mirror of
https://github.com/NawfalMotii79/PLFM_RADAR.git
synced 2026-06-08 22:47:16 +00:00
Jason
3d2ffc3f2c
cosim_dir revival:
- gen_realdata_hex.py: also emit decimated_range_{i,q}.npy (48x512)
and doppler_map_{i,q}.npy (512x48) at production dimensions; the
same Python pipeline that produces the RTL .hex stimuli now writes
the .npy intermediates v7.replay COSIM_DIR loads. Replaces the
workflow lost when golden_reference.py was deleted in e8b495c
- test_v7.py: update test_get_frame_cosim shape from pre-PR-O.6
(64,32) to (NUM_RANGE_BINS, NUM_DOPPLER_BINS)
- check in 4 .npy reference files (~400 KB, deterministic SCENE_SEED=42)
Ruff lint cleanup (was 66 errors; now 0):
- pyproject.toml: ignore T20 in tb/cosim/**.py (CLI tools)
- compare_independent.py: drop redundant int() casts (RUF046),
swap try/except scipy import for importlib.util.find_spec,
remove dead duplicate np import, ASCII-ize comment unicode,
wrap E501 format strings
- fpga_reference.py: drop unused fs arg from nco_reference,
collapse if/else to ternary, mark _out_im unused
- v7/processing.py: ASCII-ize x in docstring, collapse if-branches
- {dashboard,software_fpga,workers,radar_protocol}.py: wrap E501
- test_v7.py: ASCII-ize comment unicode, _alias renames where unused
Result: test_v7 100/100 (0 skips on radar_venv, was 9 graceful
skips); 5 cosim_dir orphan tests now active and passing.
771 lines
27 KiB
Python
771 lines
27 KiB
Python
"""
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v7.processing — Radar signal processing and GPS parsing.
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Classes:
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- RadarProcessor — dual-CPI fusion, multi-PRF unwrap, DBSCAN clustering,
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association, Kalman tracking
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- USBPacketParser — parse GPS text/binary frames from STM32 CDC
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Note: RadarPacketParser (old A5/C3 sync + CRC16 format) was removed.
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All packet parsing now uses production RadarProtocol (0xAA/0xBB format)
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from radar_protocol.py.
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"""
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import struct
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import time
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import logging
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import math
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import numpy as np
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from .models import (
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RadarTarget, GPSData, ProcessingConfig,
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SCIPY_AVAILABLE, SKLEARN_AVAILABLE, FILTERPY_AVAILABLE,
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)
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if SKLEARN_AVAILABLE:
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from sklearn.cluster import DBSCAN
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if FILTERPY_AVAILABLE:
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from filterpy.kalman import KalmanFilter
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if SCIPY_AVAILABLE:
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from scipy.signal import windows as scipy_windows
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logger = logging.getLogger(__name__)
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# =============================================================================
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# Utility: pitch correction (Bug #4 fix — was never defined in V6)
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# =============================================================================
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def apply_pitch_correction(raw_elevation: float, pitch: float) -> float:
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"""
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Apply platform pitch correction to a raw elevation angle.
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Returns the corrected elevation = raw_elevation - pitch.
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"""
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return raw_elevation - pitch
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# =============================================================================
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# Radar Processor — signal-level processing & tracking pipeline
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# =============================================================================
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class RadarProcessor:
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"""Full radar processing pipeline: fusion, clustering, association, tracking."""
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def __init__(self):
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self.range_doppler_map = np.zeros((1024, 32))
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self.detected_targets: list[RadarTarget] = []
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self.track_id_counter: int = 0
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self.tracks: dict[int, dict] = {}
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self.frame_count: int = 0
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self.config = ProcessingConfig()
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# MTI state: store previous frames for cancellation
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self._mti_history: list[np.ndarray] = []
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# ---- Configuration -----------------------------------------------------
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def set_config(self, config: ProcessingConfig):
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"""Update the processing configuration and reset MTI history if needed."""
