RF Fingerprint Lab

Similarity search over RF spectrum fingerprints (research).

Industry	Level	Difficulty	Recommended mode
Electrical & electronics	5 of 5	Frontier	sq8

Coming soon

A complete, runnable RF Fingerprint Lab reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• RF environments are crowded.
• Identifying emitters is hard.
• Data volumes are high.

How SrvDB powers RF Fingerprint Lab

1. Spectrum features become vectors in SrvDB.
2. Unknown signals retrieve similar known fingerprints.
3. sq8 for large libraries.
4. Offline operation in the field.

What changes

Today	With RF Fingerprint Lab on SrvDB
Manual spectrum analysis	Similarity lookup
Large raw data	Compact vectors
Lab-only tools	Field-capable

How it works

1. Extract spectral features.
2. Store labelled fingerprints.
3. Query unknowns.
4. Validate.
5. Grow the library.

SrvDB features it uses

• SQ8 compression (sq8): stores each value in one byte, 4× smaller than full precision.
• Fully offline: SrvDB runs in-process and never opens a network connection.
• Rust API: embed SrvDB directly in Rust services and devices.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Interference hunting
• Device identification
• Spectrum monitoring
• Test labs
• IoT diagnostics
• Research datasets

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./rf-fingerprint-lab-db", dimension=128, mode="sq8")

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def known_emitters_like(fingerprint, k=5):
    hits = db.search(embed(fingerprint), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Research direction; accuracy depends on features and must be validated.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Electrical & electronics projects

Level	Project	What it does
1 · Beginner	Datasheet Q&A	Ask questions of your component datasheets and get page citations.
2 · Intermediate	Second-source Finder	Suggests substitute components by specification similarity.
3 · Advanced	Test Log Analyzer	Finds similar past test failures and anomalous logs.
4 · Expert	Hardware Lab Agent	An agent that remembers bench sessions, scope captures and decisions.
5 · Frontier	RF Fingerprint Lab (this page)	Similarity search over RF spectrum fingerprints (research).

See also: Use cases · Live examples (coming soon)