diff --git a/ch4_rf_point_positioning/README.md b/ch4_rf_point_positioning/README.md
index 1ab185c..b0de3fb 100644
--- a/ch4_rf_point_positioning/README.md
+++ b/ch4_rf_point_positioning/README.md
@@ -4,6 +4,10 @@
 
 This module implements RF (Radio Frequency) positioning algorithms described in **Chapter 4** of *Principles of Indoor Positioning and Indoor Navigation*. It provides simulation-based examples of various RF positioning techniques including TOA, TDOA, AOA, and RSS-based positioning.
 
+**Anchor-setting convention in this chapter:**
+- Inline demos use a shared **asymmetric** anchor layout to reduce symmetry bias.
+- Square/circular/linear layouts are retained for explicit geometry-comparison datasets only.
+
 ## Quick Start
 
 ```bash
@@ -330,7 +334,7 @@ This properly accounts for:
 from core.rf import AOAPositioner, aoa_angle_vector
 import numpy as np
 
-anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+anchors = np.array([[0, 0], [12, 1], [10.5, 11.5], [1.5, 9]], dtype=float)
 aoa = aoa_angle_vector(anchors, np.array([4.0, 6.0]))
 
 positioner = AOAPositioner(anchors)
@@ -424,8 +428,8 @@ Where σ symbols are **standard deviations** (not variances):
 ```python
 from core.rf import compute_geometry_matrix, compute_dop, position_error_from_dop
 
-# Square anchor layout
-anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+# Default inline anchor layout (asymmetric)
+anchors = np.array([[0, 0], [12, 1], [10.5, 11.5], [1.5, 9]], dtype=float)
 position = np.array([5.0, 5.0])
 
 # Compute geometry matrix and DOP
@@ -455,8 +459,8 @@ print(f"Expected horizontal error: {sigma_horizontal:.2f} m")  # 0.42 m
 import numpy as np
 from core.rf import TOAPositioner
 
-# Define anchor layout (square)
-anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+# Define default Chapter 4 inline layout (asymmetric)
+anchors = np.array([[0, 0], [12, 1], [10.5, 11.5], [1.5, 9]], dtype=float)
 
 # True position and compute ranges
 true_pos = np.array([5.0, 5.0])
@@ -568,7 +572,7 @@ print(f"Total fading {total_fading:.1f} dB -> {factor:.2f}x distance")
 ```python
 from core.rf import TDOAPositioner
 
-anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+anchors = np.array([[0, 0], [12, 1], [10.5, 11.5], [1.5, 9]], dtype=float)
 true_pos = np.array([5.0, 5.0])
 
 # Compute TDOA measurements (relative to anchor 0)
@@ -595,7 +599,7 @@ Chapter 4: TOA Positioning Example
 ======================================================================
 
 --- Setting up test scenario ---
-  Anchors: 4 anchors in square configuration (10m x 10m)
+  Anchors: 4 anchors in asymmetric configuration
   True position: [5.0, 5.0] m
 
 --- TOA Positioning (Perfect Measurements) ---
diff --git a/ch4_rf_point_positioning/example_aoa_positioning.py b/ch4_rf_point_positioning/example_aoa_positioning.py
index c4bb399..8c6a21b 100644
--- a/ch4_rf_point_positioning/example_aoa_positioning.py
+++ b/ch4_rf_point_positioning/example_aoa_positioning.py
@@ -38,6 +38,15 @@
     aoa_tan_azimuth,
 )
 
+# Chapter 4 default anchor layouts.
+DEFAULT_ANCHORS_2D = np.array(
+    [[0.0, 0.0], [12.0, 1.0], [10.5, 11.5], [1.5, 9.0]], dtype=float
+)
+DEFAULT_ANCHORS_3D = np.array(
+    [[0.0, 0.0, 5.0], [12.0, 1.0, 5.0], [10.5, 11.5, 5.0], [1.5, 9.0, 5.0]],
+    dtype=float,
+)
+
 
 def demo_aoa_basic():
     """Demonstrate basic AOA positioning with I-WLS."""