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old_order = self.config.mti_order
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self.config = config
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if config.mti_order != old_order:
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self._mti_history.clear()
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# ---- Windowing ----------------------------------------------------------
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@staticmethod
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def apply_window(data: np.ndarray, window_type: str) -> np.ndarray:
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"""Apply a window function along each column (slow-time dimension).
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*data* shape: (range_bins, doppler_bins). Window is applied along
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axis-1 (Doppler / slow-time).
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"""
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if window_type == "None" or not window_type:
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return data
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n = data.shape[1]
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if n < 2:
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return data
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if SCIPY_AVAILABLE:
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wtype = window_type.lower()
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if wtype == "hann":
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w = scipy_windows.hann(n, sym=False)
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elif wtype == "hamming":
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w = scipy_windows.hamming(n, sym=False)
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elif wtype == "blackman":
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w = scipy_windows.blackman(n)
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elif wtype == "kaiser":
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w = scipy_windows.kaiser(n, beta=14)
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elif wtype == "chebyshev":
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w = scipy_windows.chebwin(n, at=80)
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else:
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w = np.ones(n)
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else:
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# Fallback: numpy Hann
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wtype = window_type.lower()
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if wtype == "hann":
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w = np.hanning(n)
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elif wtype == "hamming":
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w = np.hamming(n)
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elif wtype == "blackman":
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w = np.blackman(n)
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else:
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w = np.ones(n)
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return data * w[np.newaxis, :]
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# ---- DC Notch (zero-Doppler removal) ------------------------------------
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@staticmethod
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def dc_notch(data: np.ndarray) -> np.ndarray:
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"""Remove the DC (zero-Doppler) component by subtracting the
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mean along the slow-time axis for each range bin."""
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return data - np.mean(data, axis=1, keepdims=True)
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# ---- MTI (Moving Target Indication) -------------------------------------
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def mti_filter(self, frame: np.ndarray) -> np.ndarray:
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"""Apply MTI cancellation of order 1, 2, or 3.
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Order-1: y[n] = x[n] - x[n-1]
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Order-2: y[n] = x[n] - 2*x[n-1] + x[n-2]
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Order-3: y[n] = x[n] - 3*x[n-1] + 3*x[n-2] - x[n-3]
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The internal history buffer stores up to 3 previous frames.
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"""
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order = self.config.mti_order
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self._mti_history.append(frame.copy())
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# Trim history to order + 1 frames
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max_len = order + 1
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if len(self._mti_history) > max_len:
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self._mti_history = self._mti_history[-max_len:]
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if len(self._mti_history) < order + 1:
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# Not enough history yet — return zeros (suppress output)
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return np.zeros_like(frame)
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h = self._mti_history
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if order == 1:
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return h[-1] - h[-2]
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if order == 2:
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return h[-1] - 2.0 * h[-2] + h[-3]
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if order == 3:
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return h[-1] - 3.0 * h[-2] + 3.0 * h[-3] - h[-4]
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return h[-1] - h[-2]
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# ---- CFAR (Constant False Alarm Rate) -----------------------------------
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@staticmethod
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def cfar_1d(signal_vec: np.ndarray, guard: int, train: int,
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threshold_factor: float, cfar_type: str = "CA-CFAR") -> np.ndarray:
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"""1-D CFAR detector.
