses):
+                updated_state.log_weight = copy.copy(updated_state.log_weight)
+            updated_state.log_weight -= logsumexp(updated_state.log_weight)
 
         # Resampling
         if self.resampler is not None:

stonesoup/updater/tests/test_particle.py

@@ -35,6 +35,12 @@ def dummy_constraint_function(particles):
     return part_indx
 
 
+@pytest.fixture(params=[True, False])
+def constraint_func(request):
+    if request.param:
+        return dummy_constraint_function
+
+
 @pytest.fixture(params=(
         ParticleUpdater,
         partial(ParticleUpdater, resampler=SystematicResampler()),
@@ -102,7 +108,7 @@ def test_particle(updater):
             assert np.allclose(updated_state.mean, StateVectors([[15.0], [20.0]]), rtol=5e-2)
 
 
-def test_bernoulli_particle():
+def test_bernoulli_particle(constraint_func):
     timestamp = datetime.datetime.now()
     timediff = 2
     new_timestamp = timestamp + datetime.timedelta(seconds=timediff)
@@ -173,7 +179,8 @@ def test_bernoulli_particle():
                                        clutter_rate=2,
                                        clutter_distribution=1/10,
                                        nsurv_particles=9,
-                                       detection_probability=detection_probability)
+                                       detection_probability=detection_probability,
+                                       constraint_func=constraint_func)
 
     hypotheses = MultipleHypothesis(
         [SingleHypothesis(prediction, detection) for detection in detections])