🌙 LUNAR DESCENT — BSides San Diego 2026 RF Village CTF

A capture-the-flag challenge based on a real signal processing problem in a radar altimeter.

ISRO's KaRA radar altimeter guided Chandrayaan-3 to a soft lunar landing on 23 August 2023. The Radar Altimeter Processor (RAP) computes altitude and velocity from FMCW chirp signals, running on a single Xilinx Virtex-5 FPGA. This CTF uses a Python model of that system — faithful to the published paper — where the altimeter feeds a landing autopilot. The altimeter works perfectly. The autopilot keeps crashing. Why?

For Participants

pip install numpy matplotlib
python lunar_descent_ctf.py --help               # See all options
python lunar_descent_ctf.py                      # Run the mission — watch it crash
python lunar_descent_ctf.py --modes              # See the sweep mode table
python lunar_descent_ctf.py --test -p all        # Test all three profiles
python lunar_descent_ctf.py --score              # Score your fix and earn flags

Rules:

• Edit ONLY the MeasurementQualifier class (clearly marked in the source)
• Don't change the RAP, signal generation, autopilot, or scoring
• The qualifier decides what the autopilot sees — fix it there
• Submit flags at the RF Village table

Three Flags

Zero flags are free. The buggy code scores 0 / 1000 out of the box.

Flag	Points	Challenge
🔍 RECON	100	Explain the bug to village staff (no hash on screen)
🔭 FIRST LIGHT	500	Land all three profiles without crashing
📡 NO GAPS	400	Zero qualifier rejections on all profiles

Total: 1000 points

The Scenario

The KaRA radar altimeter was tested on helicopters and aircraft at altitudes above 50 meters. It worked flawlessly. Field test performance met all mission specifications.

The altimeter is now integrated with a landing autopilot that uses both altitude and velocity measurements for thrust control during final approach. In simulation, the autopilot crashes the lander every time below 15 meters altitude. The altitude readings are fine — sub-meter accuracy all the way to touchdown. Something else is killing the lander.

You need to find out what's going wrong and fix the measurement qualification logic so the autopilot can land safely.

Difficulty Curve

• 0 pts — Running the code unmodified. The default run shows OK status all the way down until the final approach, then CRASH.
• 100 pts — Explaining the problem to staff.
• 600 pts — Fixing the MeasurementQualifier so it can land.
• 1000 pts — Eliminating all qualifier rejections.

Validating Flag 1 (RECON)

No hash is printed on screen for Flag 1. Staff issue the flag manually.

Timing

The CTF can run all day alongside the workshop modules and talks. It's self-paced and doesn't require staff attention except for Flag 1 validation and prize distribution.

Test Profiles

Profile	Character	What It Tests
standard	Chandrayaan-3-like smooth descent, 10 km → 3 m, with altitude excursion at 20 m (thruster anomaly or drifting over crater)	Landing
aggressive	Fast exponential braking, 10 km → 3 m in 400 s	Rapid mode transitions at high altitude + Landing
stepwise	Hover at guard band boundaries (9851/4795/2334/553/131/31/5 m), drop between them	Mode transitions + Low Hover

The Physics

The RAP uses FMCW radar. Up-chirp and down-chirp signals produce beat frequencies:

• f_up = fb − fd (up-chirp)
• f_dn = fb + fd (down-chirp)

Altitude comes from the sum: R = M × (f_up_index + f_dn_index).

Velocity comes from the difference: fd = (f_dn_index − f_up_index) × freq_res / 2.

The FFT is always 8192 points, but the number of real signal samples depends on the sweep time:

• Mode 12 (high altitude): 8192 samples → full FFT
• Mode 0 (3 m altitude): 14 samples → 99.8% zero-padding

Connection to Real Engineering

The problem is pedagogically framed but the pattern is real. Sensor qualification, knowing when to trust a measurement and when to reject it, is important.

• Radar and sonar tracking systems (Doppler reliability vs integration time)
• GPS/INS integration (knowing when satellite geometry is too poor to trust)
• Medical imaging (SNR-dependent confidence in measurements)
• Autonomous vehicle sensor fusion (camera vs lidar vs radar confidence)

The paper mentions "three sample qualification logics to generate the final altitude" without detailing them. What are those logics? Will some of those qualification logics help solve this CTF?

Source

Based on: Sharma et al., "FPGA Implementation of a Hardware-Optimized Autonomous Real-Time Radar Altimeter Processor for Interplanetary Landing Missions," IEEE A&E Systems Magazine, Vol. 41, No. 1, January 2026. DOI: 10.1109/MAES.2025.3595090

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