Tagged: AIS

SDR– Updated to V2.2: Many New Decoders, Tools and KrakenSDR Direction Finding and Beamforming Support Added

Last month we posted about the release of SDR-- (SDR minus minus), an open-source app that lets you visually wire together a receiver on a canvas. Since that time, Julian, the developer of SDR--, has been busy adding support for many decoders, various tools, and improving performance. Julian has also added KrakenSDR support, with radio direction-finding and beamforming blocks. Julian shares the following update for SDR-- V2.20:

New Decoders

  • Aviation and satellite: VDL2, HFDL, Inmarsat Aero and STD-C, Iridium, Drone Remote ID, 406 MHz beacons
  • Voice: TETRA, Tetrapol, GSM downlink, DECT, D-STAR, FreeDV, NXDN, dPMR
  • Data: LoRa (LoRaWAN, Meshtastic, MeshCore), ISM sensors, End-of-Train, DSC, BLE, Wi-Fi survey
  • Weather: NOAA APT, Meteor LRPT, WEFAX, radiosondes with landing forecast
  • Broadcast: DVB-T/T2, DVB-S2X, DAB packet mode, DRM with xHE-AAC

Coherent and mobile

  • Direction finding, beamforming and passive radar on the KrakenSDR
  • An iOS app with CarPlay for DF while driving is in TestFlight. Android is coming soon.

Hardware

  • Native Support: KrakenSDR, RTL-SDR Blog V4 Lite, AntSDR, PlutoSDR, SDRplay, Airspy, bladeRF and ESP-SDR
  • NanoVNA and antenna rotators
  • rtl_tcp and ADS-B Beast output for other apps

Tools

  • A satellite node with Doppler correction, plus a map with footprints and tracks
  • A spectrum monitor that classifies signals automatically, and a scanner with priorities and lockouts
  • Antenna calculator, noise figure, PER tester, SID monitor
  • Audio denoise, call recording, and a decoder log grouped by station
  • Event outputs: CSV, desktop notifications, PostgreSQL, InfluxDB

App

  • A redesigned UI with a unified look for every node
  • Multi-user editing, remote access from a browser, built-in HTTPS
  • An MCP server, so an AI assistant can build and tune a canvas

Performance

All measured on an M4 Max, full results at https://sdrmm.com/benchmarks:

  • DSP against GNU Radio:
  • FIR is 3.5x faster
  • NCO is 3x faster
  • FM demod is 4x faster
  • DDC is 1.6x faster
  • A 4096-point FFT is 1.6x faster than FFTW
  • Decoding rate in noise (48 trials per decoder across an SNR range):
  • ADS-B: 68%, against 56% for readsb and 50% for dump1090-fa
  • POCSAG: 73%, against 50% for multimon-ng
  • DMR: 81%, against 60% for dsd-fme
  • NXDN: 67%, against 23% for dsd-fme

(feel free to verify yourself with cargo xtask compare dsp)

The speed comes from hand-written SIMD kernels (NEON, AVX2, AVX-512), one shared downconversion for all channels on a radio, and a DSP path that never locks or allocates. The noise gains come from decoding IQ directly with
coherent receivers and soft decisions.

SDR-- Screenshot
SDR-- Screenshot

AI Disclaimer: This software is likely to be AI-assisted or vibe-coded.

FoxSDR Updates: Node Canvas, Single Map Display, Linux+Android Builds, Locating Air Traffic Radar Positions

Thank you to wonderingStars for writing in and sharing updates to his FoxSDR software program. FoxSDR is a new AI-coded software that supports all common protocols and SDRs.

The first update that wonderingStars notes is the addition of a node canvas where you can wire radios, decoders, speakers, and displays together. It can run up to five SDRs at the same time, each with its own decoders. This is now FoxSDR's main screen.

The second update is a map that can accept multiple inputs from multiple decoders, allowing users to plot various data streams such as ADS-B, AIS, amateur high-altitude balloons, and more on a single map.

The third update is that Linux x64 builds are now published, although they are noted to still be in development. An Android arm64 build has also been released in alpha form. To access this, users will need to sign up to test.

The final update adds a new radar sweep feature, which locates the positions of air-traffic radars around you by listening to 1090 MHz. The author explains:

Secondary surveillance radars rotate and interrogate aircraft transponders as their beam passes. Aircraft that broadcast ADS-B also report their own position, so every reply they give reveals where a radar was pointing at that moment. Radar Sweep collects these replies over hours and works out each radar's location, rotation speed and direction on its own. It then draws bearing lines towards aircraft that answer the radar but broadcast no position, and marks a position where two radars' lines cross.

It is the same idea as PlanePlotter's Beamfinder, except that Radar Sweep finds the radar sites itself instead of being given them. It is not affiliated with PlanePlotter or its authors.

