diff --git a/lisainstrument/instrument.py b/lisainstrument/instrument.py
index 2928d0853c1633344d610a446e7f5f5ea564714c..1893ecfa39fde3009c22be8633acdd55d31c80ec 100644
--- a/lisainstrument/instrument.py
+++ b/lisainstrument/instrument.py
@@ -45,6 +45,7 @@ class Instrument:
                  clock_freqquaddrifts=None, clockinv_tolerance=1E-10, clockinv_maxiter=5,
                  # Optical pathlength noises
                  backlink_asds=3E-12, backlink_fknees=2E-3, testmass_asds=2.4E-15, testmass_fknees=0.4E-3,
+                 oms_asds=(6.35E-12, 1.25E-11, 1.42E-12, 3.38E-12, 3.32E-12, 7.90E-12), oms_fknees=2E-3,
                  # Pseudo-ranging
                  ranging_biases=0, ranging_asds=3E-9,
                  # Physics simulation sampling and filtering
@@ -80,7 +81,10 @@ class Instrument:
             backlink_asds: dictionary of amplitude spectral densities for backlink noise [m/sqrt(Hz)]
             backlink_fknees: dictionary of cutoff frequencied for backlink noise [Hz]
             testmass_asds: dictionary of amplitude spectral densities for test-mass noise [ms^(-2)/sqrt(Hz)]
-            testmass_fknees: dictionary of cutoff frequencied for test-mass noise [Hz]
+            testmass_fknees: dictionary of cutoff frequencies for test-mass noise [Hz]
+            oms_asds: tuple of dictionaries of amplitude spectral densities for OMS noise [m/sqrt(Hz)],
+                ordered as (isc_carrier, isc_usb, tm_carrier, tm_usb, ref_carrier, ref_usb)
+            oms_fknees: dictionary of cutoff frequencies for OMS noise
             ranging_biases: dictionary of ranging noise bias [s]
             ranging_asds: dictionary of ranging noise amplitude spectral densities [s/sqrt(Hz)]
             physics_upsampling: ratio of sampling frequencies for physics vs. measurement simulation
@@ -163,11 +167,18 @@ class Instrument:
         self.ranging_biases = ForEachMOSA(ranging_biases)
         self.ranging_asds = ForEachMOSA(ranging_asds)
 
-        # Backlink and test-mass acceleration noise
+        # Backlink, OMS and test-mass acceleration noise
         self.backlink_asds = ForEachMOSA(backlink_asds)
         self.backlink_fknees = ForEachMOSA(backlink_fknees)
         self.testmass_asds = ForEachMOSA(testmass_asds)
         self.testmass_fknees = ForEachMOSA(testmass_fknees)
+        self.oms_isc_carrier_asds = ForEachMOSA(oms_asds[0])
+        self.oms_isc_usb_asds = ForEachMOSA(oms_asds[1])
+        self.oms_tm_carrier_asds = ForEachMOSA(oms_asds[2])
+        self.oms_tm_usb_asds = ForEachMOSA(oms_asds[3])
+        self.oms_ref_carrier_asds = ForEachMOSA(oms_asds[4])
+        self.oms_ref_usb_asds = ForEachMOSA(oms_asds[5])
+        self.oms_fknees = ForEachMOSA(oms_fknees)
 
         # Frequency plan
         if offsets_freqs is not None:
@@ -391,6 +402,12 @@ class Instrument:
         """Turn off all optical pathlength noises."""
         self.backlink_asds = ForEachMOSA(0)
         self.testmass_asds = ForEachMOSA(0)
+        self.oms_isc_carrier_asds = ForEachMOSA(0)
+        self.oms_isc_usb_asds = ForEachMOSA(0)
+        self.oms_tm_carrier_asds = ForEachMOSA(0)
+        self.oms_tm_usb_asds = ForEachMOSA(0)
+        self.oms_ref_carrier_asds = ForEachMOSA(0)
+        self.oms_ref_usb_asds = ForEachMOSA(0)
 
     def disable_ranging_noises(self):
         """Turn off all pseudo-ranging noises."""
@@ -541,7 +558,8 @@ class Instrument:
 
