◈ SPECTRUM-GHOST

███████╗██████╗ ███████╗ ██████╗████████╗██████╗ ██╗   ██╗███╗   ███╗
██╔════╝██╔══██╗██╔════╝██╔════╝╚══██╔══╝██╔══██╗██║   ██║████╗ ████║
███████╗██████╔╝█████╗  ██║        ██║   ██████╔╝██║   ██║██╔████╔██║
╚════██║██╔═══╝ ██╔══╝  ██║        ██║   ██╔══██╗██║   ██║██║╚██╔╝██║
███████║██║     ███████╗╚██████╗   ██║   ██║  ██║╚██████╔╝██║ ╚═╝ ██║
╚══════╝╚═╝     ╚══════╝ ╚═════╝   ╚═╝   ╚═╝  ╚═╝ ╚═════╝ ╚═╝     ╚═╝
           ██████╗ ██╗  ██╗ ██████╗ ███████╗████████╗                 
          ██╔════╝ ██║  ██║██╔═══██╗██╔════╝╚══██╔══╝                 
          ██║  ███╗███████║██║   ██║███████╗   ██║                    
          ██║   ██║██╔══██║██║   ██║╚════██║   ██║                    
          ╚██████╔╝██║  ██║╚██████╔╝███████║   ██║                    
           ╚═════╝ ╚═╝  ╚═╝ ╚═════╝ ╚══════╝   ╚═╝                    

LISTENING TO FREQUENCIES THAT SHOULDN'T EXIST

HackRF/RTL-SDR integration • Phantom carrier detection • Signal archaeology • RF fingerprinting

---

◈ CONCEPT

The electromagnetic spectrum is crowded with signals—radio, WiFi, cellular, satellite. But sometimes there are signals that have no registered source. Carriers with no modulation. Frequencies that respond to nothing. spectrum-ghost hunts these phantom signals.

"The ghost in the machine speaks on frequencies we forgot to listen to."

---

◈ SUPPORTED HARDWARE

Device	Frequency Range	Bandwidth	TX	Notes
HackRF One	1MHz - 6GHz	20MHz	✓	Full duplex, portable
RTL-SDR v3	24MHz - 1.7GHz	2.4MHz	✗	Low cost, great for beginners
RTL-SDR v4	500kHz - 1.7GHz	2.4MHz	✗	Direct sampling mode
Airspy HF+	9kHz - 31MHz	768kHz	✗	HF specialist, high dynamic range
Airspy Mini	24MHz - 1.8GHz	6MHz	✗	Compact, high performance
LimeSDR	100kHz - 3.8GHz	61.44MHz	✓	Research grade, MIMO
USRP B210	70MHz - 6GHz	56MHz	✓	Professional, high precision
PlutoSDR	325MHz - 3.8GHz	20MHz	✓	Affordable TX/RX

---

◈ CAPABILITIES

▸ PHANTOM CARRIER DETECTION

Hunt for unregistered signals across the spectrum:

from spectrum_ghost import SpectrumScanner, PhantomDetector

scanner = SpectrumScanner(device="hackrf")
detector = PhantomDetector()

# Full spectrum sweep
async for sweep in scanner.sweep(1e6, 6e9, step=1e6):
    phantoms = detector.analyze(sweep)
    
    for phantom in phantoms:
        print(f"▸ PHANTOM at {phantom.frequency/1e6:.3f} MHz")
        print(f"  Power: {phantom.power_dbm} dBm")
        print(f"  Bandwidth: {phantom.bandwidth/1e3:.1f} kHz")
        print(f"  Modulation: {phantom.modulation or 'NONE'}")
        print(f"  Registered source: {phantom.registered or 'NONE'}")
        print(f"  Strangeness: {phantom.strangeness}%")

▸ SIGNAL ARCHAEOLOGY

Recover and analyze signals that have been absorbed, reflected, or partially occluded:

from spectrum_ghost import SignalArchaeologist

archaeologist = SignalArchaeologist()

# Recover signal from environmental reflections
recovered = await archaeologist.recover(
    frequency=433.92e6,
    duration_seconds=60,
    method="multipath_reconstruction"
)

print(f"Recovered signal confidence: {recovered.confidence}%")
print(f"Original source bearing: {recovered.bearing}°")
print(f"Time offset: {recovered.time_offset_us}µs")

# Decode if modulation detected
if recovered.decodable:
    data = archaeologist.decode(recovered)
    print(f"Recovered data: {data}")

▸ RF FINGERPRINTING

Every transmitter has a unique RF fingerprint—slight imperfections in the hardware that create identifiable patterns:

from spectrum_ghost import RFFingerprinter

fingerprinter = RFFingerprinter()

# Capture and fingerprint a signal
signature = await fingerprinter.capture(frequency=915e6)

# Check against database
match = fingerprinter.identify(signature)

if match:
    print(f"Identified device: {match.device_type}")
    print(f"Manufacturer: {match.manufacturer}")
    print(f"Confidence: {match.confidence}%")
    print(f"Previous sightings: {match.sighting_count}")
else:
    print("Unknown device - adding to database")
    fingerprinter.add_unknown(signature)

