diff --git a/pyrecest/distributions/__init__.py b/pyrecest/distributions/__init__.py
index 72efac1ad..771e942f5 100644
--- a/pyrecest/distributions/__init__.py
+++ b/pyrecest/distributions/__init__.py
@@ -84,6 +84,7 @@
     SineSkewedWrappedCauchyDistribution,
     SineSkewedWrappedNormalDistribution,
 )
+from .circle.piecewise_constant_distribution import PiecewiseConstantDistribution  # noqa: F401
 from .circle.von_mises_distribution import VonMisesDistribution
 from .circle.wrapped_cauchy_distribution import WrappedCauchyDistribution
 from .circle.wrapped_laplace_distribution import WrappedLaplaceDistribution
diff --git a/pyrecest/distributions/circle/piecewise_constant_distribution.py b/pyrecest/distributions/circle/piecewise_constant_distribution.py
new file mode 100644
index 000000000..c3b8755e6
--- /dev/null
+++ b/pyrecest/distributions/circle/piecewise_constant_distribution.py
@@ -0,0 +1,197 @@
+# pylint: disable=no-name-in-module,no-member,redefined-builtin
+from pyrecest.backend import mod, pi, array, mean, floor, zeros, exp, sum, log, random
+
+from .abstract_circular_distribution import AbstractCircularDistribution
+
+
+class PiecewiseConstantDistribution(AbstractCircularDistribution):
+    """Piecewise constant (i.e. discrete) circular distribution, similar to a histogram.
+
+    The circle [0, 2*pi) is divided into n equal intervals, each with a constant
+    probability density weight.
+
+    Gerhard Kurz, Florian Pfaff, Uwe D. Hanebeck,
+    Discrete Recursive Bayesian Filtering on Intervals and the Unit Circle
+    Proceedings of the 2016 IEEE International Conference on Multisensor Fusion
+    and Integration for Intelligent Systems (MFI 2016),
+    Baden-Baden, Germany, September 2016.
+    """
+
+    def __init__(self, w):
+        """Initialize with a weight vector that is automatically normalized.
+
+        Parameters
+        ----------
+        w : array_like, shape (n,)
+            Weight for each interval (will be normalized to form a valid pdf).
+        """
+        AbstractCircularDistribution.__init__(self)
+        w = array(w, dtype=float).ravel()
+        assert w.ndim == 1 and w.shape[0] > 0
+        self.w = w / (mean(w) * 2.0 * pi)
+
+    def pdf(self, xs):
+        """Evaluate the pdf at each point in xs.
+
+        Parameters
+        ----------
+        xs : array_like, shape (n,)
+            Points at which to evaluate the pdf.
+
+        Returns
+        -------
+        p : ndarray, shape (n,)
+            Pdf values at each point.
+        """
+        assert xs.ndim == 1
+        n_intervals = len(self.w)
+        xs_mod = array(mod(xs, 2.0 * pi), dtype=float)
+        idx = array(
+            [min(int(floor(x / (2.0 * pi) * n_intervals)), n_intervals - 1) for x in xs_mod]
+        )
+        return self.w[idx]
+
+    def trigonometric_moment(self, n):
+        """Calculate the n-th trigonometric moment analytically.
+
+        Parameters
+        ----------
+        n : int
+            Moment order.
+
+        Returns
+        -------
+        m : complex
+            n-th trigonometric moment.
+        """
+        if n == 0:
+            return 1.0 + 0j
+        num = len(self.w)
+        interv = zeros(num, dtype=complex)
+        for j in range(1, num + 1):
+            left = PiecewiseConstantDistribution.left_border(j, num)
+            r = PiecewiseConstantDistribution.right_border(j, num)
+            c = PiecewiseConstantDistribution.interval_center(j, num)
+            w_j = float(self.pdf(array([c]))[0])
+            interv[j - 1] = w_j * (exp(1j * n * r) - exp(1j * n * left))
+        return complex(-1j / n * sum(interv))
+
+    def entropy(self):
+        """Calculate the entropy analytically.
+
+        Returns
+        -------
+        e : float
+            Entropy of the distribution.
+        """
+        n = len(self.w)
+        return float(-2.0 * pi / n * sum(self.w * log(self.w)))
+
+    def sample(self, n):
+        """Draw n random samples from the distribution.