@@ -45,9 +54,9 @@ def demo_aoa_basic():
     print("Demo 1: Basic AOA Positioning (I-WLS)")
     print("=" * 70)
 
-    # Setup anchors (4 anchors at corners) in ENU coordinates
+    # Setup anchors (4 anchors, asymmetric) in ENU coordinates
     # E=x, N=y in 2D
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    anchors = DEFAULT_ANCHORS_2D.copy()
 
     # True position
     true_position = np.array([4.0, 6.0])
@@ -213,9 +222,7 @@ def demo_minimum_anchors():
     anchor_configs = {
         "2 anchors": np.array([[0, 0], [10, 0]], dtype=float),
         "3 anchors": np.array([[0, 0], [10, 0], [5, 10]], dtype=float),
-        "4 anchors": np.array(
-            [[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float
-        ),
+        "4 anchors": DEFAULT_ANCHORS_2D.copy(),
     }
 
     print(f"\nTrue position (E, N): {true_position}")
@@ -256,7 +263,7 @@ def visualize_aoa_geometry():
     print("=" * 70)
 
     # Setup in ENU coordinates (E=x-axis, N=y-axis)
-    anchors = np.array([[0, 0], [12, 0], [12, 12], [0, 12]], dtype=float)
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_position = np.array([5.0, 7.0])
 
     # Generate AOA using new convention (Eq. 4.64: psi from North)
@@ -380,7 +387,7 @@ def demo_closed_form_algorithms():
 
     # === 2D Comparison ===
     print("\n--- 2D Comparison (I-WLS vs PLE) ---")
-    anchors_2d = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    anchors_2d = DEFAULT_ANCHORS_2D.copy()
     true_pos_2d = np.array([4.0, 6.0])
 
     # Generate azimuth angles
@@ -405,9 +412,7 @@ def demo_closed_form_algorithms():
 
     # === 3D Comparison ===
     print("\n--- 3D Comparison (I-WLS vs OVE vs PLE) ---")
-    anchors_3d = np.array(
-        [[0, 0, 5], [10, 0, 5], [10, 10, 5], [0, 10, 5]], dtype=float
-    )
+    anchors_3d = DEFAULT_ANCHORS_3D.copy()
     true_pos_3d = np.array([4.0, 6.0, 0.0])
 
     # Generate angles
@@ -503,7 +508,7 @@ def demo_geometry_sensitivity():
 
     # Define different anchor geometries
     geometries = {
-        "Square (good)": np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float),
+        "Asymmetric (good)": DEFAULT_ANCHORS_2D.copy(),
         "Triangle (good)": np.array([[0, 0], [10, 0], [5, 10]], dtype=float),
         "Linear (poor)": np.array([[0, 0], [5, 0], [10, 0], [15, 0]], dtype=float),
         "Near-collinear (very poor)": np.array(
diff --git a/ch4_rf_point_positioning/example_comparison.py b/ch4_rf_point_positioning/example_comparison.py
index f3cfbcc..b27553f 100644
--- a/ch4_rf_point_positioning/example_comparison.py
+++ b/ch4_rf_point_positioning/example_comparison.py
@@ -261,13 +261,15 @@ def generate_scenario(seed=42):
     """Generate a test scenario with anchors and true positions (inline mode)."""
     np.random.seed(seed)
 
-    # Square anchor layout (10m x 10m area)
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    # Chapter 4 default: mildly asymmetric anchor geometry.
+    anchors = np.array(
+        [[0.0, 0.0], [12.0, 1.0], [10.5, 11.5], [1.5, 9.0]], dtype=float
+    )
 
     # Generate test positions
     n_points = 50
-    x = np.random.uniform(1, 9, n_points)
-    y = np.random.uniform(1, 9, n_points)
+    x = np.random.uniform(2.0, 10.0, n_points)
+    y = np.random.uniform(2.0, 9.5, n_points)
     true_positions = np.column_stack([x, y])
 
     return anchors, true_positions
@@ -295,7 +297,9 @@ def toa_positioning_test(
 
         try:
             positioner = TOAPositioner(anchors, method="iterative_ls")
-            est_pos, info = positioner.solve(ranges, initial_guess=np.array([5.0, 5.0]))
+            est_pos, info = positioner.solve(
+                ranges, initial_guess=np.mean(anchors, axis=0)
+            )
             if info["converged"]:
                 error = np.linalg.norm(est_pos - true_pos)
                 errors.append(error)
@@ -322,7 +326,9 @@ def tdoa_positioning_test(anchors, true_positions, noise_std=0.0):
 
         try:
             positioner = TDOAPositioner(anchors, reference_idx=0)
-            est_pos, info = positioner.solve(tdoa, initial_guess=np.array([5.0, 5.0]))
+            est_pos, info = positioner.solve(
+                tdoa, initial_guess=np.mean(anchors, axis=0)
+            )
             if info["converged"]:
                 error = np.linalg.norm(est_pos - true_pos)
                 errors.append(error)
@@ -333,18 +339,40 @@ def tdoa_positioning_test(anchors, true_positions, noise_std=0.0):
 
 
 def aoa_positioning_test(anchors, true_positions, noise_std=0.0):
-    """Test AOA positioning (inline mode)."""
