diff --git a/pyrecest/filters/hyperspherical_ukf.py b/pyrecest/filters/hyperspherical_ukf.py
new file mode 100644
index 000000000..267357128
--- /dev/null
+++ b/pyrecest/filters/hyperspherical_ukf.py
@@ -0,0 +1,61 @@
+import numpy as np
+from pyrecest.distributions import GaussianDistribution
+from .unscented_kalman_filter import UnscentedKalmanFilter
+from .abstract_hyperspherical_filter import AbstractHyperSphericalFilter
+
+
+class HypersphericalUKF(AbstractHyperSphericalFilter):
+    def __init__(self):
+        # Constructor
+        self.state = GaussianDistribution(1, 1)
+        self.ukf = UnscentedKalmanFilter(dim_x=1, dim_z=1, dt=1)
+
+    @property
+    def filter_state(self):
+        return self._filter_state
+
+    @filter_state.setter
+    def filter_state(self, new_state):
+        # Sets the current system state
+        #
+        # Parameters:
+        #   new_state (GaussianDistribution)
+        #       new state (1D Gaussian)
+        if not isinstance(new_state, GaussianDistribution):
+            new_state = new_state.toGaussian()
+        self._filter_state = new_state
+
+    def predictNonlinear(self, f, gaussSys):
+        # Predicts assuming a nonlinear system model, i.e.,
+        # x(k+1) = f(x(k)) + w(k)
+        # where w(k) is additive noise given by gaussSys.
+        #
+        # Parameters:
+        #   f (function handle)
+        #       function from R^(d-1) to R^(d-1)
+        #   gaussSys (GaussianDistribution)
+        #       distribution of additive noise (warning: mean is
+        #       ignored)
+        assert callable(f)
+        if not isinstance(gaussSys, GaussianDistribution):
+            gaussSys = gaussSys.toGaussian()
+
+        def g(x):
+            x = x / np.linalg.norm(x)
+            y = f(x)
+            y = y / np.linalg.norm(y)
+            return y
+
+        self.ukf.x = self.state.mu
+        self.ukf.P = self.state.C
+        self.ukf.predict(fx=g, Q=gaussSys.C)
+        self.state.mu, self.state.C = self.ukf.x, self.ukf.P
+
+        # normalize mean
+        if np.linalg.norm(self.state.mu) != 0:
+            self.state.mu = self.state.mu / np.linalg.norm(self.state.mu)
+        else:
+            raise ValueError('mu was 0')
+        
+    def getEstimateMean(self):
+        return self.state.mu
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