Expand metrology telemetry with 10 kinematic and physical array statistics for rigorous CFAR evaluation

scripts/ISOLATE/model_wrapper.py

@@ -116,6 +116,7 @@ class ShenronRadarModel:
             
             # 5. Capture Advanced Metrology
             # Calculate SNR and basic noise stats for the Frame Metrics
+            self.last_pcd = rich_pcd
             self.last_metrology = metrology
             
             return rich_pcd
@@ -148,17 +149,76 @@ class ShenronRadarModel:
             
         rd = self.last_metrology['rd_heatmap']
         noise = self.last_metrology['threshold_matrix']
+        ra = self.last_metrology['ra_heatmap']
         
         peak_mag = np.max(rd)
         avg_noise = np.mean(noise)
         snr = 10 * np.log10(peak_mag / avg_noise) if avg_noise > 0 else 0
         
-        return {
+        metrics = {
             "peak_magnitude": float(peak_mag),
             "avg_noise_floor": float(avg_noise),
             "peak_snr_db": float(snr),
-            "active_bins": int(np.sum(rd > avg_noise))
+            "active_bins": int(np.sum(rd > noise))
         }
+        
+        try:
+            # 1. Point Cloud Metrics
+            pcd = getattr(self, 'last_pcd', np.array([]))
+            cfar_count = len(pcd)
+            
+            if cfar_count > 0:
+                ranges = np.linalg.norm(pcd[:, :2], axis=1)
+                vels = pcd[:, 3]
+                farthest = np.max(ranges)
+                closest = np.min(ranges)
+                mean_doppler = np.mean(np.abs(vels))
+                doppler_var = np.var(vels)
+            else:
+                farthest = 0.0
+                closest = 0.0
+                mean_doppler = 0.0
+                doppler_var = 0.0
+            
+            metrics["cfar_target_count"] = int(cfar_count)
+            metrics["farthest_target_m"] = float(farthest)
+            metrics["closest_target_m"] = float(closest)
+            metrics["mean_absolute_doppler"] = float(mean_doppler)
+            metrics["doppler_variance"] = float(doppler_var)
+            
+            # 2. Physical Array Metrics
+            min_hit = np.min(rd[rd > noise]) if cfar_count > 0 else avg_noise
+            dyn_range = 10 * np.log10(peak_mag / min_hit) if peak_mag > 0 and min_hit > 0 else 0.0
+            
+            r_axis = self.processor.rangeAxis
+            v_axis = self.processor.velAxis
+            r_mask = r_axis < 5.0
+            v_mask = np.abs(v_axis) < 1.0
+            
+            if len(r_axis) == rd.shape[0] and len(v_axis) == rd.shape[1]:
+                ego_power = np.sum(rd[np.ix_(r_mask, v_mask)])
+            else:
+                ego_power = 0.0
+                
+            clutter_bins = (rd > avg_noise) & (rd <= noise)
+            clutter_ratio = np.sum(clutter_bins) / rd.size
+            avg_clutter = np.mean(rd[clutter_bins]) if np.any(clutter_bins) else avg_noise
+            scr = 10 * np.log10(peak_mag / avg_clutter) if avg_clutter > 0 else snr
+            
+            metrics["dynamic_range_db"] = float(dyn_range)
+            metrics["ego_vicinity_power"] = float(ego_power)
+            metrics["clutter_ratio"] = float(clutter_ratio)
+            metrics["signal_to_clutter_ratio_db"] = float(scr)
+            
+            # 3. Array Health
+            az_var = np.mean(np.var(ra, axis=1)) if ra is not None else 0.0
+            metrics["azimuth_variance"] = float(az_var)
+            
+        except Exception as e:
+            print(f"[WARNING] Advanced metrology calculation failed: {e}")
+            pass
+            
+        return metrics
 
     def get_radar_specs(self):
         """Return radar hardware specs for dashboard telemetry."""