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Parameters
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----------
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signal_vec : 1-D array (power in linear scale)
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guard : number of guard cells on each side
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train : number of training cells on each side
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threshold_factor : multiplier on estimated noise level
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cfar_type : CA-CFAR, OS-CFAR, GO-CFAR, or SO-CFAR
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Returns
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-------
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detections : boolean array, True where target detected
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"""
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n = len(signal_vec)
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detections = np.zeros(n, dtype=bool)
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half = guard + train
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for i in range(half, n - half):
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# Leading training cells
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lead = signal_vec[i - half: i - guard]
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# Lagging training cells
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lag = signal_vec[i + guard + 1: i + half + 1]
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if cfar_type == "CA-CFAR":
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noise = (np.sum(lead) + np.sum(lag)) / (2 * train)
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elif cfar_type == "GO-CFAR":
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noise = max(np.mean(lead), np.mean(lag))
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elif cfar_type == "SO-CFAR":
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noise = min(np.mean(lead), np.mean(lag))
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elif cfar_type == "OS-CFAR":
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all_train = np.concatenate([lead, lag])
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all_train.sort()
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k = int(0.75 * len(all_train)) # 75th percentile
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noise = all_train[min(k, len(all_train) - 1)]
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else:
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noise = (np.sum(lead) + np.sum(lag)) / (2 * train)
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threshold = noise * threshold_factor
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if signal_vec[i] > threshold:
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detections[i] = True
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return detections
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def cfar_2d(self, rdm: np.ndarray) -> np.ndarray:
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"""Apply 1-D CFAR along each range bin (across Doppler dimension).
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Returns a boolean mask of the same shape as *rdm*.
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"""
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cfg = self.config
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mask = np.zeros_like(rdm, dtype=bool)
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for r in range(rdm.shape[0]):
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row = rdm[r, :]
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if row.max() > 0:
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mask[r, :] = self.cfar_1d(
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row, cfg.cfar_guard_cells, cfg.cfar_training_cells,
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cfg.cfar_threshold_factor, cfg.cfar_type,
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)
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return mask
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# ---- Full processing pipeline -------------------------------------------
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def process_frame(self, raw_frame: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
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"""Run the full signal processing chain on a Range x Doppler frame.
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Parameters
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----------
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raw_frame : 2-D array (range_bins x doppler_bins), complex or real
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Returns
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-------
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(processed_rdm, detection_mask)
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processed_rdm — processed Range-Doppler map (power, linear)
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detection_mask — boolean mask of CFAR / threshold detections
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"""
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cfg = self.config
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data = raw_frame.astype(np.float64)
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# 1. DC Notch
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if cfg.dc_notch_enabled:
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data = self.dc_notch(data)
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# 2. Windowing (before FFT — applied along slow-time axis)
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if cfg.window_type and cfg.window_type != "None":
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data = self.apply_window(data, cfg.window_type)
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# 3. MTI
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if cfg.mti_enabled:
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data = self.mti_filter(data)
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# 4. Power (magnitude squared)
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power = np.abs(data) ** 2
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power = np.maximum(power, 1e-20) # avoid log(0)
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# 5. CFAR detection or simple threshold
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if cfg.cfar_enabled:
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detection_mask = self.cfar_2d(power)
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else:
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# Simple threshold: convert dB threshold to linear
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power_db = 10.0 * np.log10(power)
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noise_floor = np.median(power_db)
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detection_mask = power_db > (noise_floor + cfg.detection_threshold_db)
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# Update stored RDM
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self.range_doppler_map = power
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self.frame_count += 1
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return power, detection_mask
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# ---- Dual-CPI fusion ---------------------------------------------------
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@staticmethod
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def dual_cpi_fusion(range_profiles_1: np.ndarray,
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range_profiles_2: np.ndarray) -> np.ndarray:
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"""Dual-CPI fusion for better detection."""
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return np.mean(range_profiles_1, axis=0) + np.mean(range_profiles_2, axis=0)
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# ---- DBSCAN clustering -------------------------------------------------
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@staticmethod
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def clustering(detections: list[RadarTarget],
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eps: float = 100, min_samples: int = 2) -> list:
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"""DBSCAN clustering of detections (requires sklearn)."""
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if not SKLEARN_AVAILABLE or len(detections) == 0:
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return []
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points = np.array([[d.range, d.velocity] for d in detections])
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labels = DBSCAN(eps=eps, min_samples=min_samples).fit(points).labels_
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clusters = []
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for label in set(labels):
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if label == -1:
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continue
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cluster_points = points[labels == label]
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clusters.append({
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"center": np.mean(cluster_points, axis=0),
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"points": cluster_points,
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"size": len(cluster_points),
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})
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return clusters
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# ---- Association -------------------------------------------------------
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def association(self, detections: list[RadarTarget],
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_clusters: list) -> list[RadarTarget]:
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"""Associate detections to existing tracks (nearest-neighbour)."""