In testing on a 12.8-hour recording, it confirmed two radars, each within about 5 km of a published airfield position. Bearings to held-out aircraft had a median error of 0.3°.

You need a 1090 MHz antenna, an SDR running at 2 MS/s or more, and a few hours of traffic. Radar Sweep remembers the radars it finds and never reveals your location.

It is available free to FoxSDR users in the United States.

The author also wanted to share an upcoming feature, which is the GUI builder, which will allow you to customize your own GUI.

FoxSDR: New Node Canvas + Radar Sweep
FoxSDR: New Node Canvas + Radar Sweep

AI Disclaimer: wonderingStars describes their company as a human-gated AI company, and FoxSDR is AI coded.

RF-Traffic-Monitor: Track Aircraft, Ships, Drones and Radiosondes in One Program

Thank you to Stefanos (Steeod) for sharing a new program he created, "RF-traffic-monitor." Stefanos created this program because he initially wanted to install adsb.im (an ADS-B feeder image that also tracks ships, radiosondes, and ACARS/VDL2/HFDL) on Windows 10, but was unsuccessful in doing so, as adsb.im is written for Linux machines.

So, to get around this, Stefanos decided to fire up ChatGPT to write his own similar Windows program. The end result was RF-traffic-monitor, which is capable of tracking aircraft, ships, and radiosondes using an RTL-SDR or HackRF. It can also use the SDR to decode ACARS, VDL2, and HFDL. With a compatible WiFi adatper the software is also capable of tracking drones via Wi-Fi Remote ID.

RF-Traffic-Monitor Screenshot
RF-Traffic-Monitor Screenshot

AI Disclaimer: The author notes that this program was written by ChatGPT.

SDR–: A Software-Defined Radio Application with Visual Signal Path

Thank you to Julian for writing in and sharing with us his interesting project called SDR-- (SDR minus minus). SDR-- is an open-source app that lets you wire devices, decoders, and displays together on a desktop window or browser-based canvas. It is described as "a software-defined radio application with a visual signal path. Connect devices, decoders, displays, and recorders on a canvas, then pin the controls you use to a rack."

A separate Rust server, which can sit on the same PC or a remote computer (like a Raspberry Pi), handles the DSP. Julian notes that SDR-- has native RTL-SDR support, and decoders for ADS-B, AIS, POCSAG, FT8, SSTV, and RDS are already implemented, with more in progress. The app also has a built-in signal generator and a repository of IQ recordings that can be used for experimentation and testing the various decoders.

The project releases are available on GitHub releases, and they cover Windows, Linux, and MacOS. 

SDR-- Screenshot
SDR-- Screenshot

IC-SDR: A New Multimode SDR Software for Windows Written in Go

Thank you to Luis Lopez Martinez for writing in and sharing the release of his open-source software, IC-SDR, a new multi-mode SDR application for Windows written in Gowith a focus on performance and efficiency. Luis writes:

IC-SDR supports AM, NFM, WFM, LSB and USB demodulation, along with several integrated digital decoders:

  • AIS
  • ADS-B (1090 MHz and UAT 978 MHz)
  • Radiosondes
  • APRS
  • RTL_433
  • DMR
  • SSTV
  • TETRA

It also includes a memory bank, real-time spectrum and waterfall displays, an audio recorder with automatic silence removal, and a frequency-segment scanner with instant triggering.

The Windows release can be found on the project's GitHub Releases page.

9GRadio: An Android RTL-SDR App for Spectrum Monitoring and Decoding Analog/HAM/Aviation/Marine Radio Traffic

Thank you to Richard (9G5AR) for writing in and sharing with us the release of his open source Android app called 9GRadio. Richard writes that 9GRadio is an app designed for spectrum monitoring/analysis and the decoding of analog/ham/aviation and marine radio traffic.

In terms of demodulation it supports AM, FM, NFM, WFM, WFM Stereo, USB, LSB, CW, CWR, DSB and RAW IQ modes.

For digital decoding, it supports APRS, digital voice modes such as DMR, D-STAR, YSF, dPMR, NXDN, and aircraft, marine, and datalink modes such as ADS-B, AIS, ACARS, RDS, and multilateration.

Richard also notes that 9GRadio includes a full multilateration stack for locating transmitters, which works with 2+ networked 9GRadio receivers. Interestingly, the multilateration stack is noted to be protocol-agnostic, meaning it should work not only with ADS-B, but also with any mode that provides a digital identity for a transmission, such as AIS, ACARS, RDS, and digital voice modes.

The app is not available on the Google Play Store, but the APK is available for sideloading on the project's GitHub Releases page, along with the open source code.

9GRadio GIF of various screens
9GRadio GIF of various screens

NyxScope: A Windows Multi-Protocol SDR Decoder Program with Multiple Digital Native Decoders

WARNING: Multiple people have noted that this program is not working as expected and may be overly buggy.

