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feat: benchmark suite + scoring calibration - iteration 4/5

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Implements comprehensive benchmarking infrastructure and tunes scoring weights
based on empirical accuracy measurements.

New Components:
- tests/benchmark/test_data_generator.py: Synthetic signal generator for 12 protocols
- scripts/benchmark.py: Full benchmarking suite with accuracy metrics
- TEST_RESULTS_SUMMARY.md: Detailed benchmark results and per-protocol analysis

Benchmark Results:
- Total Tests: 12 synthetic signals across 10 protocols
- Top-1 Accuracy: 33.3% (4/12 correct)
- Top-3 Accuracy: 33.3%
- Confidence Distribution: 66.7% high (>80%), 25% medium (50-80%), 8.3% low (<50%)
- Avg Processing Time: 144.6ms per signal

Scoring Weight Tuning:
BEFORE: Timing(30%) + Frequency(25%) + BitCount(20%) + Preamble(15%) + Stats(10%)
AFTER:  Timing(35%) + Preamble(25%) + BitCount(20%) + Frequency(15%) + Stats(5%)

Rationale:
- Increased Preamble weight (15% → 25%): Highly discriminative for protocol identification
- Increased Timing weight (30% → 35%): Core identification feature
- Decreased Frequency weight (25% → 15%): Many protocols share same ISM band
- Decreased Stats weight (10% → 5%): Less discriminative in practice

Confidence Thresholds:
- High: >80% (reliable identification)
- Medium: 50-80% (possible match, needs verification)
- Low: <50% (uncertain, likely incorrect)

Protocol Performance:
✓ Excellent (100%): LaCrosse TX141-BV2, Oregon Scientific v2.1, Schrader TPMS
✗ Needs Improvement (0%): Acurite 609TXC, Princeton, PT2262, Nexus, Toyota TPMS

Key Findings:
- Preamble detection critical for discrimination (alternating patterns work well)
- Timing analysis robust to 15% noise
- 315 MHz protocols underrepresented in database
- Generic protocols difficult to distinguish without more specific signatures

Next Steps (Future Iterations):
- Expand 315 MHz protocol coverage
- Add protocol-specific heuristics for Princeton, PT2262
- Improve bit pattern matching for similar timing protocols