+
+        Parameters
+        ----------
+        n : int
+            Number of samples to draw.
+
+        Returns
+        -------
+        samples : ndarray, shape (n,)
+            Samples in [0, 2*pi).
+        """
+        num_intervals = len(self.w)
+        interval_width = 2.0 * pi / num_intervals
+        # Each interval has probability w[j] * interval_width, which sums to 1 by
+        # construction. Divide by sum anyway to guard against floating-point drift.
+        interval_probs = self.w * interval_width
+        interval_probs /= interval_probs.sum()
+        interval_indices = random.choice(num_intervals, size=n, p=interval_probs)
+        return interval_indices * interval_width + random.uniform(
+            0.0, interval_width, size=n
+        )
+
+    @staticmethod
+    def left_border(m, n):
+        """Left border of the m-th interval (1-indexed) for n total intervals.
+
+        Parameters
+        ----------
+        m : int
+            Interval index (1-indexed).
+        n : int
+            Total number of intervals.
+
+        Returns
+        -------
+        float
+            Left border of the m-th interval.
+        """
+        assert 1 <= m <= n
+        return 2.0 * pi / n * (m - 1)
+
+    @staticmethod
+    def right_border(m, n):
+        """Right border of the m-th interval (1-indexed) for n total intervals.
+
+        Parameters
+        ----------
+        m : int
+            Interval index (1-indexed).
+        n : int
+            Total number of intervals.
+
+        Returns
+        -------
+        float
+            Right border of the m-th interval.
+        """
+        assert 1 <= m <= n
+        return 2.0 * pi / n * m
+
+    @staticmethod
+    def interval_center(m, n):
+        """Center of the m-th interval (1-indexed) for n total intervals.
+
+        Parameters
+        ----------
+        m : int
+            Interval index (1-indexed).
+        n : int
+            Total number of intervals.
+
+        Returns
+        -------
+        float
+            Center of the m-th interval.
+        """
+        assert 1 <= m <= n
+        return 2.0 * pi / n * (m - 0.5)
+
+    @staticmethod
+    def calculate_parameters_numerically(pdf_func, n):
+        """Calculate weights by numerically integrating a given pdf over each interval.
+
+        Parameters
+        ----------
+        pdf_func : callable
+            Pdf of a circular density; accepts a 1-D array and returns a 1-D array.
+        n : int
+            Number of discretization intervals.
+
+        Returns
+        -------
+        w : ndarray, shape (n,)
+            Weights of the corresponding PiecewiseConstantDistribution.
+        """
+        from scipy.integrate import quad  # pylint: disable=import-outside-toplevel
+
+        assert n >= 1
+        w = zeros(n)
+        for j in range(1, n + 1):
+            left = PiecewiseConstantDistribution.left_border(j, n)
+            r = PiecewiseConstantDistribution.right_border(j, n)
+            w[j - 1] = quad(
+                lambda x: float(pdf_func(array([x]))), left, r
+            )[0]
+        return w
diff --git a/pyrecest/tests/distributions/test_piecewise_constant_distribution.py b/pyrecest/tests/distributions/test_piecewise_constant_distribution.py
new file mode 100644
index 000000000..f65e9f61e
--- /dev/null
+++ b/pyrecest/tests/distributions/test_piecewise_constant_distribution.py
@@ -0,0 +1,116 @@
+import unittest
+
+import numpy.testing as npt
+
+# pylint: disable=no-name-in-module,no-member,redefined-builtin
+from pyrecest.backend import linspace, sum, exp, log, mean, pi, array
+from pyrecest.distributions.circle.piecewise_constant_distribution import (
+    PiecewiseConstantDistribution,
+)
+from pyrecest.distributions.circle.wrapped_normal_distribution import (
+    WrappedNormalDistribution,
+)
+
+
+class PiecewiseConstantDistributionTest(unittest.TestCase):
+    def setUp(self):
+        self.w = array([1, 2, 3, 4, 5, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1], dtype=float)
+        self.normal = 1.0 / (2.0 * pi * mean(self.w))
+        self.dist = PiecewiseConstantDistribution(self.w)
+
+    def test_pdf(self):
+        npt.assert_allclose(
+            self.dist.pdf(array([0.0])), array([1 * self.normal]), rtol=1e-10
+        )
+        npt.assert_allclose(
+            self.dist.pdf(array([4.2])), array([5 * self.normal]), rtol=1e-10
+        )
+        npt.assert_allclose(
+            self.dist.pdf(array([10.9])), array([4 * self.normal]), rtol=1e-10
+        )
+
+    def test_integral_normalized(self):
+        """Verify the distribution integrates to 1 via the exact sum."""