         logger.debug("Computing inter-spacecraft carrier beatnote fluctuations on TPS")
         self.tps_isc_carrier_fluctuations = \
-            self.distant_isc_carrier_fluctuations - self.local_isc_carrier_fluctuations
+            self.distant_isc_carrier_fluctuations - self.local_isc_carrier_fluctuations \
+            + self.central_freq * self.oms_isc_carrier_noises
 
         logger.debug("Computing inter-spacecraft upper sideband beatnote offsets on TPS")
         self.tps_isc_usb_offsets = \
@@ -549,7 +567,8 @@ class Instrument:
 
         logger.debug("Computing inter-spacecraft upper sideband beatnote fluctuations on TPS")
         self.tps_isc_usb_fluctuations = \
-            self.distant_isc_usb_fluctuations - self.local_isc_usb_fluctuations
+            self.distant_isc_usb_fluctuations - self.local_isc_usb_fluctuations \
+            + self.central_freq * self.oms_isc_usb_noises
 
         ## Measured pseudo-ranging on TPS grid (high-frequency)
 
@@ -604,7 +623,8 @@ class Instrument:
 
         logger.debug("Computing test-mass carrier beatnote fluctuations on TPS")
         self.tps_tm_carrier_fluctuations = \
-            self.adjacent_tm_carrier_fluctuations - self.local_tm_carrier_fluctuations
+            self.adjacent_tm_carrier_fluctuations - self.local_tm_carrier_fluctuations \
+            + self.central_freq * self.oms_tm_carrier_noises
 
         logger.debug("Computing test-mass upper sideband beatnote offsets on TPS")
         self.tps_tm_usb_offsets = \
@@ -612,7 +632,8 @@ class Instrument:
 
         logger.debug("Computing test-mass upper sideband beatnote fluctuations on TPS")
         self.tps_tm_usb_fluctuations = \
-            self.adjacent_tm_usb_fluctuations - self.local_tm_usb_fluctuations
+            self.adjacent_tm_usb_fluctuations - self.local_tm_usb_fluctuations \
+            + self.central_freq * self.oms_tm_usb_noises
 
         ## Reference interferometer local beams
 
@@ -656,7 +677,8 @@ class Instrument:
 
         logger.debug("Computing reference carrier beatnote fluctuations on TPS")
         self.tps_ref_carrier_fluctuations = \
-            self.adjacent_ref_carrier_fluctuations - self.local_ref_carrier_fluctuations
+            self.adjacent_ref_carrier_fluctuations - self.local_ref_carrier_fluctuations \
+            + self.central_freq * self.oms_ref_carrier_noises
 
         logger.debug("Computing reference upper sideband beatnote offsets on TPS")
         self.tps_ref_usb_offsets = \
@@ -664,7 +686,8 @@ class Instrument:
 
         logger.debug("Computing reference upper sideband beatnote fluctuations on TPS")
         self.tps_ref_usb_fluctuations = \
-            self.adjacent_ref_usb_fluctuations - self.local_ref_usb_fluctuations
+            self.adjacent_ref_usb_fluctuations - self.local_ref_usb_fluctuations \
+            + self.central_freq * self.oms_ref_usb_noises
 
         ## Sampling beatnotes and measured pseudo-ranges to THE grid
 
@@ -922,6 +945,40 @@ class Instrument:
                 self.physics_size, self.ranging_asds[mosa])
         )
 
+        ## OMS noise
+
+        logger.info("Generating OMS noise")
+
+        self.oms_isc_carrier_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_isc_carrier_asds[mosa], self.oms_fknees[mosa])
+        )
+
+        self.oms_isc_usb_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_isc_usb_asds[mosa], self.oms_fknees[mosa])
+        )
+
+        self.oms_tm_carrier_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_tm_carrier_asds[mosa], self.oms_fknees[mosa])
+        )
+
+        self.oms_tm_usb_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_tm_usb_asds[mosa], self.oms_fknees[mosa])
+        )
+
+        self.oms_ref_carrier_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_ref_carrier_asds[mosa], self.oms_fknees[mosa])
+        )
+
+        self.oms_ref_usb_noises = ForEachMOSA(lambda mosa:
+            noises.oms(self.physics_fs, self.physics_size,
+                self.oms_ref_usb_asds[mosa], self.oms_fknees[mosa])
+        )
+
     def invert_timer_deviations(self, timer_deviations, sc):
         """Invert timer deviations of a given spacecraft.
 
