VIDEO-005: add full offline QPSK IQ video receiver

protocol/video005_carrier.py

@@ -0,0 +1,68 @@
+"""Оценка несущей по преамбуле и слежение за фазой QPSK-пакета."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import numpy as np
+
+from protocol.video003_modem import (
+    PHY_HEADER_SYMBOLS,
+    PREAMBLE_SYMBOLS,
+    _estimate_phase_frequency,
+    bpsk_symbols,
+    known_preamble_bits,
+)
+
+
+BODY_START = PREAMBLE_SYMBOLS + PHY_HEADER_SYMBOLS
+TRACK_BLOCK_SYMBOLS = 64
+
+
+@dataclass(frozen=True)
+class PilotEstimate:
+    phase_rad: float
+    frequency_hz: float
+    phase_step_rad: float
+    quadrant: int
+
+
+def estimate_from_preamble(symbols: np.ndarray, symbol_rate: int) -> PilotEstimate:
+    """Оценивает частоту и абсолютную фазу, включая поворот на 90°/180°."""
+    if symbols.ndim != 1 or len(symbols) < PREAMBLE_SYMBOLS:
+        raise ValueError("недостаточно символов известной преамбулы")
+    known = bpsk_symbols(known_preamble_bits())
+    phase, phase_step = _estimate_phase_frequency(symbols[:PREAMBLE_SYMBOLS], known)
+    return PilotEstimate(
+        phase,
+        phase_step * symbol_rate / (2 * math.pi),
+        phase_step,
+        round(phase / (math.pi / 2)) % 4,
+    )
+
+
+def correct_pilot_estimate(symbols: np.ndarray, estimate: PilotEstimate) -> np.ndarray:
+    """Убирает оценённые фазу и CFO из всего найденного пакета."""
+    indexes = np.arange(len(symbols), dtype=np.float64)
+    correction = np.exp(-1j * (estimate.phase_rad + estimate.phase_step_rad * indexes))
+    return symbols * correction.astype(np.complex64)
+
+
+def track_qpsk_body(symbols: np.ndarray) -> tuple[np.ndarray, float]:
+    """Причинное слежение: решение строится из текущего QPSK, без эталонных битов."""
+    corrected = symbols.copy()
+    phase_state = 0.0
+    errors: list[float] = []
+    for start in range(BODY_START, len(symbols), TRACK_BLOCK_SYMBOLS):
+        stop = min(start + TRACK_BLOCK_SYMBOLS, len(symbols))
+        block = symbols[start:stop] * np.exp(-1j * phase_state)
+        decisions = (np.where(block.real >= 0, 1.0, -1.0)
+                     + 1j * np.where(block.imag >= 0, 1.0, -1.0)).astype(np.complex64)
+        error = float(np.angle(np.sum(block * np.conj(decisions))))
+        error = float(np.clip(error, -0.2, 0.2))
+        phase_state += 0.35 * error
+        corrected[start:stop] = symbols[start:stop] * np.exp(-1j * phase_state)
+        errors.append(error)
+    root_mean_square = math.sqrt(sum(error * error for error in errors) / len(errors)) if errors else 0.0
+    return corrected, root_mean_square