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associated = []
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for det in detections:
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best_track = None
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min_dist = float("inf")
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for tid, track in self.tracks.items():
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dist = math.sqrt(
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(det.range - track["state"][0]) ** 2
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+ (det.velocity - track["state"][2]) ** 2
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)
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if dist < min_dist and dist < 500:
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min_dist = dist
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best_track = tid
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if best_track is not None:
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det.track_id = best_track
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else:
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det.track_id = self.track_id_counter
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self.track_id_counter += 1
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associated.append(det)
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return associated
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# ---- Kalman tracking ---------------------------------------------------
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def tracking(self, associated_detections: list[RadarTarget]):
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"""Kalman filter tracking (requires filterpy)."""
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if not FILTERPY_AVAILABLE:
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return
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now = time.time()
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for det in associated_detections:
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if det.track_id not in self.tracks:
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kf = KalmanFilter(dim_x=4, dim_z=2)
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kf.x = np.array([det.range, 0, det.velocity, 0])
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kf.F = np.array([
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[1, 1, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 1],
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[0, 0, 0, 1],
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])
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kf.H = np.array([
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[1, 0, 0, 0],
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[0, 0, 1, 0],
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])
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kf.P *= 1000
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kf.R = np.diag([10, 1])
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kf.Q = np.eye(4) * 0.1
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self.tracks[det.track_id] = {
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"filter": kf,
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"state": kf.x,
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"last_update": now,
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"hits": 1,
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}
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else:
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track = self.tracks[det.track_id]
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track["filter"].predict()
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track["filter"].update([det.range, det.velocity])
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track["state"] = track["filter"].x
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track["last_update"] = now
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track["hits"] += 1
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# Prune stale tracks (> 5 s without update)
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stale = [tid for tid, t in self.tracks.items()
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if now - t["last_update"] > 5.0]
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for tid in stale:
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del self.tracks[tid]
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# =============================================================================
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# USB / GPS Packet Parser
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# =============================================================================
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class USBPacketParser:
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"""
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Parse GPS (and general) data arriving from the STM32 via USB CDC.
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Supports:
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- Text format: ``GPS:lat,lon,alt,pitch\\r\\n``
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- Binary format: ``GPSB`` header, 30 bytes total
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"""
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def __init__(self):
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pass
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def parse_gps_data(self, data: bytes) -> GPSData | None:
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"""Attempt to parse GPS data from a raw USB CDC frame."""
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if not data:
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return None
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try:
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# Text format: "GPS:lat,lon,alt,pitch\r\n"
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text = data.decode("utf-8", errors="ignore").strip()
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if text.startswith("GPS:"):
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parts = text.split(":")[1].split(",")
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if len(parts) >= 4:
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return GPSData(
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latitude=float(parts[0]),
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longitude=float(parts[1]),
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altitude=float(parts[2]),
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pitch=float(parts[3]),
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timestamp=time.time(),
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)
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# Binary format: [GPSB 4][lat 8][lon 8][alt 4][pitch 4][CRC 2] = 30 bytes
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if len(data) >= 30 and data[0:4] == b"GPSB":
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return self._parse_binary_gps(data)
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except (ValueError, struct.error) as e:
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logger.error(f"Error parsing GPS data: {e}")
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return None
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@staticmethod
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def _parse_binary_gps(data: bytes) -> GPSData | None:
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"""Parse 30-byte binary GPS frame."""
