diff --git a/.github/releaseBody.md b/.github/releaseBody.md
index 90c99b8..cc11bb8 100644
--- a/.github/releaseBody.md
+++ b/.github/releaseBody.md
@@ -1,6 +1,7 @@
-## Changed return status of `GaussNewton_Sens_Cal`
+## Significant improvement of `GaussNewton_Sens_Cal`
 
-**Improvements:**
-- Changed return status of `GaussNewton_Sens_Cal` so that now it returns `UTILS_STATUS_TIMEOUT` when the maximum number of iterations is reached
+**New features:**
+- `GaussNewton_Sens_Cal` can now calculate automatically both sphere radius and starting conditions, that are no longer needed as input parameters
+- `GaussNewton_Sens_Cal` returns now an error if `NaN` is detected
 
 See [Changelog](Changelog.md)
\ No newline at end of file
diff --git a/Changelog.md b/Changelog.md
index b053903..3e26c23 100644
--- a/Changelog.md
+++ b/Changelog.md
@@ -1,8 +1,9 @@
 # Changelog
 
-## v1.7.3
+## v1.8.0
 
-**Improvements:**
+**New features:**
+- `GaussNewton_Sens_Cal` can now calculate automatically both sphere radius and starting conditions, that are no longer needed as input parameters
 - `GaussNewton_Sens_Cal` returns now an error if `NaN` is detected
 
 ## v1.7.2
diff --git a/inc/numMethods.h b/inc/numMethods.h
index 9fb3af2..0e34fdb 100644
--- a/inc/numMethods.h
+++ b/inc/numMethods.h
@@ -175,8 +175,8 @@ void QuadProd(matrix_t* A, matrix_t* B, matrix_t* result);
  * s33=out(8,0);
  *
  * \param[in]       Data: pointer to raw data matrix object Data
- * \param[in]       k: radius of sphere to be approximated
- * \param[in]       X0: pointer to starting vector X0 (usually [0 0 0 1 0 0 1 0 1])
+ * \param[in]       k: radius of sphere to be approximated. If 0, it is calculated automatically
+ * \param[in]       X0: pointer to starting vector X0 (usually [0 0 0 1 0 0 1 0 1]). If NULL it is calculated automatically
  * \param[in]       nmax: maximum number of iterations (200 is generally fine, even if it usually converges within 10 iterations)
  * \param[in]       tol: stopping tolerance (1e-6 is generally fine)
  * \param[out]      result: pointer to result matrix object S
@@ -197,8 +197,8 @@ utilsStatus_t GaussNewton_Sens_Cal_9(matrix_t* Data, float k, matrix_t* X0, uint
  * s33=out(5,0);
  *
  * \param[in]       Data: pointer to raw data matrix object Data
- * \param[in]       k: radius of sphere to be approximated
- * \param[in]       X0: pointer to starting vector X0 (usually [0 0 0 1 1 1])
+ * \param[in]       k: radius of sphere to be approximated. If 0, it is calculated automatically
+ * \param[in]       X0: pointer to starting vector X0 (usually [0 0 0 1 1 1]). If NULL it is calculated automatically
  * \param[in]       nmax: maximum number of iterations (200 is generally fine, even if it usually converges within 10 iterations)
  * \param[in]       tol: stopping tolerance (1e-6 is generally fine)
  * \param[out]      result: pointer to result matrix object S
diff --git a/src/numMethods.c b/src/numMethods.c
index 5d5b33d..e8b9fa1 100644
--- a/src/numMethods.c
+++ b/src/numMethods.c
@@ -371,11 +371,8 @@ void LinSolveGauss(matrix_t* A, matrix_t* B, matrix_t* result) {
 
 utilsStatus_t GaussNewton_Sens_Cal_9(matrix_t* Data, float k, matrix_t* X0, uint16_t nmax, float tol,
                                      matrix_t* result) {
-    matrixCopy(X0, result);
     float d1, d2, d3, rx1, rx2, rx3, t1, t2, t3;
-    float k2 = k * k;
-    uint16_t n_iter;
-    uint8_t jj;
+    float k2;
     matrix_t Jr, res, delta, tmp1;
     matrixInit(&Jr, Data->rows, 9);
     matrixInit(&res, Data->rows, 1);
@@ -390,8 +387,43 @@ utilsStatus_t GaussNewton_Sens_Cal_9(matrix_t* Data, float k, matrix_t* X0, uint
         return UTILS_STATUS_ERROR;
     }
 