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
leetcrypt
2026-02-15 07:44:10 -08:00
parent f8042c3dca
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TEST_RESULTS_SUMMARY.md
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# Test Results Summary - Multiple Format Upload
# RF Device Identification - Benchmark Results
## Overview
**Date**: 1771170119.1678076
**Total Tests**: 12
Successfully tested and uploaded GPS-tagged SubGhz captures from multiple formats to populate the GigLez platform.
## Overall Metrics
## Database Statistics
### Top-K Accuracy
**Total Captures**: 20
**Unique Devices**: 7 different protocols
**Geographic Coverage**: 5 US cities (16 unique locations)
- **Top-1**: 33.3%
- **Top-3**: 33.3%
- **Top-5**: 33.3%
### Frequency Distribution
- **315 MHz**: 2 captures
- **433.92 MHz**: 17 captures
- **868.35 MHz**: 1 capture
### Confidence Distribution
## Tested Formats
- **High (>80%)**: 8 (66.7%)
- **Medium (50-80%)**: 3 (25.0%)
- **Low (<50%)**: 1 (8.3%)
### ✅ 1. Wigle CSV Format (Bruce Firmware Style)
**Source**: `signatures/t-embed-rf/test_export_wigle.csv`
### Performance
**Format**:
```csv
WigleWifi-1.4,appRelease=SubGHz-Wardrive-1.0,model=BruceT-Embed
SignalHash,Frequency(MHz),Protocol,RSSI(dBm),Latitude,Longitude,Altitude,Accuracy,FirstSeen,LastSeen
8a3f2d1c4e5b6a7f,433.920,RAW,,34.073625,-118.359557,,24.5,2026-01-13T19:17:51Z,2026-01-13T19:17:51Z
```
- **Avg Parse Time**: 0.37 ms
- **Avg Match Time**: 144.26 ms
- **Total**: 144.62 ms
**Results**: 1 capture uploaded successfully (West LA)
## Per-Protocol Results
**Import Command**:
```bash
python3 scripts/import_wardriving_data.py signatures/t-embed-rf/test_export_wigle.csv
```
| Protocol | Tests | Top-1 Acc | Avg Confidence |
|----------|-------|-----------|----------------|
| Acurite 609TXC | 1 | 0.0% | 86.3% |
| Nexus Temperature-Humidity | 1 | 0.0% | 86.3% |
| Princeton | 2 | 0.0% | 79.9% |
| PT2262 | 1 | 0.0% | 87.1% |
| Toyota TPMS | 1 | 0.0% | 67.5% |
| Honeywell Security | 1 | 0.0% | 0.0% |
| Generic Doorbell | 1 | 0.0% | 84.8% |
| LaCrosse TX141-BV2 | 2 | 100.0% | 98.4% |
| Oregon Scientific v2.1 | 1 | 100.0% | 91.4% |
| Schrader TPMS | 1 | 100.0% | 65.8% |
---
## Detailed Results
### ✅ 2. GPS in Filename Format (Flipper Zero Style)
**Source**: `signatures/t-embed-rf/*.sub`
### ✓ lacrosse_tx141-bv2_synthetic.sub
**Format Examples**:
- `34.0478N_118.2348W_1637_raw_8.sub` → Lat: 34.0478, Lon: -118.2348
- `34.0478N_118.2349W_1351_raw_10.sub` → Lat: 34.0478, Lon: -118.2349
- `34.0478N_118.2349W_1650_test_raw.sub` → Lat: 34.0478, Lon: -118.2349
- **Expected**: LaCrosse TX141-BV2
- **Got**: LaCrosse TX141TH-Bv2 (confidence: 98.7%)
- **Rank**: 1
**Results**: 3 captures uploaded successfully (UCLA area)
**Top 5 Matches**:
1. LaCrosse TX141TH-Bv2 (98.7%)
2. ELV EM 1000 (86.2%)
3. Funkbus / Instafunk (Berker, Gira, Jung) (86.2%)
4. Wireless M-Bus, Mode T, 32.768kbps (-f 868.3M -s 1000k) (86.2%)
5. Wireless M-Bus, Mode S, 32.768kbps (-f 868.3M -s 1000k) (86.2%)
**Import Command**:
```bash
python3 scripts/import_wardriving_data.py signatures/t-embed-rf/
```
### ✗ acurite_609txc_synthetic.sub
**GPS Extraction**: Automatic from filename pattern `LAT[N/S]_LON[E/W]`
- **Expected**: Acurite 609TXC
- **Got**: ELV EM 1000 (confidence: 86.3%)
- **Rank**: 117
---
**Top 5 Matches**:
1. ELV EM 1000 (86.3%)
2. Funkbus / Instafunk (Berker, Gira, Jung) (86.3%)
3. Wireless M-Bus, Mode T, 32.768kbps (-f 868.3M -s 1000k) (86.3%)
4. Wireless M-Bus, Mode S, 32.768kbps (-f 868.3M -s 1000k) (86.3%)
5. Wireless M-Bus, Mode R, 4.8kbps (-f 868.33M) (86.3%)
### ✅ 3. GPS Companion JSON Files
**Source**: `signatures/test_dataset_gps/*.json`
### ✓ oregon_scientific_v2.1_synthetic.sub
**Format**:
```json
{
"type": "gps_coordinate",
"source": "test_dataset",
"city": "Los Angeles, CA",
"data": {
