fix: Replace mock/placeholder code with real implementations (ADR-011)

- csi_processor.py: Replace np.random.rand(10) Doppler placeholder with
  real temporal phase-difference FFT extraction from CSI history buffer.
  Returns zeros (not random) when insufficient history frames available.

- csi_extractor.py: Replace np.random.rand() fallbacks in ESP32 and
  Atheros parsers with proper data parsing (ESP32) and explicit error
  raising (Atheros). Add CSIExtractionError for clear failure reporting
  instead of silent random data substitution.

These are the two most critical mock eliminations identified in ADR-011.

https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714

v1/src/core/csi_processor.py

@@ -385,13 +385,54 @@ class CSIProcessor:
         return correlation_matrix
     
     def _extract_doppler_features(self, csi_data: CSIData) -> tuple:
-        """Extract Doppler and frequency domain features."""
-        # Simple Doppler estimation (would use history in real implementation)
-        doppler_shift = np.random.rand(10)  # Placeholder
-        
-        # Power spectral density
-        psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
-        
+        """Extract Doppler and frequency domain features from temporal CSI history.
+
+        Computes Doppler spectrum by analyzing temporal phase differences across
+        frames in self.csi_history, then applying FFT to obtain the Doppler shift
+        frequency components. If fewer than 2 history frames are available, returns
+        a zero-filled Doppler array (never random data).
+
+        Returns:
+            tuple: (doppler_shift, power_spectral_density) as numpy arrays
+        """
+        n_doppler_bins = 64
+
+        if len(self.csi_history) >= 2:
+            # Build temporal phase matrix from history frames
+            # Each row is the mean phase across antennas for one time step
+            history_list = list(self.csi_history)
+            phase_series = []
+            for frame in history_list:
+                # Average phase across antennas to get per-subcarrier phase
+                if frame.phase.ndim == 2:
+                    phase_series.append(np.mean(frame.phase, axis=0))
+                else:
+                    phase_series.append(frame.phase.flatten())
+
+            phase_matrix = np.array(phase_series)  # shape: (num_frames, num_subcarriers)
+
+            # Compute temporal phase differences between consecutive frames
+            phase_diffs = np.diff(phase_matrix, axis=0)  # shape: (num_frames-1, num_subcarriers)
+
+            # Average phase diff across subcarriers for each time step
+            mean_phase_diff = np.mean(phase_diffs, axis=1)  # shape: (num_frames-1,)
+
+            # Apply FFT to get Doppler spectrum from the temporal phase differences
+            doppler_spectrum = np.abs(scipy.fft.fft(mean_phase_diff, n=n_doppler_bins)) ** 2
+
+            # Normalize to prevent scale issues
+            max_val = np.max(doppler_spectrum)
+            if max_val > 0:
+                doppler_spectrum = doppler_spectrum / max_val
+
+            doppler_shift = doppler_spectrum
+        else:
+            # Not enough history for Doppler estimation -- return zeros, never random
+            doppler_shift = np.zeros(n_doppler_bins)
+
+        # Power spectral density of the current frame
+        psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128)) ** 2
+
         return doppler_shift, psd
     
     def _analyze_motion_patterns(self, features: CSIFeatures) -> float: