948c966226
- WiFi CSI-based indoor positioning system for self-hosted home environments - docs/plan/plan.md: full 9-phase implementation plan (65 gaps closed by analysis) - docs/research/: CSI fundamentals, physics, algorithms, signal processing, mesh topology, accuracy limits, literature - docs/notes/: recovery mechanisms, simulation testing, UX visualization - .marathon/instruction.md: per-iteration marathon instructions with detailed commit format - .marathon/start.sh: GLM-5 tmux launcher via ZAI proxy Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
111 lines
5.1 KiB
Markdown
111 lines
5.1 KiB
Markdown
# Realistic Accuracy Bounds and Limitations
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## Localization Accuracy by System Type
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| System type | Typical accuracy | Conditions |
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| RSSI fingerprinting | 2–5 m | Calibrated environment |
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| CSI fingerprinting | 0.5–2 m | Controlled environment |
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| CSI model-based (geometric) | 0.5–1.5 m | Good geometry, single moving person |
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| SpotFi (MUSIC + subcarrier extension) | ~40 cm median | 3-antenna AP, calibrated |
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| IndoTrack (Doppler-MUSIC) | ~35 cm median | 3 antennas, commodity hardware |
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| Widar2.0 (single link) | ~75 cm median | Moving target |
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| Widar2.0 (two links) | ~63 cm median | Moving target |
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| Multi-link geometric (4–8 nodes) | 0.5–1.0 m | Typical indoor room |
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### 3D (Z-axis) Accuracy
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- Z-axis accuracy is typically worse than XY: **1–3 m** with geometric approaches
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- Machine learning approaches have achieved 0.4 m vertical RMSE in some studies
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- Fundamental limit from wavelength: 2.4 GHz → λ/4 = 3.1 cm minimum theoretical resolution per dimension (unreachable with commodity hardware noise)
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- Z accuracy improves significantly with nodes at mixed heights
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---
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## The Stillness Problem
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This is the most significant practical limitation. When a person is motionless:
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- **No Doppler shift** → DFS-based methods (Widar, IndoTrack) fail entirely
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- **EMA baseline adapts** → person gradually absorbed into background; presence fades
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- **Variance-based detectors** see near-zero variance → interpret as empty room
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### Mitigations
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| Mitigation | Mechanism | Limitation |
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| Long EMA time constant (α = 0.999) | Slows background adaptation | Eventually still absorbs stationary person |
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| Motion-gated baseline update | Only update when variance < threshold | Requires quiet periods to capture baseline |
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| Breathing detection (0.1–0.5 Hz) | Chest movement ~5 mm still detectable | Needs 10–30 s averaging; fails with noisy environment |
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| Entry/exit hysteresis | Require exit event before marking absent | Doesn't detect initial entry of already-still person |
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| Static CSI fingerprint comparison | Empty vs. occupied room differ by 3–12 dB on affected links | Requires clean empty-room reference |
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**Breathing at 2.4 GHz**: chest displacement ~5 mm → phase change ≈ 2π × 2 × 0.005 / 0.125 ≈ **0.5 rad**. Detectable above noise with sufficient averaging.
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---
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## Multiple Person Degradation
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From WiMANS dataset and related work:
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| People | Accuracy degradation |
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| 1 | Baseline (e.g. 35 cm for IndoTrack) |
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| 2 | ~2× increase in error; ambiguity from two reflection sources |
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| 3 | ~3–4× error increase |
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| 5 | Localization error +15.4%, activity recognition −25.7% vs. single person |
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Multi-person tracking is an open research problem. Current approaches:
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- Track as many simultaneous targets as there are resolvable Doppler sources (limited by angular/frequency resolution)
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- Use multiple antennas with MUSIC to spatially separate sources
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- Deep learning end-to-end approaches show promise but require large training datasets
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---
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## Environmental Sensitivity
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| Change | Effect | Recovery |
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| Furniture moved | Static multipath changes → fingerprint invalid | Forced baseline reset + slow EMA re-adaptation |
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| Temperature change (AC on/off) | Slight material property and phase shifts | Slow EMA absorbs over hours |
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| High-activity elsewhere in building | Background dynamic CSI from other rooms | Narrow sensing band; spatial filtering |
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| Humidity / rain | Affects building material dielectric properties | Slow EMA absorbs |
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| New large objects added | New permanent multipath components | Forced baseline reset |
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---
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## ESP32-Specific Limitations
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| Limitation | Impact | Workaround |
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| Single antenna | No direct AoA from one node | Multi-node mesh provides angular diversity |
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| int8 dynamic range | Saturates near nodes; low SNR far from nodes | Place nodes at 2–5 m from sensing area |
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| No phase coherence across nodes | Cannot directly apply array MUSIC to multi-node data | Use geometric Fresnel method instead |
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| No 5 GHz | Limited to 2.4 GHz (12.5 cm wavelength) | Adequate for body-scale detection |
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| ToF resolution at 20 MHz | c/(2B) = 7.5 m — useless for ranging | Manual node position measurement |
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| Packet rate ~20 Hz | Limits DFS resolution to ±10 Hz | Adequate for walking (16 Hz DFS) |
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---
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## What Spaxel Can Realistically Achieve
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### Conservative (safe to claim)
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- **Presence detection** (someone in the room vs. empty): reliable with 2+ nodes on opposite sides
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- **Approximate 2D position** (±0.5–1.0 m): reliable with 4+ well-placed nodes for a moving person
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- **Motion detection and tracking**: reliable with 4+ nodes
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- **Rough count** (0 vs. 1 vs. 2+ people): works in practice, degrades with 3+
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### Possible with Good Conditions
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- **Rough 3D position** (±1–2 m Z): with nodes at mixed heights
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- **Stationary person detection**: via breathing detection on stable setup
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- **Velocity estimation**: via DFS analysis
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### Not Achievable with Commodity ESP32-S3
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- Sub-10 cm accuracy
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- Reliable skeletal pose estimation
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- Fine-grained limb position
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- Reliable tracking of 5+ simultaneous people
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- Through-floor sensing (different frequency needed)
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