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try:
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if len(data) < 30:
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return None
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# Simple checksum CRC
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crc_rcv = (data[28] << 8) | data[29]
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crc_calc = sum(data[0:28]) & 0xFFFF
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if crc_rcv != crc_calc:
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logger.warning("GPS binary CRC mismatch")
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return None
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lat = struct.unpack(">d", data[4:12])[0]
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lon = struct.unpack(">d", data[12:20])[0]
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alt = struct.unpack(">f", data[20:24])[0]
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pitch = struct.unpack(">f", data[24:28])[0]
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return GPSData(
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latitude=lat,
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longitude=lon,
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altitude=alt,
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pitch=pitch,
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timestamp=time.time(),
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)
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except (ValueError, struct.error) as e:
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logger.error(f"Error parsing binary GPS: {e}")
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return None
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# ============================================================================
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# Utility: polar → geographic coordinate conversion
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# ============================================================================
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def polar_to_geographic(
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radar_lat: float,
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radar_lon: float,
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range_m: float,
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azimuth_deg: float,
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) -> tuple:
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"""Convert polar (range, azimuth) relative to radar → (lat, lon).
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azimuth_deg: 0 = North, clockwise.
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"""
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r_earth = 6_371_000.0 # Earth radius in metres
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lat1 = math.radians(radar_lat)
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lon1 = math.radians(radar_lon)
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bearing = math.radians(azimuth_deg)
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lat2 = math.asin(
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math.sin(lat1) * math.cos(range_m / r_earth)
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+ math.cos(lat1) * math.sin(range_m / r_earth) * math.cos(bearing)
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)
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lon2 = lon1 + math.atan2(
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math.sin(bearing) * math.sin(range_m / r_earth) * math.cos(lat1),
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math.cos(range_m / r_earth) - math.sin(lat1) * math.sin(lat2),
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)
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return (math.degrees(lat2), math.degrees(lon2))
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|
|
|
|
|
# ============================================================================
|
|
# Shared target extraction (used by both RadarDataWorker and ReplayWorker)
|
|
# ============================================================================
|
|
|
|
def extract_targets_from_frame(
|
|
frame,
|
|
range_resolution: float = 1.0,
|
|
velocity_resolution: float = 1.0,
|
|
gps: GPSData | None = None,
|
|
) -> list[RadarTarget]:
|
|
"""Extract RadarTarget list from a RadarFrame's detection mask.
|
|
|
|
This is the bin-to-physical conversion + geo-mapping shared between
|
|
the live and replay data paths.
|
|
|
|
Parameters
|
|
----------
|
|
frame : RadarFrame
|
|
Frame with populated ``detections``, ``magnitude``, ``range_doppler_i/q``.
|
|
range_resolution : float
|
|
Meters per range bin.
|
|
velocity_resolution : float
|
|
m/s per Doppler bin.
|
|
gps : GPSData | None
|
|
GPS position for geo-mapping (latitude/longitude).
|
|
|
|
Returns
|
|
-------
|
|
list[RadarTarget]
|
|
One target per detection cell.
