diff --git a/generate_radar_data.py b/generate_radar_data.py
index 1f32056..c9fa96d 100644
--- a/generate_radar_data.py
+++ b/generate_radar_data.py
@@ -1,77 +1,53 @@
-import random
-
-def generate_comparison_data(num_points=10):
-    # ================= 1. 初始状态与运动趋势 (真值配置) =================
-    # 距离 (km): 模拟目标从 12.5km 处靠近 (参考文件中远距离测试)
-    start_dist = 12.500 
-    dist_speed = -0.020  # 负数表示靠近 (每点移动20米)
-    
-    # 方位 (度): 模拟目标在 145度 方向缓慢右移
-    start_az = 145.20
-    az_speed = 0.05      # 缓慢变化
-    
-    # 俯仰 (度): 模拟低空飞行，角度很小
-    start_el = 0.45
-    el_speed = 0.002     # 几乎平飞
-
-    # ================= 2. 雷达测量误差 (噪声配置) =================
-    # 参考依据：文件中的RMS指标 (方位<=0.3度, 距离<=15m)
-    # 这里的 sigma 是标准差，决定了测量值的抖动幅度
-    sigma_az = 0.12      # 方位抖动 (度)
-    sigma_el = 0.15      # 俯仰抖动 (度)
-    sigma_dist = 0.008   # 距离抖动 (km), 0.008km = 8米
-
-    # ================= 生成逻辑 =================
-    
-    # 容器
-    truth_az, truth_el, truth_dist = [], [], []
-    meas_az, meas_el, meas_dist = [], [], []
-
-    current_d = start_dist
-    current_a = start_az
-    current_e = start_el
-
-    for _ in range(num_points):
-        # --- A. 生成真值 (平滑运动 + 极微小物理抖动) ---
-        t_d = current_d + random.gauss(0, 0.001) 
-        t_a = current_a + random.gauss(0, 0.01)
-        t_e = current_e + random.gauss(0, 0.005)
-        
-        # 存入真值列表 (保留格式)
-        truth_dist.append(f"{t_d:.3f}")
-        truth_az.append(f"{t_a:.2f}")
-        truth_el.append(f"{t_e:.2f}")
-
-        # --- B. 生成测量值 (真值 + 传感器噪声) ---
-        m_d = t_d + random.gauss(0, sigma_dist)
-        m_a = t_a + random.gauss(0, sigma_az)
-        m_e = t_e + random.gauss(0, sigma_el)
-
-        # 存入测量值列表
-        meas_dist.append(f"{m_d:.3f}")
-        meas_az.append(f"{m_a:.2f}")
-        meas_el.append(f"{m_e:.2f}")
-
-        # 更新下一步的基准位置
-        current_d += dist_speed
-        current_a += az_speed
-        current_e += el_speed
-
-    # ================= 3. 格式化输出 (方便复制) =================
-    print("\n" + "="*40)
-    print("【第一部分：真值数据 (Truth)】")
-    print("="*40)
-    print("方位(°)\t" + "\t".join(truth_az))
-    print("俯仰(°)\t" + "\t".join(truth_el))
-    print("距离(km)\t" + "\t".join(truth_dist))
-
-    print("\n" + "="*40)
-    print("【第二部分：测量结果 (Measured)】")
-    print("="*40)
-    print("方位(°)\t" + "\t".join(meas_az))
-    print("俯仰(°)\t" + "\t".join(meas_el))
-    print("距离(km)\t" + "\t".join(meas_dist))
-    print("="*40 + "\n")
-
-if __name__ == "__main__":
-    generate_comparison_data()
\ No newline at end of file
+import random
+import csv
+import math
+
+def generate_radar_test_data(filename="Radar_Accuracy_Test_Data.csv"):
+    # ================= 1. 项目参数配置 (依据QDGZ雷达报告) =================
+    # 依据报告 [Source 52] [Source 59]
+    # 精度测试要求：距离 1km~3km 范围内
+    # 精度指标：方位 <= 0.6°, 俯仰 <= 0.6°, 距离 <= 10m
+    
+    # --- 初始真值设定 ---
+    # 模拟目标：无人机 (RCS 0.01m2)
+    start_dist_km = 2.500      # 起始距离 2.5km (符合1-3km测试区间)
+    # 速度：模拟 V ≈ 10m/s (0.01 km/s)
+    speed_km_s = -0.010        # 负数表示靠近雷达
+    time_interval_s = 2.0      # 两次采样间隔 (模拟数据率)
+    
+    start_az = 45.50           # 初始方位
+    az_rate = 0.05             # 方位角变化率 (度/次)
+    
+    start_el = 2.50            # 初始俯仰 (低空目标)
+    el_rate = 0.01             # 俯仰角变化率
+    
+    num_points = 10            # 对应表A.3的10组数据
+
+    # --- 传感器噪声设定 (Standard Deviation) ---
+    # 为了满足 RMS 指标，标准差通常设为指标的 1/2 到 1/3 左右
+    # 距离精度指标 10m -> 设定噪声 std ≈ 4m (0.004km)
+    sigma_dist_km = 0.004 
+    # 角度精度指标 0.6° -> 设定噪声 std ≈ 0.2°
+    sigma_angle = 0.2     
+
+    # ================= 2. 数据生成逻辑 =================
+    data_rows = []
+    
+    # 用于事后计算RMS以验证数据是否合格
+    sq_err_dist = 0
+    sq_err_az = 0
+    sq_err_el = 0
+
+    current_d = start_dist_km
+    current_a = start_az
+    current_e = start_el
+
+    for i in range(1, num_points + 1):
+        # --- A. 生成真值 (平滑运动轨迹) ---
+        # 加入极微小的物理抖动(模拟真实飞行的不绝对平滑)
+        t_d = current_d + random.gauss(0, 0.0005) 
+        t_a = current_a + random.gauss(0, 0.005)
+        t_e = current_e + random.gauss(0, 0.005)
+        
+        # --- B. 生成雷达测量值 (真值 + 传感器高斯噪声) ---
+        m_d = t_d + random.gauss(0, sigma_dist_km)
\ No newline at end of file