diff --git a/lisainstrument/noises.py b/lisainstrument/noises.py
index 417a5111edd79a06b9c92784dd60d7909d8922ab..28896cd2eebd070e44a1035884d13799ea28da2f 100644
--- a/lisainstrument/noises.py
+++ b/lisainstrument/noises.py
@@ -204,3 +204,27 @@ def testmass(fs, size, asd=2.4E-15, fknee=0.4E-3):
         fs, size, asd, fknee)
     return red(fs, size, asd / (2 * pi * c)) \
         + infrared(fs, size, asd * fknee / (2 * pi * c))
+
+def oms(fs, size, asd, fknee):
+    """Generate optical metrology system (OMS) noise allocation [ffs].
+
+    The power spectral density in displacement is given by
+
+        S_oms(f) [m] = asd^2 [ 1 + (fknee / f)^4 ].
+
+    Multiplying by (2Ï€ f / c)^2 to express it as fractional frequency deviations,
+
+        S_oms(f) [ffd] = (2Ï€ asd / c)^2 [ f^2 + (fknee^4 / f^2) ]
+                      = (2Ï€ asd / c)^2 f^2 + (2Ï€ asd fknee^2 / c)^2 f^(-2)
+
+    Note that the level of this noise depends on the interferometer and the type of beatnote.
+
+    Warning: this corresponds to the overall allocation for the OMS noise from the Performance
+    Model. It is a collection of different noises, some of which are duplicates of standalone
+    noises we already implement in the simulation (e.g., backlink noise).
+
+    """
+    logger.debug("Generating OMS noise (fs=%s Hz, size=%s, asd=%s m/sqrt(Hz), fknee=%s Hz)",
+        fs, size, asd, fknee)
+    return violet(fs, size, 2 * pi * asd / c) \
+        + red(fs, size, 2 * pi * asd * fknee**2 / c)
diff --git a/tests/noises.ipynb b/tests/noises.ipynb
index 5d73ade285a2e8b8b8a6bae39804f92da0b7b363..692161174840daf0c8d350918e27cddcef067b54 100644
--- a/tests/noises.ipynb
+++ b/tests/noises.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +736,9 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -938,6 +940,102 @@
     "plt.grid()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optical metrology system (OMS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs = 1\n",
+    "size = 10000000\n",
+    "t = np.arange(size) / fs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 893 ms, sys: 191 ms, total: 1.08 s\n",
+      "Wall time: 1.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time oms = lisainstrument.noises.oms(fs, size, 6.35E-12, 2E-3)\n",
+    "\n",
+    "plt.figure(figsize=(16,6))\n",
+    "plt.plot(t, oms)\n",
+    "plt.xlabel('Sample')\n",
+    "plt.title('OMS noise')\n",
+    "plt.grid();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-8-ca9407df8050>:5: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  plt.loglog(f, 6.35E-12 * np.sqrt(1 + (2E-3 / f)**4) * (2 * np.pi * f / c), '--')\n",
+      "<ipython-input-8-ca9407df8050>:5: RuntimeWarning: invalid value encountered in multiply\n",
+      "  plt.loglog(f, 6.35E-12 * np.sqrt(1 + (2E-3 / f)**4) * (2 * np.pi * f / c), '--')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, psd = sg.welch(oms, fs, nperseg=size/psdav, detrend='constant')\n",
+    "\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "plt.loglog(f, np.sqrt(psd))\n",
+    "plt.loglog(f, 6.35E-12 * np.sqrt(1 + (2E-3 / f)**4) * (2 * np.pi * f / c), '--')\n",
+    "plt.xlabel('Frequency [Hz]')\n",
+    "plt.ylabel(r'ASD [$/\\sqrt{Hz}$]')\n",
+    "plt.title('OMS noise')\n",
+    "plt.grid()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,