-    for (n_iter = 0; n_iter < nmax; n_iter++) {
-        for (jj = 0; jj < Data->rows; jj++) {
+    /* Set starting point if not given as input */
+    if (X0 != NULL) {
+        matrixCopy(X0, result);
+    } else {
+        matrixZeros(result);
+        for (uint8_t ii = 0; ii < Data->rows; ii++) {
+            ELEMP(result, 0, 0) += matrixGet(Data, ii, 0) / Data->rows;
+            ELEMP(result, 1, 0) += matrixGet(Data, ii, 1) / Data->rows;
+            ELEMP(result, 2, 0) += matrixGet(Data, ii, 2) / Data->rows;
+        }
+        matrixSet(result, 3, 0, 1);
+        matrixSet(result, 6, 0, 1);
+        matrixSet(result, 8, 0, 1);
+    }
+
+    /* Set target radius if not given as input */
+    if (k != 0) {
+        k2 = k * k;
+    } else {
+        float max = matrixGet(Data, 0, 0) - matrixGet(result, 0, 0);
+        float min = matrixGet(Data, 0, 0) - matrixGet(result, 0, 0);
+        for (uint8_t ii = 0; ii < Data->rows; ii++) {
+            for (uint8_t jj = 0; jj < 3; jj++) {
+                float data = matrixGet(Data, ii, jj) - matrixGet(result, jj, 0);
+                if (data > max) {
+                    max = data;
+                } else if (data < min) {
+                    min = data;
+                }
+            }
+        }
+        k2 = 0.25 * (max - min) * (max - min);
+    }
+
+    /* Perform best-fit algorithm */
+    for (uint16_t n_iter = 0; n_iter < nmax; n_iter++) {
+        for (uint8_t jj = 0; jj < Data->rows; jj++) {
             d1 = ELEMP(Data, jj, 0) - ELEMP(result, 0, 0);
             d2 = ELEMP(Data, jj, 1) - ELEMP(result, 1, 0);
             d3 = ELEMP(Data, jj, 2) - ELEMP(result, 2, 0);
@@ -449,11 +481,8 @@ utilsStatus_t GaussNewton_Sens_Cal_9(matrix_t* Data, float k, matrix_t* X0, uint
 
 utilsStatus_t GaussNewton_Sens_Cal_6(matrix_t* Data, float k, matrix_t* X0, uint16_t nmax, float tol,
                                      matrix_t* result) {
-    matrixCopy(X0, result);
     float d1, d2, d3, t1, t2, t3;
-    float k2 = k * k;
-    uint16_t n_iter;
-    uint8_t jj;
+    float k2;
 
     matrix_t Jr, res, delta, tmp1;
     matrixInit(&Jr, Data->rows, 6);
@@ -469,8 +498,43 @@ utilsStatus_t GaussNewton_Sens_Cal_6(matrix_t* Data, float k, matrix_t* X0, uint
         return UTILS_STATUS_ERROR;
     }
 
-    for (n_iter = 0; n_iter < nmax; n_iter++) {
-        for (jj = 0; jj < Data->rows; jj++) {
+    /* Set starting point if not given as input */
+    if (X0 != NULL) {
+        matrixCopy(X0, result);
+    } else {
+        matrixZeros(result);
+        for (uint8_t ii = 0; ii < Data->rows; ii++) {
+            ELEMP(result, 0, 0) += matrixGet(Data, ii, 0) / Data->rows;
+            ELEMP(result, 1, 0) += matrixGet(Data, ii, 1) / Data->rows;
+            ELEMP(result, 2, 0) += matrixGet(Data, ii, 2) / Data->rows;
+        }
+        matrixSet(result, 3, 0, 1);
+        matrixSet(result, 4, 0, 1);
+        matrixSet(result, 5, 0, 1);
+    }
+
+    /* Set target radius if not given as input */
+    if (k != 0) {
+        k2 = k * k;
+    } else {
+        float max = matrixGet(Data, 0, 0) - matrixGet(result, 0, 0);
+        float min = matrixGet(Data, 0, 0) - matrixGet(result, 0, 0);
+        for (uint8_t ii = 0; ii < Data->rows; ii++) {
+            for (uint8_t jj = 0; jj < 3; jj++) {
+                float data = matrixGet(Data, ii, jj) - matrixGet(result, jj, 0);
+                if (data > max) {
+                    max = data;
+                } else if (data < min) {
+                    min = data;
+                }
+            }
+        }
+        k2 = 0.25 * (max - min) * (max - min);
+    }
+
+    /* Perform best-fit algorithm */
+    for (uint16_t n_iter = 0; n_iter < nmax; n_iter++) {
+        for (uint8_t jj = 0; jj < Data->rows; jj++) {
             d1 = ELEMP(Data, jj, 0) - ELEMP(result, 0, 0);
             d2 = ELEMP(Data, jj, 1) - ELEMP(result, 1, 0);
             d3 = ELEMP(Data, jj, 2) - ELEMP(result, 2, 0);