Recently, we've learned about NyxScope, a multi-protocol SDR receiver program for Windows that comes with multiple native decoders built right into the software. Their own description describes this all-in-one program best:

You get spectrum and waterfall, multiple concurrent VFOs, trunked-radio following, digital voice, aviation and marine tracking, paging, ISM sensors, HD Radio, and transcription, in one binary.

NyxScope includes decoders for P25 Phase 1 and Phase 2 voice, EDACS and NXDN control channels, ADS-B, AIS, ACARS, POCSAG, FLEX, LoRa CSS PHY + LoRaWAN MAC, Morse, RDS, CTCSS/DCS, Iridium decoding (with voice), and also includes a signal classification tool, Bluetooth LE scanner, and Whisper voice-to-text transcriber. It also outsources decoding of other protocols to mature decoding software such as multimon-ng, rtl_433, dsd-neo, nrsc5, direwolf, dumpvdl2, dump978, rs41mod where required, noting that the outsourced decoders are bundled with the software, meaning no extra installation work is required.

The software supports RTL-SDR, HackRF, Airpsy, bladeRF, SDRplay, Fobos and PlutoSDR. It also supports using multiple SDRs used in parallel.

The software does not appear to be open source, but it is provided for free with a limitation of 3 concurrent VFOs and a limitation on recording, transcribing, and pager messages. A perpetual per-machine license for $89.95 can be purchased to lift these restrictions and add access to their FCC frequency lookup database.

AI-Disclaimer: While the developers have not noted any use of AI tools, we suspect that AI was used in the creation of this software.

NyxScope Screenshot Scanning the 800 MHz Band
NyxScope Screenshot Scanning the 800 MHz Band

Decentralizing AIS: Trustless Maritime Tracking with SDR

Recently, Owen Taylor, the CEO of WAKE (Worldwide AIS Network), wrote in to us asking if they could promote their project, which is a decentralized AIS aggregation network based on receivers like RTL-SDR. The twist compared to existing aggregators like marinetraffic.com is that WAKE aims to reward users via a crypto token and simultaneously solve the distributed verification problem to avoid problems like spoofing and poor transparency. 

The post below is their own words, and we note that we are not affiliated with WAKE.


Every second, ships transmit short bursts of data over VHF, broadcasting their position, speed, course, and identity. This is AIS (Automatic Identification System), an open, unencrypted protocol that lets vessels, ports, and coastal authorities maintain a shared picture of maritime traffic. Beyond collision avoidance, AIS feeds into port logistics, environmental monitoring, search and rescue operations, and even the financial analysis that drives global commodities trading.

For years, much of this coverage has been built on a mix of official receivers, satellites and a scattered network of volunteers, many of them SDR hobbyists streaming data from antennas on rooftops and coastal hills.

This model works, until it doesn’t. AIS has a well-known weakness: there’s no built-in authentication. Anyone can transmit a valid-looking AIS message. That opens the door to errors and deliberate spoofing, and right now there’s no universal method for verifying what’s real.

How AIS Works

AIS operates on two dedicated VHF channels, 161.975 MHz and 162.025 MHz, using 9600 bps Gaussian Minimum Shift Keying (GMSK) modulation. Transmissions follow a self-organizing time division multiple access (SOTDMA) scheme, where each station selects its own time slots to avoid collisions.

An AIS message can carry vessel identity (MMSI), position (latitude, longitude), speed over ground (SOG), course over ground (COG), navigational status, and other voyage data. Ships transmit at intervals from every few seconds (for fast-moving craft) to every few minutes (for anchored vessels).

For terrestrial reception, the chain looks familiar to any SDR operator:

Antenna → RTL-SDR (or similar) → AIS decoder software → Data feed.

Noise floor, antenna gain, and local RF environment all influence range, which for a coastal VHF station is typically 20–40 nautical miles. Higher elevations and directional antennas can stretch this significantly.

The Current Aggregation Model

Global AIS coverage today comes from a mix of satellite AIS for open-ocean tracking and terrestrial AIS for coastal areas, ports, and choke points. The terrestrial component is heavily dependent on a patchwork of volunteer-operated receivers, often nothing more than a VHF antenna, an RTL-SDR, and a small computer feeding data into a central platform.

Commercial services like MarineTraffic (now owned by Kpler), VesselFinder, and AISHub aggregate these feeds into global datasets that are then resold to shipping companies, commodity traders, insurers, and governments. The scale is impressive, but it comes at a cost to transparency.

In a recent video circulating on Reddit, the CEO of Kpler openly described their “monopoly” on maritime data, built on the volunteers giving up their data for free. While this may be good for their commercial positioning, it also highlights the underlying issue: a small number of companies effectively control access to AIS data, much of which was gathered for free from hobbyists.