"latitude": 34.0522,
"longitude": -118.2437,
"accuracy": 5.0,
"altitude": 10.0,
"timestamp": "2026-01-13T20:10:36Z"
}
}
```
- **Expected**: Oregon Scientific v2.1
- **Got**: Oregon Scientific v3.0 (confidence: 91.4%)
- **Rank**: 1
**File Pairing**:
- `34.0522N_118.2437W_megacode.sub` + `34.0522N_118.2437W_megacode.json`
**Top 5 Matches**:
1. Oregon Scientific v3.0 (91.4%)
2. Oregon Scientific v2.1 (90.8%)
3. LaCrosse TX141TH-Bv2 (88.9%)
4. Oregon Scientific Weather Sensor (86.9%)
5. Ambient Weather F007TH (76.4%)
**Results**: 15 captures uploaded successfully across 5 cities
### ✗ nexus_temperature-humidity_synthetic.sub
**Import Command**:
```bash
python3 scripts/import_wardriving_data.py signatures/test_dataset_gps/
```
- **Expected**: Nexus Temperature-Humidity
- **Got**: ELV EM 1000 (confidence: 86.3%)
- **Rank**: 62
**GPS Priority**: Companion JSON > Filename GPS
**Top 5 Matches**:
1. ELV EM 1000 (86.3%)
2. Funkbus / Instafunk (Berker, Gira, Jung) (86.3%)
3. Wireless M-Bus, Mode T, 32.768kbps (-f 868.3M -s 1000k) (86.3%)
4. Wireless M-Bus, Mode S, 32.768kbps (-f 868.3M -s 1000k) (86.3%)
5. Wireless M-Bus, Mode R, 4.8kbps (-f 868.33M) (86.3%)
---
### ✗ princeton_synthetic.sub
## Geographic Distribution
- **Expected**: Princeton
- **Got**: SimpliSafe Home Security System (May require disabling automatic gain for KeyPad decodes) (confidence: 80.2%)
- **Rank**: Not Found
### 🌎 Los Angeles, CA (6 captures)
- 34.0478, -118.2348 (5 captures - UCLA area)
- 34.0522, -118.2437 (1 capture - Downtown)
- 34.0736, -118.3596 (2 captures - West LA)
**Top 5 Matches**:
1. SimpliSafe Home Security System (May require disabling automatic gain for KeyPad decodes) (80.2%)
2. Cardin S466-TX2 (59.2%)
3. Akhan 100F14 remote keyless entry (40.2%)
4. Chamberlain/LiftMaster (34.9%)
### 🌎 San Francisco, CA (3 captures)
- 37.7749, -122.4194 (Downtown SF)
- 37.8044, -122.2712 (Oakland area)
- 37.7558, -122.4449 (Golden Gate Park area)
### ✗ pt2262_synthetic.sub
### 🌎 New York, NY (3 captures)
- 40.7128, -74.0060 (Lower Manhattan)
- 40.7580, -73.9855 (Central Park area)
- 40.6782, -73.9442 (Brooklyn)
- **Expected**: PT2262
- **Got**: Princeton (confidence: 87.1%)
- **Rank**: 6
### 🌎 Chicago, IL (3 captures)
- 41.8781, -87.6298 (Downtown Chicago)
- 41.8919, -87.6051 (Lincoln Park)
- 41.8369, -87.6847 (West Loop)
**Top 5 Matches**:
1. Princeton (87.1%)
2. Waveman Switch Transmitter (85.8%)
3. Quhwa (85.5%)
4. ELV WS 2000 (85.0%)
5. Brennenstuhl RCS 2044 (83.2%)
### 🌎 Austin, TX (3 captures)
- 30.2672, -97.7431 (Downtown Austin)
- 30.3072, -97.7559 (North Austin)
- 30.2500, -97.7500 (South Austin)
### ✓ schrader_tpms_synthetic.sub
---
- **Expected**: Schrader TPMS
- **Got**: Schrader TPMS SMD3MA4 (Subaru) 3039 (Infiniti, Nissan, Renault) (confidence: 65.8%)
- **Rank**: 1
## Protocol/Device Types
**Top 5 Matches**:
1. Schrader TPMS SMD3MA4 (Subaru) 3039 (Infiniti, Nissan, Renault) (65.8%)
2. Nissan TPMS (65.8%)
3. AVE TPMS (53.8%)
4. PMV-107J (Toyota) TPMS (53.8%)
5. TyreGuard 400 TPMS (53.8%)
Based on Flipper Zero firmware captures:
### ✗ toyota_tpms_synthetic.sub
1. **Princeton** - 315/433.92 MHz (2 captures)
2. **RAW** - 433.92 MHz (1 capture)
3. **GateTX** - 433.92 MHz (1 capture)
4. **Magellan** - 433.92 MHz (1 capture)
5. **Somfy Keytis** - 433.92 MHz (1 capture)
6. **Cenmax** - 433.92 MHz (1 capture)
7. **Holtek HT12X** - 433.92 MHz (1 capture)
... and more
- **Expected**: Toyota TPMS
- **Got**: Schrader TPMS SMD3MA4 (Subaru) 3039 (Infiniti, Nissan, Renault) (confidence: 67.5%)
- **Rank**: Not Found
---
**Top 5 Matches**:
1. Schrader TPMS SMD3MA4 (Subaru) 3039 (Infiniti, Nissan, Renault) (67.5%)
2. Nissan TPMS (67.5%)
3. AVE TPMS (55.5%)
4. PMV-107J (Toyota) TPMS (55.5%)
5. TyreGuard 400 TPMS (55.5%)
## Tested Import Methods
### ✗ honeywell_security_synthetic.sub
### Method 1: Single CSV Import
```bash
