diff --git a/src/core/sig_n_medi_collect.py b/src/core/sig_n_medi_collect.py
index be703ee..4c0d928 100644
--- a/src/core/sig_n_medi_collect.py
+++ b/src/core/sig_n_medi_collect.py
@@ -27,6 +27,9 @@ class Signal:
         self.signal = []
         self.signal_abs = []
         self.last_packet_ts = None
+        self.metric_mode = os.getenv('signal_metric_mode', 'fft_top_bins').strip().lower()
+        self.fft_top_bins = max(1, int(os.getenv('signal_fft_top_bins', '2048')))
+        self.fft_window = os.getenv('signal_fft_window', 'hann').strip().lower()
 
     def get_signal(self):
         """
@@ -47,6 +50,30 @@ class Signal:
         self.signal_abs = []
         self.last_packet_ts = None
 
+    def _build_window(self, size: int) -> np.ndarray:
+        if self.fft_window in {'', 'none', 'rect', 'rectangular'}:
+            return np.ones(size, dtype=np.float32)
+        if self.fft_window == 'hann':
+            return np.hanning(size).astype(np.float32, copy=False)
+        raise ValueError(f'unsupported fft window: {self.fft_window}')
+
+    def _compute_iq_power(self, samples: np.ndarray, signal_abs: np.ndarray) -> float:
+        if self.conv_method == 'max':
+            return float(np.max(signal_abs * signal_abs))
+
+        if self.metric_mode in {'fft', 'fft_top_bins', 'top_bins'}:
+            window = self._build_window(samples.size)
+            windowed = samples.astype(np.complex64, copy=False) * window
+            spectrum = np.fft.fft(windowed)
+            power_bins = (np.abs(spectrum) ** 2).astype(np.float32, copy=False)
+            power_bins /= max(float(np.sum(window * window)), 1.0)
+
+            bins_to_keep = min(self.fft_top_bins, power_bins.size)
+            top_bins = np.partition(power_bins, power_bins.size - bins_to_keep)[-bins_to_keep:]
+            return float(np.mean(top_bins))
+
+        return float(np.mean(signal_abs * signal_abs))
+
     def signal_preprocessing(self, length) -> float:
         """
         Предобработка сигнала.
@@ -64,10 +91,7 @@ class Signal:
             signal = np.array([i, q], dtype=np.float32)
             signal_abs = np.sqrt(i * i + q * q).astype(np.float32, copy=False)
 
-            if self.conv_method == 'max':
-                power = float(np.max(signal_abs * signal_abs))
-            else:
-                power = float(np.mean(signal_abs * signal_abs))
+            power = self._compute_iq_power(samples, signal_abs)
 
             result = 10.0 * math.log10(max(power, 1e-20))
             self.signal = signal