+    """Test AOA positioning (inline mode) with stability guards.
+
+    Uses sigma-based weighting when angle noise is known and rejects
+    implausible far-field estimates to avoid numerical outliers from
+    near-singular AOA geometry.
+    """
     errors = []
+    anchors = np.asarray(anchors, dtype=float)
+    anchor_min = np.min(anchors, axis=0)
+    anchor_max = np.max(anchors, axis=0)
+    anchor_span = np.linalg.norm(anchor_max - anchor_min)
+    # Allow estimates moderately outside the anchor hull.
+    max_dist_from_centroid = 3.0 * anchor_span
+    centroid = np.mean(anchors, axis=0)
 
     for true_pos in tqdm(true_positions, desc="  AOA", leave=False, unit="pt"):
         aoa = np.array([aoa_azimuth(anchor, true_pos) for anchor in anchors])
         if noise_std > 0:
             aoa += np.random.randn(len(aoa)) * noise_std
+        # Normalize wrapped angles to [-pi, pi] for numerical consistency.
+        aoa = (aoa + np.pi) % (2.0 * np.pi) - np.pi
 
         try:
             positioner = AOAPositioner(anchors)
-            est_pos, info = positioner.solve(aoa, initial_guess=np.array([5.0, 5.0]))
+            solve_kwargs = {"initial_guess": centroid}
+            if noise_std > 0:
+                solve_kwargs["sigma_psi"] = noise_std
+
+            est_pos, info = positioner.solve(aoa, **solve_kwargs)
             if info["converged"]:
+                # Reject physically implausible solutions produced by
+                # poor angle conditioning (e.g., nearly parallel bearings).
+                if np.linalg.norm(est_pos - centroid) > max_dist_from_centroid:
+                    continue
                 error = np.linalg.norm(est_pos - true_pos)
                 errors.append(error)
         except Exception:
@@ -407,7 +435,9 @@ def rss_positioning_test(
 
         try:
             positioner = TOAPositioner(anchors, method="iterative_ls")
-            est_pos, info = positioner.solve(ranges, initial_guess=np.array([5.0, 5.0]))
+            est_pos, info = positioner.solve(
+                ranges, initial_guess=np.mean(anchors, axis=0)
+            )
             if info["converged"]:
                 error = np.linalg.norm(est_pos - true_pos)
                 errors.append(error)
@@ -435,7 +465,7 @@ def run_inline_comparison():
     print(f"Test scenario created:")
     print(f"  Anchors: {len(anchors)}")
     print(f"  Test points: {len(true_positions)}")
-    print(f"  Area: 10m x 10m")
+    print("  Area: irregular indoor region (asymmetric anchor layout)")
 
     # ---- Independent noise schedules per method ----
     toa_noise_levels = [0.0, 0.05, 0.1, 0.2, 0.5]       # metres
diff --git a/ch4_rf_point_positioning/example_tdoa_positioning.py b/ch4_rf_point_positioning/example_tdoa_positioning.py
index a295800..23de3ff 100644
--- a/ch4_rf_point_positioning/example_tdoa_positioning.py
+++ b/ch4_rf_point_positioning/example_tdoa_positioning.py
@@ -25,6 +25,14 @@
     toa_fang_solver,
 )
 
+# Chapter 4 default anchor layouts.
+DEFAULT_ANCHORS_2D = np.array(
+    [[0.0, 0.0], [12.0, 1.0], [10.5, 11.5], [1.5, 9.0]], dtype=float
+)
+DEFAULT_ANCHORS_2D_WITH_EXTRA = np.array(
+    [[0.0, 0.0], [12.0, 1.0], [10.5, 11.5], [1.5, 9.0], [6.0, 3.5]], dtype=float
+)
+
 
 def demo_tdoa_basic():
     """Demonstrate basic TDOA positioning with I-WLS."""
@@ -32,10 +40,8 @@ def demo_tdoa_basic():
     print("Demo 1: Basic TDOA Positioning (I-WLS)")
     print("=" * 70)
 
-    # Setup anchors (5 anchors in a larger area)
-    anchors = np.array(
-        [[0, 0], [15, 0], [15, 15], [0, 15], [7.5, 7.5]], dtype=float
-    )
+    # Setup anchors (5 anchors, asymmetric)
+    anchors = DEFAULT_ANCHORS_2D_WITH_EXTRA.copy()
 
     # True position
     true_position = np.array([5.0, 8.0])
@@ -76,9 +82,7 @@ def demo_tdoa_with_noise():
     print("=" * 70)
 
     # Setup
-    anchors = np.array(
-        [[0, 0], [20, 0], [20, 20], [0, 20], [10, 10]], dtype=float
-    )
+    anchors = DEFAULT_ANCHORS_2D_WITH_EXTRA.copy()
     true_position = np.array([7.0, 12.0])
 
     # Generate noiseless TDOA
@@ -162,9 +166,7 @@ def demo_correlated_covariance():
     print("=" * 70)
 
     # Setup: 5 anchors with heterogeneous noise levels
-    anchors = np.array(
-        [[0, 0], [20, 0], [20, 20], [0, 20], [10, 10]], dtype=float
-    )
+    anchors = DEFAULT_ANCHORS_2D_WITH_EXTRA.copy()
 
     # True position
     true_position = np.array([7.0, 12.0])
@@ -300,9 +302,7 @@ def demo_covariance_sensitivity():
     print("=" * 70)
 
     # Setup: 4 anchors
-    anchors = np.array(
-        [[0, 0], [20, 0], [20, 20], [0, 20]], dtype=float
-    )
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_position = np.array([8.0, 12.0])
 
     print(f"\nTrue position: {true_position}")
@@ -476,10 +476,8 @@ def demo_geometry_effect():
 
     true_position = np.array([5.0, 5.0])
 
-    # Good geometry: anchors surrounding the target
-    good_anchors = np.array(
-        [[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float
-    )
+    # Good geometry: anchors surrounding the target (asymmetric quadrilateral)
+    good_anchors = DEFAULT_ANCHORS_2D.copy()
 
     # Poor geometry: anchors on one side
     poor_anchors = np.array(
@@ -492,7 +490,7 @@ def demo_geometry_effect():
     print(f"Noise std: {noise_std} m")
 
     # Test with good geometry
-    print("\n--- Good Geometry (surrounding) ---")
+    print("\n--- Good Geometry (surrounding, asymmetric) ---")
     dist_ref = np.linalg.norm(true_position - good_anchors[0])
     tdoa_good = np.array(
         [
@@ -571,8 +569,8 @@ def demo_fang_toa_solver():
     print("Demo 7: Fang's TOA Closed-Form vs I-WLS (Eqs. 4.43-4.49)")
     print("=" * 70)
 
-    # Setup: 4 anchors in a square
-    anchors = np.array([[0, 0], [20, 0], [20, 20], [0, 20]], dtype=float)
+    # Setup: 4 anchors with default asymmetric geometry
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_position = np.array([7.0, 12.0])
 
     print(f"\nTrue position: {true_position}")
@@ -669,10 +667,8 @@ def demo_chan_tdoa_solver():
     print("Demo 8: Chan's TDOA Closed-Form vs I-WLS (Eqs. 4.50-4.62)")
     print("=" * 70)
 
-    # Setup: 5 anchors for good geometry
-    anchors = np.array(
-        [[0, 0], [20, 0], [20, 20], [0, 20], [10, 10]], dtype=float
-    )
+    # Setup: 5 anchors with default asymmetric geometry
+    anchors = DEFAULT_ANCHORS_2D_WITH_EXTRA.copy()
     true_position = np.array([8.0, 12.0])
     ref_idx = 0
 
@@ -791,9 +787,7 @@ def demo_closed_form_comparison():
     print("=" * 70)
 
     # Setup
-    anchors = np.array(
-        [[0, 0], [20, 0], [20, 20], [0, 20], [10, 10]], dtype=float
-    )
+    anchors = DEFAULT_ANCHORS_2D_WITH_EXTRA.copy()
     true_position = np.array([7.5, 11.0])
     ref_idx = 0
 
diff --git a/ch4_rf_point_positioning/example_toa_positioning.py b/ch4_rf_point_positioning/example_toa_positioning.py
index 5c7584c..89587b1 100644
--- a/ch4_rf_point_positioning/example_toa_positioning.py
+++ b/ch4_rf_point_positioning/example_toa_positioning.py
@@ -30,6 +30,12 @@
     toa_solve_with_clock_bias,
 )
 
+# Chapter 4 default anchor layout:
+# use a mildly irregular quadrilateral to avoid overly symmetric geometry.
