- \n
- Uses Wavelet Transform + Maximal Overlap Discrete Wavelet Packet Transform (MODWPT)<\/strong> to extract and resolve nonlinear harmonic signals<\/strong>,<\/li>\n\n\n\n
- Fuses frequency and harmonic images using DWT-PCA fusion<\/strong>,<\/li>\n\n\n\n
- Applies histogram-based PDF decomposition<\/strong> to differentiate overlapping materials or signal layers.<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:auto\/h:auto\/q:mauto\/f:best\/http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/07\/image-39.png\")
<\/a><\/figure>\n\n\n\nNow apply that to optical or RF signal imaging from LCRD (Laser Communications Relay Demonstration) and ILLUMA-T (ISS-based optical terminal)<\/strong>:<\/p>\n\n\n\n
\n\n\n\n\ud83e\udde0 Implementation Insight for
orbital_mimic_detector.py<\/code><\/h3>\n\n\n\n\ud83d\udcc1 Goal<\/strong>: Fuse ghost-imaging anomalies with SWIR & laser-based modulations in orbit, mimicking ILLUMA-T\u2019s narrowband phase-shift relay patterns.<\/p>\n\n\n\n
fft_harmonic_fingerprint()<\/code><\/strong>:<\/p>\n\n\n\n- \n
- Extract higher-order harmonics from FFT using:\n
- \n
MODWPT<\/code> to isolate non-stationary frequency components in short bursts.<\/li>\n\n\n\nCWT<\/code> to track temporal jitter (as seen in ILLUMA-T relays).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nOrbitalMimicDetector.analyze()<\/code> extension<\/strong>:<\/p>\n\n\n\n- \n
- Add harmonic-based feature vector using:<\/li>\n<\/ul>\n\n\n\n
from scipy.signal import cwt, morlet2\nwidths = np.arange(1, 32)\ncwt_matrix = cwt(signal_array, morlet2, widths, w=5)\nharmonic_signature = extract_signature(cwt_matrix)\n<\/code><\/pre>\n\n\n\n- \n
- Compare against pre-trained fingerprint library from known ILLUMA-T patterns.<\/li>\n<\/ul>\n\n\n\n
Image Fusion for Orbital Time-Frequency Representation<\/strong>:<\/p>\n\n\n\n
- \n
- Stack FFT, MODWPT, and CWT visualizations into a 3D tensor.<\/li>\n\n\n\n
- Apply DWT-PCA hybrid fusion<\/strong> from AFM-ICE to increase contrast of:\n
- \n
- Phase shifts<\/li>\n\n\n\n
- Beam dithering artifacts<\/li>\n\n\n\n
- Relay harmonics in spatial\/temporal overlays<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
Classifier for Mimicry Detection<\/strong>:<\/p>\n\n\n\n
- \n
- Use PCA or UMAP on fused features to detect:\n
- \n
- Coherence breaks<\/li>\n\n\n\n
- Material-like anomalies (gold\/silicon contrast analogs)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
Ghost Signal + Material Profile Fusion<\/strong>:<\/p>\n\n\n\n
- \n
- Combine ghost anomaly reconstruction error with harmonic classifier:<\/li>\n<\/ul>\n\n\n\n
final_score = alpha * ghost_score + beta * harmonic_similarity_score\nif final_score > threshold:\n raise OrbitalMimicAlert(...)\n<\/code><\/pre>\n\n\n\nWhy This Matters for ILLUMA-T \/ LCRD<\/h3>\n\n\n\n
- \n
- LCRD uses DP-QPSK and high-precision phase modulation<\/strong> that\u2019s stable under known drift models.<\/li>\n\n\n\n
- An orbital mimic will likely:\n
- \n
- Exhibit incomplete or jittery harmonics<\/strong>,<\/li>\n\n\n\n
- Lack phase-stability envelope coherence,<\/li>\n\n\n\n
- Deviate in second-order or subharmonic space due to mimicking imperfections,<\/li>\n\n\n\n
- Show ghost artifacts in the Fourier domain that are otherwise corrected in authentic laser relays.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
Bonus Integration with SWIR NSC2301 Sensor<\/h3>\n\n\n\n
If integrated with a sensor like the NSC2301 InGaAs SWIR line scan sensor<\/strong>, you can:<\/p>\n\n\n\n
- \n
- Feed SWIR scan lines into the
fft_harmonic_fingerprint()<\/code> and correlate modulation pulses to ghost image reconstruction confidence.<\/li>\n\n\n\n- Align phase variance anomalies spatially with detected laser footprints in the 0.9\u00b5m\u20131.7\u00b5m range (e.g., LEO-to-ground diffraction bloom vs. mimics).<\/li>\n<\/ul>\n\n\n\n