|
|
"""
|
|
det_indices = np.argwhere(frame.detections > 0)
|
|
n_doppler = frame.detections.shape[1] if frame.detections.ndim == 2 else 48
|
|
doppler_center = n_doppler // 2
|
|
|
|
targets: list[RadarTarget] = []
|
|
for idx in det_indices:
|
|
rbin, dbin = int(idx[0]), int(idx[1])
|
|
mag = float(frame.magnitude[rbin, dbin])
|
|
snr = 10.0 * math.log10(max(mag, 1.0)) if mag > 0 else 0.0
|
|
|
|
range_m = float(rbin) * range_resolution
|
|
velocity_ms = float(dbin - doppler_center) * velocity_resolution
|
|
|
|
lat, lon, azimuth, elevation = 0.0, 0.0, 0.0, 0.0
|
|
if gps is not None:
|
|
azimuth = gps.heading
|
|
# Spread detections across ±15° sector for single-beam radar
|
|
if len(det_indices) > 1:
|
|
spread = (dbin - doppler_center) / max(doppler_center, 1) * 15.0
|
|
azimuth = gps.heading + spread
|
|
lat, lon = polar_to_geographic(
|
|
gps.latitude, gps.longitude, range_m, azimuth,
|
|
)
|
|
|
|
targets.append(RadarTarget(
|
|
id=len(targets),
|
|
range=range_m,
|
|
velocity=velocity_ms,
|
|
azimuth=azimuth,
|
|
elevation=elevation,
|
|
latitude=lat,
|
|
longitude=lon,
|
|
snr=snr,
|
|
timestamp=frame.timestamp,
|
|
))
|
|
return targets
|
|
|
|
|
|
# ============================================================================
|
|
# PR-Q.5 — 3-PRI Chinese-Remainder Doppler unfolding (audit C-5)
|
|
# ============================================================================
|
|
|
|
def unfold_velocity_crt(
|
|
v_meas_per_sf: list[float],
|
|
v_unamb_per_sf: list[float],
|
|
v_res_per_sf: list[float] | None = None,
|
|
max_alias_k: int = 6,
|
|
tol_factor: float = 0.5,
|
|
) -> tuple[float, str, list[float]]:
|
|
"""3-PRI Chinese-Remainder Doppler velocity unfolding.
|
|
|
|
Each per-subframe FFT measures v_true folded into a signed
|
|
[-v_unamb_i, +v_unamb_i] interval (the standard fftshift
|
|
convention). With 3 coprime PRIs (PR-Q ladder: 175/161/167 us,
|
|
giving v_unamb ≈ 40.79/44.34/42.79 m/s), brute-force search over
|
|
alias depth k_0 ∈ [-K, K] generates candidates
|
|
``v_true = v_meas_0 + k_0 · 2 · v_unamb_0``. A candidate is
|
|
*valid* when it folds back into all other active PRIs to within
|
|
``tol_factor * max(v_res)``.
|
|
|
|
Args:
|
|
v_meas_per_sf: signed velocity measurement per active sub-frame
|
|
(m/s), already folded by the FFT. Length 1, 2, or 3.
|
|
v_unamb_per_sf: per-sub-frame v_unamb (m/s), same length.
|
|
v_res_per_sf: per-sub-frame v_res (m/s). If None, assumes
|
|
``v_res = v_unamb / 8`` (matches chirps_per_subframe = 16).
|
|
max_alias_k: alias search depth in PRI-0 fold steps. K=6 covers
|
|
±6 · 2 · v_unamb_0 ≈ ±490 m/s, well above
|
|
``WaveformConfig.extended_max_velocity_mps_crt(K=6) ≈ ±266``.
|
|
tol_factor: per-PRI agreement tolerance, in units of max(v_res).
|
|
1.0 = within one bin width.
|
|
|
|
Returns:
|
|
(v_est, confidence, alias_set):
|
|
|
|
- v_est (m/s): best-fit unfolded velocity. Falls back to PRI-0's
|
|
measurement if no candidate satisfies all PRIs within tolerance.
|
|
- confidence: ``"CONFIRMED"`` / ``"LIKELY"`` / ``"AMBIGUOUS"``.
|
|
* CONFIRMED — 3-PRI input, exactly one fold within tolerance.
|
|
* LIKELY — 3-PRI input with 2 candidates, or 2-PRI input
|
|
with a unique solution.
|
|
* AMBIGUOUS — 1-PRI input (no CRT possible), 3+ candidates,
|
|
2-PRI input with 2 candidates, or no candidate
|
|
within tolerance.
|
|
- alias_set (m/s): all candidate v_true within tolerance, sorted
|
|
by goodness-of-fit (best first).