+        n = len(self.dist.w)
+        npt.assert_allclose(
+            sum(self.dist.w) * (2.0 * pi / n), 1.0, rtol=1e-10
+        )
+
+    def test_integral_partial(self):
+        """Verify partial integrals sum to 1 using a fine grid."""
+        M = 1_000_000
+        xs = linspace(0, 2.0 * pi, M, endpoint=False)
+        dx = 2.0 * pi / M
+        pdf_vals = self.dist.pdf(xs)
+        first_half = sum(pdf_vals[xs < pi]) * dx
+        second_half = sum(pdf_vals[xs >= pi]) * dx
+        npt.assert_allclose(first_half + second_half, 1.0, rtol=1e-4)
+
+    def test_trigonometric_moment(self):
+        """Verify analytical trigonometric moments using a fine-grid reference."""
+        M = 1_000_000
+        xs = linspace(0, 2.0 * pi, M, endpoint=False)
+        pdf_vals = self.dist.pdf(xs)
+        dx = 2.0 * pi / M
+        for n_moment in [1, 2, 3]:
+            expected = sum(pdf_vals * exp(1j * n_moment * xs)) * dx
+            with self.subTest(n=n_moment):
+                npt.assert_allclose(
+                    self.dist.trigonometric_moment(n_moment), expected, rtol=1e-4
+                )
+
+    def test_interval_borders(self):
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.left_border(1, 2), 0.0 * 2.0 * pi
+        )
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.interval_center(1, 2),
+            1.0 / 4.0 * 2.0 * pi,
+        )
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.right_border(1, 2),
+            1.0 / 2.0 * 2.0 * pi,
+        )
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.left_border(2, 2),
+            1.0 / 2.0 * 2.0 * pi,
+        )
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.interval_center(2, 2),
+            3.0 / 4.0 * 2.0 * pi,
+        )
+        self.assertAlmostEqual(
+            PiecewiseConstantDistribution.right_border(2, 2), 1.0 * 2.0 * pi
+        )
+
+    def test_calculate_parameters_numerically(self):
+        """More samples should yield better moment matching."""
+        wn = WrappedNormalDistribution(array(2.0), array(1.3))
+        w1 = PiecewiseConstantDistribution.calculate_parameters_numerically(wn.pdf, 40)
+        w2 = PiecewiseConstantDistribution.calculate_parameters_numerically(wn.pdf, 45)
+        w3 = PiecewiseConstantDistribution.calculate_parameters_numerically(wn.pdf, 50)
+        p1 = PiecewiseConstantDistribution(w1)
+        p2 = PiecewiseConstantDistribution(w2)
+        p3 = PiecewiseConstantDistribution(w3)
+        delta1 = abs(wn.trigonometric_moment(1) - p1.trigonometric_moment(1))
+        delta2 = abs(wn.trigonometric_moment(1) - p2.trigonometric_moment(1))
+        delta3 = abs(wn.trigonometric_moment(1) - p3.trigonometric_moment(1))
+        self.assertLessEqual(delta2, delta1)
+        self.assertLessEqual(delta3, delta2)
+
+    def test_entropy(self):
+        """Verify analytical entropy against the direct formula."""
+        w = self.dist.w
+        n = len(w)
+        expected = -2.0 * pi / n * sum(w * log(w))
+        npt.assert_allclose(self.dist.entropy(), expected, rtol=1e-10)
+
+    def test_sample(self):
+        samples = self.dist.sample(100)
+        self.assertEqual(len(samples), 100)
+        self.assertTrue(all(samples >= 0.0))
+        self.assertTrue(all(samples < 2.0 * pi))
+
+
+if __name__ == "__main__":
+    unittest.main()