▸ TEMPORAL SIGNAL ANALYSIS

Signals that appear and disappear on schedules, or that correlate with other events:

from spectrum_ghost import TemporalAnalyzer

analyzer = TemporalAnalyzer()

# Monitor for 24 hours
await analyzer.record(
    frequency_range=(400e6, 500e6),
    duration_hours=24
)

# Find patterns
patterns = analyzer.find_patterns()

for pattern in patterns:
    print(f"▸ Pattern: {pattern.description}")
    print(f"  Frequency: {pattern.frequency/1e6:.3f} MHz")
    print(f"  Schedule: {pattern.schedule}")
    print(f"  Correlation: {pattern.correlation}")

---

◈ SAMPLE OUTPUT

◈ SPECTRUM-GHOST v2.0 › RF HUNT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

DEVICE: HackRF One
RANGE: 1MHz → 6GHz
MODE: Phantom Detection

SWEEP PROGRESS ████████████░░░░░░░░ 60%

PHANTOMS DETECTED

▸ PHANTOM #1 [433.847 MHz]
  Power: -67 dBm • BW: 12 kHz
  Modulation: NONE (carrier only)
  Registered: ISM band, no active license
  Duration: 47 minutes continuous
  Behavior: Responds to nothing
  Strangeness ████████░░ 83%

▸ PHANTOM #2 [1.42 GHz]
  Power: -112 dBm (below noise floor?)
  Modulation: Unknown pulsed pattern
  Registered: Hydrogen line (natural)
  Anomaly: Artificial modulation on natural frequency
  Strangeness █████████░ 94%

▸ PHANTOM #3 [2.847 GHz]
  Power: -78 dBm • BW: 847 kHz
  Modulation: OFDM-like structure
  Registered: NONE
  Movement: Signal source appears to be mobile
  Bearing: 247° ± 15°
  Strangeness ███████░░░ 71%

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SCAN ACTIVE • PHANTOMS: 3 • UNEXPLAINED: 2

---

◈ INTEGRATION WITH BAUDRILLARD SUITE

With spectral

Combine RF analysis with EMF and acoustic:

from spectrum_ghost import SpectrumScanner
from spectral import MultiModalScanner

scanner = MultiModalScanner()
scanner.add_rf(SpectrumScanner("hackrf"))
scanner.add_emf()
scanner.add_acoustic()

# Correlate all anomalies
async for event in scanner.unified_scan():
    if event.multi_modal_correlation > 0.7:
        print(f"Multi-modal anomaly!")
        print(f"RF: {event.rf}")
        print(f"EMF: {event.emf}")
        print(f"Acoustic: {event.acoustic}")

With thermal-eye

Correlate RF sources with thermal signatures:

from spectrum_ghost import DirectionFinder
from thermal_eye import ThermalScanner

df = DirectionFinder()
thermal = ThermalScanner()

# Find RF source direction
bearing = await df.locate(frequency=915e6)

# Look for thermal source in that direction
thermal_sources = await thermal.scan_direction(bearing)

for source in thermal_sources:
    print(f"Possible transmitter: {source.description}")
    print(f"Temperature: {source.temp}°C")

---

◈ ADVANCED FEATURES

▸ DISTRIBUTED SENSING

Multiple SDRs working together for enhanced detection:

from spectrum_ghost import DistributedNetwork

network = DistributedNetwork()
network.add_node("hackrf@192.168.1.10")
network.add_node("rtlsdr@192.168.1.11")
network.add_node("rtlsdr@192.168.1.12")

# Triangulation mode
source = await network.triangulate(frequency=433.92e6)
print(f"Source location: {source.lat}, {source.lon}")
print(f"Accuracy: ±{source.accuracy_m}m")

▸ WATERFALL ARCHAEOLOGY

Recover historical signals from long recordings:

from spectrum_ghost import WaterfallArchaeologist

wa = WaterfallArchaeologist()

# Load historical recording
wa.load("spectrum_24h.raw")

# Find signals that no longer exist
historical = wa.find_departed()

for signal in historical:
    print(f"Signal last seen: {signal.last_seen}")
    print(f"Frequency: {signal.frequency/1e6:.3f} MHz")
    print(f"Duration: {signal.total_duration}")

---

◈ DESKTOP APPLICATION

Native Tauri app with:

• Real-time spectrum waterfall display
• Phantom signal highlighting
• Signal recording and playback
• Direction finding visualization
• Multi-SDR support

---

◈ INSTALLATION

# Core library
pip install baudrillard-spectrum-ghost

# Desktop app
cd apps/spectrum-ghost-desktop
npm install && npm run tauri build

# SDR drivers
sudo apt install libhackrf-dev rtl-sdr librtlsdr-dev

---

"Every frequency has a story. Some stories were never meant to be told."

BAUDRILLARD SUITE