Documentation updates

dstl · Oct 19, 2023 · c9d980c · c9d980c
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diff --git a/docs/source/stonesoup.hypothesiser.rst b/docs/source/stonesoup.hypothesiser.rst
@@ -7,6 +7,12 @@ Hypothesiser
 .. automodule:: stonesoup.hypothesiser.base
     :show-inheritance:
 
+Simple
+------
+
+.. automodule:: stonesoup.hypothesiser.simple
+    :show-inheritance:
+
 Distance
 --------
 

diff --git a/stonesoup/predictor/particle.py b/stonesoup/predictor/particle.py
@@ -388,16 +388,22 @@ def get_detections(prior):
 class SMCPHDPredictor(Predictor):
     """SMC-PHD Predictor class
 
-    Sequential Monte-Carlo (SMC) PHD predictor implementation, based on [1]_.
+    Sequential Monte Carlo (SMC) PHD predictor implementation, based on [#]_.
 
-    Notes
-    -----
-    - It is assumed that the proposal distribution is the same as the dynamics
-    - Target "spawing" is not implemented
+    .. note::
 
-     .. [1] Ba-Ngu Vo, S. Singh and A. Doucet, "Sequential Monte Carlo Implementation of the
+        - It is assumed that the proposal distribution is the same as the dynamics
+        - Target "spawing" is not implemented
+
+    Parameters
+    ----------
+
+    References
+    ----------
+     .. [#] Ba-Ngu Vo, S. Singh and A. Doucet, "Sequential Monte Carlo Implementation of the
             PHD Filter for Multi-target Tracking," Sixth International Conference of Information
             Fusion, 2003. Proceedings of the, 2003, pp. 792-799, doi: 10.1109/ICIF.2003.177320.
+
     """
     prob_death: Probability = Property(
         doc="The probability of death")
@@ -433,7 +439,6 @@ def predict(self, prior, timestamp=None, **kwargs):
         -------
         : :class:`~.ParticleStatePrediction`
             The predicted state
-
         """
         num_samples = len(prior)
         log_prior_weights = prior.log_weight

diff --git a/stonesoup/updater/particle.py b/stonesoup/updater/particle.py
@@ -525,19 +525,24 @@ def _log_space_product(A, B):
 class SMCPHDUpdater(ParticleUpdater):
     """ SMC-PHD updater class
 
-    Sequential Monte-Carlo (SMC) PHD updater implementation, based on [1]_ .
-
-    Notes
-    -----
-    - It is assumed that the proposal distribution is the same as the dynamics
-    - Target "spawing" is not implemented
-
-    .. [1] Ba-Ngu Vo, S. Singh and A. Doucet, "Sequential Monte Carlo Implementation of the
-            PHD Filter for Multi-target Tracking," Sixth International Conference of Information
-            Fusion, 2003. Proceedings of the, 2003, pp. 792-799, doi: 10.1109/ICIF.2003.177320.
-    .. [2]  P. Horridge and S. Maskell,  “Using a probabilistic hypothesis density filter to
-            confirm tracks in a multi-target environment,” in 2011 Jahrestagung der Gesellschaft
-            fr Informatik, October 2011.
+    Sequential Monte Carlo (SMC) PHD updater implementation, based on [#]_ and [#]_.
+
+    .. note::
+
+        - It is assumed that the proposal distribution is the same as the dynamics
+        - Target "spawing" is not implemented
+
+    Parameters
+    ----------
+
+    References
+    ----------
+    .. [#] Ba-Ngu Vo, S. Singh and A. Doucet, "Sequential Monte Carlo Implementation of the
+           PHD Filter for Multi-target Tracking," Sixth International Conference of Information
+           Fusion, 2003. Proceedings of the, 2003, pp. 792-799, doi: 10.1109/ICIF.2003.177320.
+    .. [#] P. Horridge and S. Maskell,  “Using a probabilistic hypothesis density filter to
+           confirm tracks in a multi-target environment,” in 2011 Jahrestagung der Gesellschaft
+           fr Informatik, October 2011.
     """
     prob_detect: Probability = Property(
         default=Probability(0.85),