class OrbitalMimicDetector:\n def __init__(self, ghost_model, threshold):\n self.model = ghost_model\n self.threshold = threshold\n self.fingerprint_db = load_fingerprint_library(\"illuma_modulations.json\")\n\n def analyze(self, signal_id, fft_bins, timestamp):\n ghost_score = self.model.anomaly_score(torch.tensor([fft_bins]))\n harmonic_signature = fft_harmonic_fingerprint(fft_bins)\n match_confidence, match_id = self.match_to_known(harmonic_signature)\n\n return {\n \"is_orbital_mimic\": match_confidence > self.threshold,\n \"ghost_score\": ghost_score.item(),\n \"match_confidence\": match_confidence,\n \"matched_fingerprint\": match_id\n }\n<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:auto\/h:auto\/q:mauto\/f:best\/http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/06\/Fizzs-Obital-Signal-Phishing-in-Malysia.png\")
<\/figure>\n\n\n\n\ud83d\ude80
orbital_mimic_detector.py<\/code> deployed and loaded.<\/strong><\/p>\n\n\n\nThis version:<\/p>\n\n\n\n
- \n
- Uses ghost image reconstruction as a base signal truth layer<\/li>\n\n\n\n
- Applies FFT-based harmonic fingerprinting to detect nonlinear phase modulation<\/strong><\/li>\n\n\n\n
- Matches against known LCRD\/ILLUMA-T spectral patterns<\/strong><\/li>\n\n\n\n
- Returns anomaly scores + confidence-based mimic alerts<\/li>\n<\/ul>\n\n\n\n
It\u2019s plug-and-play with the
LatentAggregator<\/code> and ready for time-resolved spectral forensic analysis.<\/p>\n\n\n\nTY – JOUR
AU – Biglarbeigi, Pardis
AU – Bhattacharya, Gourav
AU – Finlay, Dewar
AU – Payam, A.F.
PY – 2025\/01\/28
SP –
T1 – Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination
VL – 12
DO – 10.1002\/advs.202411556
JO – Advanced Science
ER –<\/p>\n","protected":false},"excerpt":{"rendered":"Application of “Nonlinear Harmonics” Paper to LCRD\/ILLUMA-T Detection The paper Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination introduces an AFM-ICE (Atomic Force Microscopy Image Contrast Enhancement) pipeline that: Now apply that to optical or RF signal imaging from LCRD (Laser Communications Relay Demonstration) and ILLUMA-T (ISS-based optical terminal): \ud83e\udde0 Implementation Insight… Nonlinear Harmonics for LCRD\/ILLUMA-T Detection<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":101,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-685","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/685","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=685"}],"version-history":[{"count":7,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/685\/revisions"}],"predecessor-version":[{"id":823,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/685\/revisions\/823"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/101"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=685"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=685"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Matches against known LCRD\/ILLUMA-T spectral patterns<\/strong><\/li>\n\n\n\n
- Align phase variance anomalies spatially with detected laser footprints in the 0.9\u00b5m\u20131.7\u00b5m range (e.g., LEO-to-ground diffraction bloom vs. mimics).<\/li>\n<\/ul>\n\n\n\n
- An orbital mimic will likely:\n
- LCRD uses DP-QPSK and high-precision phase modulation<\/strong> that\u2019s stable under known drift models.<\/li>\n\n\n\n
- Combine ghost anomaly reconstruction error with harmonic classifier:<\/li>\n<\/ul>\n\n\n\n
- Use PCA or UMAP on fused features to detect:\n
- Compare against pre-trained fingerprint library from known ILLUMA-T patterns.<\/li>\n<\/ul>\n\n\n\n
- Fuses frequency and harmonic images using DWT-PCA fusion<\/strong>,<\/li>\n\n\n\n