diff --git a/src/pyrecest/filters/interacting_multiple_model_filter.py b/src/pyrecest/filters/interacting_multiple_model_filter.py
index 4ed276933..eaf6d4b0e 100644
--- a/src/pyrecest/filters/interacting_multiple_model_filter.py
+++ b/src/pyrecest/filters/interacting_multiple_model_filter.py
@@ -13,6 +13,7 @@
     array,
     asarray,
     diag,
+    diagonal,
     empty,
     exp,
     eye,
@@ -612,12 +613,13 @@ def _log_linear_measurement_likelihood(
             @ measurement_matrix.T
             + meas_noise
         )
-        det_value = float(linalg.det(innovation_covariance))
-        if det_value <= 0.0:
+        try:
+            cholesky_factor = linalg.cholesky(innovation_covariance)
+        except (np.linalg.LinAlgError, RuntimeError, ValueError) as exc:
             raise ValueError(
                 "Innovation covariance must be positive definite to evaluate the IMM likelihood."
-            )
-        logdet = float(log(array(det_value)))
+            ) from exc
+        logdet = 2.0 * float(log(diagonal(cholesky_factor)).sum())
         mahalanobis_distance = float(
             innovation.T @ linalg.solve(innovation_covariance, innovation)
         )
diff --git a/tests/filters/test_interacting_multiple_model_filter.py b/tests/filters/test_interacting_multiple_model_filter.py
index 585fce8da..386f55309 100644
--- a/tests/filters/test_interacting_multiple_model_filter.py
+++ b/tests/filters/test_interacting_multiple_model_filter.py
@@ -1,4 +1,5 @@
 import copy
+import math
 import unittest
 
 import numpy.testing as npt
@@ -140,6 +141,27 @@ def test_external_mode_probability_update(self):
             array([0.8807970779778823, 0.11920292202211755]),
         )
 
+    def test_linear_likelihood_handles_positive_definite_determinant_underflow(self):
+        predicted_state = GaussianDistribution(
+            array([0.0, 0.0]),
+            array([[0.0, 0.0], [0.0, 0.0]]),
+            check_validity=False,
+        )
+        measurement_noise = array([[1e-200, 0.0], [0.0, 1e-200]])
+
+        log_likelihood = (
+            InteractingMultipleModelFilter._log_linear_measurement_likelihood(
+                array([0.0, 0.0]),
+                predicted_state,
+                eye(2),
+                measurement_noise,
+            )
+        )
+
+        expected = -math.log(2.0 * math.pi) + 200.0 * math.log(10.0)
+        self.assertTrue(math.isfinite(log_likelihood))
+        self.assertAlmostEqual(log_likelihood, expected, places=12)
+
     def test_rejects_nonfinite_transition_matrix_and_mode_probabilities(self):
         filter_bank = [
             MockGaussianFilter(GaussianDistribution(array([0.0]), array([[1.0]]))),