diff --git a/src/prog_models/models/__init__.py b/src/prog_models/models/__init__.py
index 5e8aebe8b..8c2241fbc 100644
--- a/src/prog_models/models/__init__.py
+++ b/src/prog_models/models/__init__.py
@@ -8,4 +8,4 @@
 from .dcmotor import DCMotor
 from .esc import ESC
 from .powertrain import Powertrain
-from .thrown_object import ThrownObject
+from .thrown_object import ThrownObject, LinearThrownObject
diff --git a/src/prog_models/models/thrown_object.py b/src/prog_models/models/thrown_object.py
index d3947e81f..6cb7bbcf5 100644
--- a/src/prog_models/models/thrown_object.py
+++ b/src/prog_models/models/thrown_object.py
@@ -1,7 +1,7 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from .. import PrognosticsModel
+from .. import PrognosticsModel, LinearModel
 import numpy as np
 
 def calc_lumped_param(params):
@@ -116,3 +116,88 @@ def event_state(self, x : dict) -> dict:
             'falling': np.maximum(x['v']/self.parameters['throwing_speed'],0),  # Throwing speed is max speed
             'impact': np.maximum(x['x']/x_max,0)  # then it's fraction of height
         }
+
+
+class LinearThrownObject(LinearModel):
+    """
+    Model that similates an object thrown into the air without air resistance. This is a linear version of ThrownObject.
+
+    Events (2)
+        | falling: The object is falling
+        | impact: The object has hit the ground
+
+    Inputs/Loading: (0)
+
+    States: (2)
+        | x: Position in space (m)
+        | v: Velocity in space (m/s)
+
+    Outputs/Measurements: (1)
+        | x: Position in space (m)
+
+    Keyword Args
+    ------------
+        process_noise : Optional, float or Dict[Srt, float]
+          Process noise (applied at dx/next_state). 
+          Can be number (e.g., .2) applied to every state, a dictionary of values for each 
+          state (e.g., {'x1': 0.2, 'x2': 0.3}), or a function (x) -> x
+        process_noise_dist : Optional, String
+          distribution for process noise (e.g., normal, uniform, triangular)
+        measurement_noise : Optional, float or Dict[Srt, float]
+          Measurement noise (applied in output eqn).
+          Can be number (e.g., .2) applied to every output, a dictionary of values for each
+          output (e.g., {'z1': 0.2, 'z2': 0.3}), or a function (z) -> z
+        measurement_noise_dist : Optional, String
+          distribution for measurement noise (e.g., normal, uniform, triangular)
+        g : Optional, float
+            Acceleration due to gravity (m/s^2). Default is 9.81 m/s^2 (standard gravity)
+        thrower_height : Optional, float
+            Height of the thrower (m). Default is 1.83 m
+        throwing_speed : Optional, float
+            Speed at which the ball is thrown (m/s). Default is 40 m/s
+    """
+
+    inputs = []  # no inputs, no way to control
+    states = [
+        'x',     # Position (m) 
+        'v'      # Velocity (m/s)
+        ]
+    outputs = [
+        'x'      # Position (m)
+    ]
+    events = [
+        'impact' # Event- object has impacted ground
+    ]
+
+    A = np.array([[0, 1], [0, 0]])
+    E = np.array([[0], [-9.81]])
+    C = np.array([[1, 0]])
+    F = None # Will override method
+
+    # The Default parameters. Overwritten by passing parameters dictionary into constructor
+    default_parameters = {
+        'thrower_height': 1.83,  # m
+        'throwing_speed': 40,  # m/s
+        'g': -9.81  # Acceleration due to gravity in m/s^2
+    }
+
+    def initialize(self, u=None, z=None):
+        return self.StateContainer({
+            'x': self.parameters['thrower_height'],  # Thrown, so initial altitude is height of thrower
+            'v': self.parameters['throwing_speed']  # Velocity at which the ball is thrown - this guy is a professional baseball pitcher
+            })
+    
+    # This is actually optional. Leaving thresholds_met empty will use the event state to define thresholds.
+    #  Threshold = Event State == 0. However, this implementation is more efficient, so we included it
+    def threshold_met(self, x):
+        return {
+            'falling': x['v'] < 0,
+            'impact': x['x'] <= 0
+        }
+
+    def event_state(self, x): 
+        x_max = x['x'] + np.square(x['v'])/(-self.parameters['g']*2) # Use speed and position to estimate maximum height
+        return {
+            'falling': np.maximum(x['v']/self.parameters['throwing_speed'],0),  # Throwing speed is max speed
+            'impact': np.maximum(x['x']/x_max,0) if x['v'] < 0 else 1  # 1 until falling begins, then it's fraction of height
+        }