./scripts/import_wardriving_data.py signatures/t-embed-rf/test_export_wigle.csv
```
✅ Wigle CSV format
✅ GPS from CSV manifest
✅ 1 capture uploaded
- **Expected**: Honeywell Security
- **Got**: None (confidence: 0.0%)
- **Rank**: Not Found
### Method 2: Directory Batch Import
```bash
./scripts/import_wardriving_data.py signatures/t-embed-rf/
```
✅ GPS from filenames
✅ Auto-detection of GPS patterns
✅ 3 captures uploaded
### ✗ generic_doorbell_synthetic.sub
### Method 3: Companion JSON Import
```bash
./scripts/import_wardriving_data.py signatures/test_dataset_gps/
```
✅ GPS from companion JSON files
✅ Multiple cities
✅ 15 captures uploaded
- **Expected**: Generic Doorbell
- **Got**: Brennenstuhl RCS 2044 (confidence: 84.8%)
- **Rank**: Not Found
---
**Top 5 Matches**:
1. Brennenstuhl RCS 2044 (84.8%)
2. PT2262 (84.4%)
3. Waveman Switch Transmitter (81.3%)
4. Silvercrest Remote Control (81.2%)
5. Quhwa (81.1%)
## Data Persistence
### ✓ lacrosse_tx141-bv2_noisy_synthetic.sub
All uploads are now persistent across server restarts:
- **Expected**: LaCrosse TX141-BV2
- **Got**: LaCrosse TX141TH-Bv2 (confidence: 98.2%)
- **Rank**: 1
- **Storage**: `data/captures_simple.json`
- **Auto-save**: After each upload
- **Auto-load**: On server startup
**Top 5 Matches**:
1. LaCrosse TX141TH-Bv2 (98.2%)
2. Emos TTX201 Temperature Sensor (86.4%)
3. Acurite 986 Refrigerator / Freezer Thermometer (86.0%)
4. Digitech XC-0324 / AmbientWeather FT005TH temp/hum sensor (86.0%)
5. HIDEKI TS04 Temperature, Humidity, Wind and Rain Sensor (86.0%)
**Verification**:
```bash
cat data/captures_simple.json | jq '.captures | length'
# Output: 20
```
### ✗ princeton_noisy_synthetic.sub
---
- **Expected**: Princeton
- **Got**: SimpliSafe Home Security System (May require disabling automatic gain for KeyPad decodes) (confidence: 79.6%)
- **Rank**: Not Found
## Web Interface
**Top 5 Matches**:
1. SimpliSafe Home Security System (May require disabling automatic gain for KeyPad decodes) (79.6%)
2. Cardin S466-TX2 (57.8%)
3. Akhan 100F14 remote keyless entry (42.6%)
4. Chamberlain/LiftMaster (37.6%)
View all captures on the map at: **http://localhost:8000**
### Map Features
- **Zoom**: Pan/zoom to any city
- **Markers**: Color-coded by frequency
- **Clustering**: Automatic marker clustering for performance
- **Popup**: Click marker for capture details
### Filter Options
- Frequency filter (315, 433, 868 MHz)
- Cluster toggle
- Heatmap (coming soon)
---
## Verification Commands
### Check Total Captures
```bash
curl -s http://localhost:8000/api/v1/stats/summary | jq '.total_captures'
```
### List All Captures
```bash
curl -s http://localhost:8000/api/v1/query/captures | jq '.captures[] | {filename, lat: .latitude, lon: .longitude, freq: .frequency}'
```
### View by City
```bash
curl -s http://localhost:8000/api/v1/query/captures | jq '[.captures | group_by(.latitude | tostring) | .[] | {lat: .[0].latitude, lon: .[0].longitude, count: length}]'
```
---
## Summary
### ✅ Successfully Tested
1. **Wigle CSV import** - Bruce firmware style
2. **GPS filename parsing** - Flipper Zero style
3. **GPS companion JSON** - Custom format
4. **Batch directory import** - Multiple files
5. **Multi-city upload** - 5 US cities
6. **Data persistence** - JSON file storage
7. **Map visualization** - 20 markers displayed
### 📊 Platform Populated With
- **20 captures** from real Flipper Zero firmware test files
- **16 unique GPS locations** across USA
- **5 major cities** (LA, SF, NYC, Chicago, Austin)
- **3 frequency bands** (315, 433, 868 MHz)
- **7 different protocols** identified
### 🗺️ Map is Live!
All captures are now visible on the interactive map at http://localhost:8000
---
**Test Date**: 2026-01-13
**Platform**: GigLez v1.0.0
**Mode**: Simplified (JSON storage)