|
|
"""
|
|
n_sf = len(v_meas_per_sf)
|
|
if n_sf != len(v_unamb_per_sf):
|
|
raise ValueError("v_meas_per_sf and v_unamb_per_sf must have same length")
|
|
if n_sf == 0:
|
|
return (0.0, "AMBIGUOUS", [])
|
|
|
|
# 1-PRI input — no CRT possible (LONG-only-at-20-km regime).
|
|
if n_sf == 1:
|
|
return (v_meas_per_sf[0], "AMBIGUOUS", [v_meas_per_sf[0]])
|
|
|
|
if v_res_per_sf is None:
|
|
v_res_per_sf = [vu / 8.0 for vu in v_unamb_per_sf]
|
|
elif len(v_res_per_sf) != n_sf:
|
|
raise ValueError("v_res_per_sf, when provided, must match v_meas_per_sf length")
|
|
|
|
pri0_meas = v_meas_per_sf[0]
|
|
pri0_span = 2.0 * v_unamb_per_sf[0]
|
|
|
|
candidates: list[tuple[float, float]] = [] # (v_candidate, max_err)
|
|
for k in range(-max_alias_k, max_alias_k + 1):
|
|
v_cand = pri0_meas + k * pri0_span
|
|
max_err = 0.0
|
|
rejected = False
|
|
for i in range(1, n_sf):
|
|
vu_i = v_unamb_per_sf[i]
|
|
span_i = 2.0 * vu_i
|
|
v_pred_i = ((v_cand + vu_i) % span_i) - vu_i
|
|
err = abs(v_pred_i - v_meas_per_sf[i])
|
|
tol_i = tol_factor * v_res_per_sf[i]
|
|
if err > tol_i:
|
|
rejected = True
|
|
break
|
|
if err > max_err:
|
|
max_err = err
|
|
if not rejected:
|
|
candidates.append((v_cand, max_err))
|
|
|
|
if not candidates:
|
|
# No fold satisfies all PRIs — fall back to PRI-0, mark AMBIGUOUS.
|
|
return (pri0_meas, "AMBIGUOUS", [pri0_meas])
|
|
|
|
candidates.sort(key=lambda c: c[1])
|
|
v_best = candidates[0][0]
|
|
alias_set = [v for (v, _) in candidates]
|
|
n_cands = len(alias_set)
|
|
|
|
if n_cands >= 3:
|
|
confidence = "AMBIGUOUS"
|
|
elif n_sf == 3 and n_cands == 1:
|
|
confidence = "CONFIRMED"
|
|
elif (n_sf == 3 and n_cands == 2) or (n_sf == 2 and n_cands == 1):
|
|
confidence = "LIKELY"
|
|
else: # n_sf == 2 and n_cands == 2
|
|
confidence = "AMBIGUOUS"
|
|
|
|
return (v_best, confidence, alias_set)
|
|
|
|
|
|
def extract_targets_from_frame_crt(
|
|
frame,
|
|
waveform,
|
|
gps: GPSData | None = None,
|
|
max_alias_k: int = 6,
|
|
) -> list[RadarTarget]:
|
|
"""Extract RadarTargets from a 48-bin frame using 3-PRI CRT unfolding.
|
|
|
|
The 48 Doppler bins are organized as 3 sub-frames of 16:
|
|
|
|
bins 0..15: SHORT PRI (``waveform.pri_short_s``)
|
|
bins 16..31: MEDIUM PRI (``waveform.pri_medium_s``)
|
|
bins 32..47: LONG PRI (``waveform.pri_long_s``)
|
|
|
|
Within each sub-frame, the 16-pt FFT uses the standard signed-bin
|
|
convention: bin 0 = DC, bins 1..7 = positive v, bin 8 = Nyquist
|
|
(treated as +v_unamb), bins 9..15 = negative v.
|
|
|
|
Detections at the same range bin across different sub-frames are
|
|
grouped, and the strongest bin per (rbin, sub-frame) is taken as
|
|
that PRI's primary Doppler measurement. ``unfold_velocity_crt``
|
|
resolves aliases when ≥2 sub-frames see the target.
|
|
|
|
Falls back to the legacy single-PRI ``extract_targets_from_frame``
|
|
when the frame is not 48-bin (e.g. 32-bin legacy recordings).