From a technical perspective, the aggregation model has another weakness: it is built on trust. If a feed sends false data, whether through AIS spoofing, misconfigured hardware, or bad GPS input, that information can still enter the global record. Most platforms only filter out data that is obviously invalid, and there is no universal multi-source verification or cryptographic proof of authenticity in the AIS ecosystem.

The Data Integrity Problem

AIS is intentionally open and unencrypted to encourage wide adoption and interoperability. The downside is that nothing stops someone from transmitting a false position for a real ship or inventing an entirely fake vessel.

Spoofing incidents have been documented around the world. “Ghost ships” have appeared hundreds of miles inland. Vessel positions have been falsified to hide illegal fishing or smuggling. In some regions, ships broadcast fake locations to evade sanctions or mislead competitors.

Because AIS is used for everything from traffic management to environmental compliance, bad data has real consequences. It can mislead port authorities, disrupt logistics chains, and undermine safety systems that depend on knowing exactly who is nearby.

Distributed Verification

When we talk about “distributed” in this context, we mean a network of many independent AIS receivers,  owned and operated by different people in different locations, all working together to validate the same signals. No single entity has control over the data pipeline, and no single point of failure can compromise the entire dataset.

This approach aligns with what’s known as DePIN (Decentralized Physical Infrastructure Networks). In a DePIN, real-world hardware, in this case, AIS receiving stations powered by RTL-SDR dongles, is deployed by a distributed community, and the data it produces is aggregated, validated, and made available on a blockchain. Contributors are often incentivized for their role in maintaining the physical infrastructure and supplying high-quality data.

Applied to AIS, DePIN solves the monopoly and trust problem by creating:

  • Redundancy — multiple stations cover the same area, making spoofing and errors easier to detect.

  • Transparency — all verification events can be independently audited.

  • Resilience — coverage doesn’t vanish if one provider shuts down or changes terms.

From a technical perspective, defeating AIS spoofing requires proving that a received message is both authentic and physically plausible. A distributed verification system can achieve this by:

  1. Time of Arrival (TOA) Checks
    Comparing reception timestamps across geographically separated receivers. A false signal transmitted from shore will produce a different TOA pattern than one from a vessel at sea.

  2. Motion Consistency
    Checking positions against realistic limits for speed, acceleration, and turn rate. If a ship appears to jump 50 nautical miles in a minute, it fails.

  3. Cross-Coverage Triangulation
    Using relative signal strength and geometry between receivers to estimate origin and compare it to the reported position.

  4. Peer Agreement
    Looking for identical messages confirmed by several uncorrelated receivers. Messages verified by multiple independent nodes have a much higher trust score.

Once these checks are complete, the verification results, not necessarily the raw AIS payload, can be recorded on a tamper-proof, public ledger (such as a blockchain). This creates a permanent, auditable history of which AIS messages passed validation and when, allowing anyone to verify the integrity of past data without relying on a single company’s database.

Incentivizing a Trustless AIS Network

WAKE (Worldwide AIS Network) applies distributed verification principles to AIS in a way that directly involves and rewards the SDR community. At its core, WAKE is a decentralized network of independently operated AIS receivers, often built around RTL-SDRs or similar hardware,  working together to validate and record maritime data in a public, tamper-proof ledger.

In the WAKE model, contributors run AIS stations that submit decoded messages to the network. These messages are cross-checked against others received in the same geographic area. A message is only accepted if it passes both multi-receiver consensus and physics-based checks such as motion consistency and TOA analysis. This ensures that false or spoofed data is rejected before it ever reaches the historical record.

For the SDR community, this represents an evolution of an existing role. Many hobbyists already contribute AIS feeds without recognition or compensation. In WAKE, you’re not just relaying what you receive, you’re part of a validation mesh that makes AIS data more secure and tamper-resistant.

Because WAKE is built on a trustless model, no single operator, including WAKE itself can alter or suppress verified data. The integrity of the maritime picture is maintained collectively, with every contributor helping to keep it honest.

For SDR operators, the incentive is clear: you can keep doing what you already do best, running reliable receivers with clean reception and precise timing, but now with direct rewards for your contribution and the satisfaction of helping build a more open, diverse, and verifiable AIS data ecosystem.

You can learn more about WAKE Here.

Closing Thoughts

AIS has transformed maritime safety and logistics, but it was designed for trust, not for security. That’s fine when all players are honest, but as spoofing incidents have shown, trust alone isn’t enough.

A verification layer built on distributed SDR receivers is one of the most promising paths toward a tamper-proof global AIS dataset. It’s not about replacing the existing AIS ecosystem, but strengthening it.

The SDR community is uniquely positioned to lead that shift. By participating in networks that focus on data integrity, you can help ensure the maritime picture is as accurate tomorrow as it was yesterday, and maybe even fill in the parts of the map no one else can see.