File size: 4,309 Bytes
d196012 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Robust outlier removal for per-satellite TLE record sequences.
Why: raw TLE archives contain corrupted records (bad element fields, wrong
epochs, mis-decoded mean anomaly / rev counter). These single-point outliers
poison linear interpolation onto the daily grid and the angle unwrapping. The
cm-tle-pred benchmark reports that outlier removal (DBSCAN on 1st/2nd-order
differences of the elements) gave their single biggest accuracy gain (~2 orders
of magnitude). We use a cheaper, dependency-free equivalent: flag any record
whose element deviates from a time-linear interpolation of its neighbors by more
than ``k_mad`` robust (MAD) scales, on the elements that should evolve smoothly.
Cleaning runs on the raw record list BEFORE daily resampling / unwrapping.
"""
from __future__ import annotations
import sys
from pathlib import Path
from typing import List, Tuple
import numpy as np
# reuse the parser's record type + helpers from the original code/utils
_CODE_UTILS = Path(__file__).resolve().parents[2] / "code" / "utils"
if str(_CODE_UTILS) not in sys.path:
sys.path.insert(0, str(_CODE_UTILS))
from tle_future_dataset import TLERecord, signed_log1p # noqa: E402
SECONDS_PER_DAY = 86400.0
def cumulative_mean_anomaly(recs: List[TLERecord]) -> np.ndarray:
"""Unwrap mean anomaly into a monotone cumulative phase (deg).
Revolution count between epochs is disambiguated with the mean motion
(rev/day), which is far more reliable than the TLE rev-counter field.
"""
n = len(recs)
phi = np.empty(n, dtype=np.float64)
phi[0] = recs[0].mean_anomaly_deg
for i in range(1, n):
dt_days = (recs[i].epoch_unix - recs[i - 1].epoch_unix) / SECONDS_PER_DAY
n_avg = 0.5 * (recs[i].mean_motion_rev_per_day + recs[i - 1].mean_motion_rev_per_day)
predicted = phi[i - 1] + n_avg * dt_days * 360.0
m_i = recs[i].mean_anomaly_deg
k = round((predicted - m_i) / 360.0)
phi[i] = 360.0 * k + m_i
return phi
def _neighbor_interp_resid(t: np.ndarray, x: np.ndarray) -> np.ndarray:
"""Residual of each interior point vs a time-linear interp of its neighbors."""
resid = np.zeros_like(x, dtype=np.float64)
if len(x) < 3:
return resid
t0, t1, t2 = t[:-2], t[1:-1], t[2:]
denom = np.where((t2 - t0) == 0, 1.0, (t2 - t0))
w = (t1 - t0) / denom
x_hat = x[:-2] + (x[2:] - x[:-2]) * w
resid[1:-1] = x[1:-1] - x_hat
return resid
def _mad(v: np.ndarray) -> float:
v = v[np.isfinite(v)]
if v.size == 0:
return 0.0
med = np.median(v)
return float(1.4826 * np.median(np.abs(v - med)))
def clean_records(
recs: List[TLERecord], k_mad: float = 6.0, max_passes: int = 2,
) -> Tuple[List[TLERecord], int]:
"""Remove single-point outlier records. Returns (cleaned, n_removed).
Flags the union of outliers across the smoothly-evolving quantities:
mean motion, inclination, eccentricity, bstar(log), and the cumulative /
unwrapped angles (mean anomaly phase, RAAN, argp).
"""
recs = sorted(recs, key=lambda r: r.epoch_unix)
removed = 0
for _ in range(max_passes):
n = len(recs)
if n < 5:
break
t = np.array([r.epoch_unix for r in recs], dtype=np.float64)
series = {
"mm": np.array([r.mean_motion_rev_per_day for r in recs]),
"inc": np.array([r.inclination_deg for r in recs]),
"ecc": np.array([r.eccentricity for r in recs]),
"bstar": np.array([signed_log1p(r.bstar) for r in recs]),
"phiM": cumulative_mean_anomaly(recs),
"raan": np.degrees(np.unwrap(np.radians([r.raan_deg for r in recs]))),
"argp": np.degrees(np.unwrap(np.radians([r.argp_deg for r in recs]))),
}
flag = np.zeros(n, dtype=bool)
for s in series.values():
resid = _neighbor_interp_resid(t, s)
scale = _mad(resid[1:-1])
if scale > 0:
flag |= np.abs(resid) > (k_mad * scale)
flag[0] = flag[-1] = False # keep endpoints (no two-sided neighbors)
if not flag.any():
break
recs = [r for r, bad in zip(recs, flag) if not bad]
removed += int(flag.sum())
return recs, removed
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