|
|
"""
|
|
if frame.detections.ndim != 2 or frame.detections.shape[1] != 48:
|
|
return extract_targets_from_frame(
|
|
frame,
|
|
range_resolution=waveform.range_resolution_m,
|
|
velocity_resolution=waveform.velocity_resolution_long_mps,
|
|
gps=gps,
|
|
)
|
|
|
|
chirps_per_sf = waveform.chirps_per_subframe # 16
|
|
v_res_per_sf_all = [
|
|
waveform.velocity_resolution_short_mps,
|
|
waveform.velocity_resolution_medium_mps,
|
|
waveform.velocity_resolution_long_mps,
|
|
]
|
|
v_unamb_per_sf_all = [
|
|
waveform.max_velocity_short_mps,
|
|
waveform.max_velocity_medium_mps,
|
|
waveform.max_velocity_long_mps,
|
|
]
|
|
|
|
# Group detections: rbin -> {sf_id: (peak_bin_in_sf, peak_mag)}
|
|
clusters: dict[int, dict[int, tuple[int, float]]] = {}
|
|
det_indices = np.argwhere(frame.detections > 0)
|
|
for idx in det_indices:
|
|
rbin, dbin = int(idx[0]), int(idx[1])
|
|
sf_id = dbin // chirps_per_sf
|
|
bin_in_sf = dbin % chirps_per_sf
|
|
mag = float(frame.magnitude[rbin, dbin])
|
|
existing = clusters.setdefault(rbin, {}).get(sf_id)
|
|
if existing is None or mag > existing[1]:
|
|
clusters[rbin][sf_id] = (bin_in_sf, mag)
|
|
|
|
targets: list[RadarTarget] = []
|
|
range_resolution = waveform.range_resolution_m
|
|
|
|
for rbin in sorted(clusters.keys()):
|
|
sf_map = clusters[rbin]
|
|
active_sfs = sorted(sf_map.keys())
|
|
v_meas_list: list[float] = []
|
|
v_unamb_list: list[float] = []
|
|
v_res_list: list[float] = []
|
|
peak_mag = 0.0
|
|
for sf_id in active_sfs:
|
|
bin_in_sf, mag = sf_map[sf_id]
|
|
# Signed bin: 0..7 positive, 8 = Nyquist (treat as +8),
|
|
# 9..15 negative. Yields v in [-8·v_res, +8·v_res].
|
|
signed_bin = bin_in_sf if bin_in_sf <= 8 else bin_in_sf - chirps_per_sf
|
|
v_meas_list.append(float(signed_bin) * v_res_per_sf_all[sf_id])
|
|
v_unamb_list.append(v_unamb_per_sf_all[sf_id])
|
|
v_res_list.append(v_res_per_sf_all[sf_id])
|
|
if mag > peak_mag:
|
|
peak_mag = mag
|
|
|
|
v_est, confidence, alias_set = unfold_velocity_crt(
|
|
v_meas_list, v_unamb_list, v_res_list, max_alias_k=max_alias_k,
|
|
)
|
|
|
|
range_m = float(rbin) * range_resolution
|
|
snr = 10.0 * math.log10(max(peak_mag, 1.0)) if peak_mag > 0 else 0.0
|
|
|
|
lat, lon, azimuth, elevation = 0.0, 0.0, 0.0, 0.0
|
|
if gps is not None:
|
|
azimuth = gps.heading
|
|
lat, lon = polar_to_geographic(
|
|
gps.latitude, gps.longitude, range_m, azimuth,
|
|
)
|
|
|
|
targets.append(RadarTarget(
|
|
id=len(targets),
|
|
range=range_m,
|
|
velocity=v_est,
|
|
azimuth=azimuth,
|
|
elevation=elevation,
|
|
latitude=lat,
|
|
longitude=lon,
|
|
snr=snr,
|
|
timestamp=frame.timestamp,
|
|
velocity_confidence=confidence,
|
|
alias_set=alias_set if alias_set else None,
|
|
))
|
|
|
|
return targets
|