diff --git a/design/unit-test/ekf_encoder_unit_test_script.m b/design/unit-test/ekf_encoder_unit_test_script.m
new file mode 100644
index 00000000..679e1cee
--- /dev/null
+++ b/design/unit-test/ekf_encoder_unit_test_script.m
@@ -0,0 +1,59 @@
+% inputs: x, P, R, encoder
+x = [
+    2.426878243614206; 4.001402344444001; 0.709431693136077; 2.108806413131375; 4.578677625945335; 3.961036647797772; 4.797462131964515; 3.278703495782934; 0.178558392870948; 4.245646529343886; 4.669966238787753; 3.393675774288867; 3.788700652891667
+
+
+];
+
+P_diag_arr = [2.95219117313746;
+0.50150522974397;
+0.31864903478599;
+1.11722922016661;
+0.59435520762892;
+1.46906291404807;
+1.01848024017227;
+2.85489139433318;
+2.76099611950969;
+0.15803099304238;
+2.21357428655099;
+0.80735827919567;
+1.26850684502642];
+
+P = diag(P_diag_arr);
+
+
+
+R = 0.01;
+encoder = 0.431651170248720; 
+
+% action
+[xhat, Phat] = ekf_correct(@model_meas_encoder, @model_meas_encoder_jacobian, x, P, encoder, 0, R);
+
+% print 
+% STATE
+fprintf('// Predicted State (x_expected)\n');
+fprintf('double x_expected[SIZE_STATE] = {\n');
+for i = 1:SIZE_STATE
+    if i < SIZE_STATE
+        fprintf('    %.15g,\n', xhat(i));
+    else
+        fprintf('    %.15g\n', xhat(i));
+    end
+end
+fprintf('};\n\n');
+
+% COVARIANCE MATRIX FLATTENED
+fprintf('// Predicted Covariance (P_expected)\n');
+fprintf('double P_expected[SIZE_STATE * SIZE_STATE] = {\n');
+for i = 1:SIZE_STATE
+    for j = 1:SIZE_STATE
+        idx = (i - 1) * SIZE_STATE + j;
+        if idx < SIZE_STATE^2
+            fprintf('    %.15g,', Phat(i, j));
+        else
+            fprintf('    %.15g\n', Phat(i, j));
+        end
+    end
+end
+
+fprintf('};\n');
\ No newline at end of file
diff --git a/design/unit-test/ekf_unit_test_script.m b/design/unit-test/ekf_unit_test_script.m
new file mode 100644
index 00000000..2ef3d40f
--- /dev/null
+++ b/design/unit-test/ekf_unit_test_script.m
@@ -0,0 +1,168 @@
+
+% === SIZE DEFINITIONS ===
+SIZE_STATE = 13;
+
+% === STATE VECTOR ===
+x = [
+    2.426878243614206; 4.001402344444001; 0.709431693136077; 2.108806413131375; 4.578677625945335; 3.961036647797772; 4.797462131964515; 3.278703495782934; 0.178558392870948; 4.245646529343886; 4.669966238787753; 3.393675774288867; 3.788700652891667
+
+
+];
+
+% === ZERO INITIAL COVARIANCE ===
+P_diag_arr = [2.95219117313746;
+0.50150522974397;
+0.31864903478599;
+1.11722922016661;
+0.59435520762892;
+1.46906291404807;
+1.01848024017227;
+2.85489139433318;
+2.76099611950969;
+0.15803099304238;
+2.21357428655099;
+0.80735827919567;
+1.26850684502642];
+
+P = diag(P_diag_arr);
+
+% === INPUT STRUCT ===
+u.accel = [1.456126946168524; 2.400841406666400; 0.425659015881646];
+u.cmd = 0.547870901214845;
+
+% encoder
+encoder = 0.431651170248720;
+
+% === TIME STEP ===
+dt = 1.038711463665142;
+
+% === PROCESS R_MTI ===
+R = diag([1e-6, 1e-6, 1e-6, 2e-3, 2e-3, 2e-3, 2e1]);
+
+% imu select
+global IMU_select;
+IMU_select = [1 1];
+
+% IMU array
+IMU_1 = [2.711216972515416;
+4.238310136658316;
+2.074393441904026;
+7.598779134258502;
+1.752878606103443;
+2.033296028751589;
+1.536372424330727;
+2.048978680348982;
+3.921288156935092;
+2.799920579853715
+];
+
+IMU_2 = [7.387037136825951;
+3.441659130636672;
+1.478530560993089;
+7.239047749439143;
+7.837987026848682;
+3.510959785008826;
+0.888953787524790;
+2.064517567296535;
+3.269758768900417;
+4.759168592068915
+
+];
+
+% bias array 
+b.bias_1 = [0.942736984276934;
+0.417744104316662;
+0.983052466469856;
+0.301454948712065;
+0.701098755900926;
+0.666338851584426;
+0.539126465042857;
+0.698105520180308;
+0.666527913402587;
+0.178132454400338
+
+];
+
+b.bias_2 = [0.128014399720173;
+0.999080394761361;
+0.171121066356432;
+0.032600820530528;
+0.561199792709660;
+0.881866500451810;
+0.669175304534394;
+0.190433267179954;
+0.368916546063895;
+0.460725937260412
+
+];
+
+
+disp("IMU_1 select")
+% === CALL CORRECTION ===
