Show HN: SentryRF – A private, local-first Android app to detect hidden trackers

[vidoluc]

Hi HN,

I built SentryRF because I was frustrated by the lack of granular control on Android for identifying unwanted tracking devices. While Google's native "Unknown Tracker Alerts" are a good start, they often feel like a black box with limited diagnostic tools.

The Tech:

Signal Analysis: Instead of just listing every BLE/wifi device, SentryRF uses signal strength (RSSI) and temporal patterns to distinguish between a "passing" device (someone walking by with an AirTag) and a "following" device (a tracker on your vehicle), even peeping-toms.

Hardware Sensors: I’ve integrated the magnetometer and ambient light sensors to help find non-broadcasting devices (like "dead" GPS units or wired pinhole cameras) by detecting magnetic anomalies and infrared emissions.

Sound Locator: I implemented an audio-guided proximity algorithm that increases beep frequency as you approach the source. It’s essentially a Geiger counter for Bluetooth and wifi signals.

Privacy (The most important part):

Zero Cloud: There is no backend. No scan data, location data, or telemetry ever leaves the device.

No Accounts: You don’t need to sign up. I don't want your email or your name.

Permissions: It requires quiet a few permissions, but I’ve documented exactly why each is needed in the app’s onboarding.

Why I’m showing it here: I’m looking for technical feedback on the signal smoothing I’m using for the proximity tracker and any edge cases I might have missed regarding the rogue cell tower (IMSI catcher) detection.

The app has a 7-day free trial so you can test the "Pro" features (like the Sound Locator) without paying a cent.

I'll be around all day to answer questions about the stack or the detection logic!

[vidoluc]

SentryRF: Cognitive Counter-Surveillance SentryRF is a professional-grade RF (Radio Frequency) situational awareness tool for Android. Unlike "camera finders" that rely on optical reflection, SentryRF operates at the signal intelligence (SIGINT) layer, detecting the digital footprint of surveillance hardware.

Core Capabilities: Multi-Protocol Detection: Scans the 2.4GHz spectrum to identify AirTags, Tiles, SmartTags, and unbranded BLE trackers using high-fidelity manufacturer data parsing.

Behavioral AI Engine: Employs a local neural network to differentiate between "ambient noise" (a neighbor's headphones) and "threat patterns" (a device following your trajectory across multiple GPS coordinates).

Tactical Sound Locator: Uses an RSSI-weighted triangulation engine and haptic/audio feedback to guide users to a hidden device's physical location through signal gradient analysis.

Deep Hardware Analysis: Identifies rogue WiFi "Evil Twins," IMSI catchers, and unauthorized IoT devices using OUI lookups and chipset-family fingerprinting.

Privacy-Centric Architecture: All analysis, including ML retraining and database storage, happens 100% on-device. No RF data or location history ever leaves the handset.

[vidoluc]

All on-device AI training, for threats and locator.

[lgats]

kinda shady to just have a bunch of "reviews" all made up.

[vidoluc]

You are right, to be honest I had a feeling about that too, thanks for the feedback! It makes a ton of difference.

[vidoluc]

Promocode 3 months free PHSENTRYRF