+DEFAULT_ANCHORS_2D = np.array(
+    [[0.0, 0.0], [12.0, 1.0], [10.5, 11.5], [1.5, 9.0]], dtype=float
+)
+
 
 def example_toa_perfect():
     """Example 1: TOA positioning with perfect measurements."""
@@ -37,8 +43,8 @@ def example_toa_perfect():
     print("Example 1: TOA Positioning with Perfect Measurements")
     print("=" * 70)
 
-    # Square anchor layout
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    # Chapter 4 default: asymmetric anchor layout.
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_pos = np.array([5.0, 5.0])
 
     print(f"\nAnchor positions:\n{anchors}")
@@ -73,7 +79,7 @@ def example_toa_with_noise():
 
     np.random.seed(42)
 
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_pos = np.array([3.0, 7.0])
 
     print(f"\nTrue position: {true_pos}")
@@ -118,7 +124,7 @@ def example_toa_with_clock_bias():
     print("Example 3: Joint Position and Clock Bias Estimation")
     print("=" * 70)
 
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_pos = np.array([5.0, 5.0])
 
     # Define clock bias in SECONDS (timing domain)
@@ -200,7 +206,7 @@ def example_rss_positioning():
 
     # RSS-based positioning example
     print("\nRSS-Based Positioning:")
-    anchors = np.array([[0, 0], [10, 0], [10, 10], [0, 10]], dtype=float)
+    anchors = DEFAULT_ANCHORS_2D.copy()
     true_pos = np.array([5.0, 5.0])
 
     rss_measurements = []
@@ -457,12 +463,15 @@ def example_wls_vs_ls():
 
     np.random.seed(42)
 
-    # Deliberately asymmetric layout: three close anchors on the left,
-    # one distant anchor on the right.
+    # Deliberately asymmetric layout:
+    # - three anchors clustered on the lower-left
+    # - one anchor far to the upper-right
+    # - user position shifted toward the far anchor side
+    # This avoids near-symmetry and emphasizes WLS benefit.
     anchors = np.array(
-        [[0, 0], [0, 8], [2, 4], [15, 5]], dtype=float,
+        [[0, 0], [1, 9], [4, 2], [19, 15]], dtype=float,
     )
-    true_pos = np.array([6.0, 4.0])
+    true_pos = np.array([11.5, 6.5])
 
     # Per-anchor noise std (far anchor has much higher noise)
     sigma_per_anchor = np.array([0.1, 0.1, 0.1, 0.8])
@@ -481,7 +490,7 @@ def example_wls_vs_ls():
         )
         noisy_ranges = true_ranges + np.random.randn(len(anchors)) * sigma_per_anchor
 
-        init = np.array([5.0, 5.0])
+        init = np.array([8.0, 8.0])
 
         pos_ls, info_ls = TOAPositioner(anchors, method="iterative_ls").solve(
             noisy_ranges, initial_guess=init,
@@ -540,9 +549,11 @@ def main():
 
     fig = plot_toa_positioning(anchors1, true_pos1, est_pos1, info1["history"])
     plt.savefig(
-        "ch4_rf_point_positioning/toa_positioning_example.png", dpi=150, bbox_inches="tight"
+        "ch4_rf_point_positioning/figs/toa_positioning_example.png",
+        dpi=150,
+        bbox_inches="tight",
     )
-    print("\nFigure saved: toa_positioning_example.png")
+    print("\nFigure saved: ch4_rf_point_positioning/figs/toa_positioning_example.png")
 
     plt.show()
 
diff --git a/ch4_rf_point_positioning/figs/ch4_aoa_geometry.png b/ch4_rf_point_positioning/figs/ch4_aoa_geometry.png
index 798152f..04f93b5 100644
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diff --git a/ch4_rf_point_positioning/figs/ch4_rf_comparison.png b/ch4_rf_point_positioning/figs/ch4_rf_comparison.png
index 42b1512..fc35a78 100644
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diff --git a/ch4_rf_point_positioning/figs/closed_form_comparison.png b/ch4_rf_point_positioning/figs/closed_form_comparison.png
index 5e05892..7847e11 100644
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diff --git a/ch4_rf_point_positioning/figs/tdoa_covariance_matrix.png b/ch4_rf_point_positioning/figs/tdoa_covariance_matrix.png
index 8ab1666..9977fef 100644
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diff --git a/ch4_rf_point_positioning/figs/toa_positioning_example.png b/ch4_rf_point_positioning/figs/toa_positioning_example.png
index 48ab878..d8c1804 100644
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