+[x, P] = ekf_algorithm(x, P, b, 0, dt, IMU_1, IMU_2, u.cmd, encoder, 0);
+
+% === PRINT C-STYLE OUTPUTS ===
+
+% STATE
+fprintf('// Predicted State (x_expected)\n');
+fprintf('double x_expected[SIZE_STATE] = {\n');
+for i = 1:SIZE_STATE
+    if i < SIZE_STATE
+        fprintf('    %.15g,\n', x(i));
+    else
+        fprintf('    %.15g\n', x(i));
+    end
+end
+fprintf('};\n\n');
+
+% COVARIANCE MATRIX FLATTENED
+fprintf('// Predicted Covariance (P_expected)\n');
+fprintf('double P_expected[SIZE_STATE * SIZE_STATE] = {\n');
+for i = 1:SIZE_STATE
+    for j = 1:SIZE_STATE
+        idx = (i - 1) * SIZE_STATE + j;
+        if idx < SIZE_STATE^2
+            fprintf('    %.15g,', P(i, j));
+        else
+            fprintf('    %.15g\n', P(i, j));
+        end
+    end
+end
+
+fprintf('};\n');
+
+
+% disp("IMU_2 select")
+% % imu_2 corr
+% x_new = xhat;
+% P_new = Phat;
+% 
+% [xhat, Phat] = ekf_correct(@model_meas_imu, @model_meas_imu_jacobian, x_new, P_new, IMU_2(4:end), b.bias_2, R);
+% 
+% % STATE
+% fprintf('// Predicted State (x_expected)\n');
+% fprintf('double x_expected[SIZE_STATE] = {\n');
+% for i = 1:SIZE_STATE
+%     if i < SIZE_STATE
+%         fprintf('    %.15g,\n', xhat(i));
+%     else
+%         fprintf('    %.15g\n', xhat(i));
+%     end
+% end
+% fprintf('};\n\n');
+% 
+% % COVARIANCE MATRIX FLATTENED
+% fprintf('// Predicted Covariance (P_expected)\n');
+% fprintf('double P_expected[SIZE_STATE * SIZE_STATE] = {\n');
+% for i = 1:SIZE_STATE
+%     for j = 1:SIZE_STATE
+%         idx = (i - 1) * SIZE_STATE + j;
+%         if idx < SIZE_STATE^2
+%             fprintf('    %.15g,', Phat(i, j));
+%         else
+%             fprintf('    %.15g\n', Phat(i, j));
+%         end
+%     end
+% end
+% 
+% fprintf('};\n');
\ No newline at end of file
diff --git a/design/unit-test/model_dynamic_unit_test_script.m b/design/unit-test/model_dynamic_unit_test_script.m
new file mode 100644
index 00000000..d107315a
--- /dev/null
+++ b/design/unit-test/model_dynamic_unit_test_script.m
@@ -0,0 +1,35 @@
+
+% === SIZE DEFINITIONS ===
+SIZE_STATE = 13;
+
+% === STATE VECTOR ===
+x = [1:13]'
+size(x)
+
+% === INPUT STRUCT ===
+u.accel = [5;77;9];
+u.cmd = 8;
+
+% === TIME STEP ===
+dt = 0.32;
+
+
+
+% === CALL PREDICTION ===
+[x_out] = model_dynamics(dt, x, u);
+
+% === PRINT C-STYLE OUTPUTS ===
+
+% STATE
+fprintf('// est state (x_expected)\n');
+fprintf('double x_expected[13] = {\n');
+for i = 1:SIZE_STATE
+    if i < SIZE_STATE
+        fprintf('    %.15g,\n', x_out(i));
+    else
+        fprintf('    %.15g\n', x_out(i));
+    end
+end
+fprintf('};\n\n');
+
+
diff --git a/design/unit-test/pad_filter_unit_test_script.m b/design/unit-test/pad_filter_unit_test_script.m
new file mode 100644
index 00000000..7fef5568
--- /dev/null
+++ b/design/unit-test/pad_filter_unit_test_script.m
@@ -0,0 +1,44 @@
+%% NewContextOneIteration
+
+% inputs
+
+IMU_1 = [0.01; 0.02; -9.81; 0.001; -0.002; 0.0005; 0.3; 0.0; 0.4; 1013.25];
+IMU_2 = [-0.02; 0.01; -9.78; 0.0005; 0.001; -0.001; 0.31; -0.01; 0.39; 1013.30];
+
+% action
+
+[x_init, bias_1, bias_2] = pad_filter(IMU_1, IMU_2);
+
+% print outputs
+
+SIZE_STATE = 13;
+SIZE_IMU_ALL = 10;
+
+fprintf('double x_expected[SIZE_STATE] = {\n');
+for i = 1:SIZE_STATE
+    if i < SIZE_STATE
+        fprintf('    %.15g,\n', x_init(i));
+    else
+        fprintf('    %.15g\n', x_init(i));
+    end
+end
+fprintf('};\n\n');
+
+fprintf('double x_expected[SIZE_IMU_ALL] = {\n');
+for i = 1:SIZE_IMU_ALL
+    if i < SIZE_IMU_ALL
+        fprintf('    %.15g,\n', bias_1(i));
+    else
+        fprintf('    %.15g\n', bias_1(i));
+    end
+end
+fprintf('};\n\n');
+fprintf('double x_expected[SIZE_IMU_ALL] = {\n');
+for i = 1:SIZE_IMU_ALL
+    if i < SIZE_IMU_ALL
+        fprintf('    %.15g,\n', bias_2(i));
+    else
+        fprintf('    %.15g\n', bias_2(i));
+    end
+end
+fprintf('};\n\n');
\ No newline at end of file