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SIH26147SoftwareSpace Technology
₹1,50,000 Prize (₹75k Joint)
•
Deadline: 20 September 2026
•
Submitted Ideas: 0/500

Automated model for analysis of .IQ and .wav files along with signal parameter extraction

Ministry / Organization: National Technical Research Organisation (NTRO)
SIH Official Portal
Technical Complexity
8/10
Dataset Accessibility
7/10
36h Hackathon Viability
9/10
Step 0 • Zero-Jargon Layman Summary

“This project involves building a smart software tool that automatically analyzes radio signals captured from the air. It identifies what kind of signal it is, cleans up the data, and extracts the hidden information inside, which is currently done manually and slowly by experts.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

A GUI-based Signal Intelligence (SIGINT) software suite that performs automated modulation classification, demodulation, de-interleaving, and error correction on .IQ and .wav radio frequency files.

Step 2

WHO IS IT FOR?

National Technical Research Organisation (NTRO) analysts and signal intelligence officers who need to process large volumes of intercepted radio data rapidly.

Step 3

WHY DOES IT EXIST?

Manual analysis is slow, prone to human error, and cannot keep up with the high volume of signals across HF, VHF, and UHF bands. Existing tools are fragmented, requiring a unified automated pipeline for actionable intelligence.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

.IQ and .wav raw radio frequency files, metadata regarding sensor location/type.

02 • Processing & AI Core

Spectral analysis (FFT/Waterfall), Deep Learning-based Modulation Classification (CNN/LSTM), Viterbi/LDPC decoding, and bit-stream correlation.

03 • Actionable Deliverables

Interactive GUI dashboard showing constellation plots, waterfall diagrams, identified signal parameters, and the extracted raw bit-stream/payload.

Step 5

CONSTRAINTS

  • Must handle high-frequency data with minimal latency.
  • Must support multiple modulation and coding schemes (FEC).
  • Must be offline-capable for secure environments.
  • High precision required for signal parameter identification.
Step 6

JURY METRICS

  • Classification accuracy (Modulation/FEC type).
  • Bit Error Rate (BER) reduction post-FEC.
  • Processing speed (throughput of signal analysis).
  • GUI usability and visualization clarity.
Step 7

CORE BOTTLENECK

The 'Blind' parameter estimation—identifying modulation and FEC schemes without knowing the transmitter's configuration beforehand—is a highly complex mathematical challenge.

Why You Should Choose This PS (Pros)

  • High strategic value to national security agencies.
  • Clear, well-defined technical requirements.
  • Strong potential for R&D and patent filing.
  • Showcases advanced signal processing and AI skills.

Challenges & Risks to Watch (Cons)

  • Requires deep domain knowledge of Digital Signal Processing (DSP).
  • Lack of real-world classified datasets will require synthetic data generation.
  • High mathematical complexity.
Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile
PyQt6 or PySide6 (for high-performance desktop GUI)Matplotlib/PyQtGraph for real-time plotting
Backend & APIs
Python (NumPy, SciPy, Scikit-learn)GNU Radio (for signal processing blocks)
AI / ML & Models
CNN-LSTM hybrid models for modulation classificationAutoencoders for signal denoising
Database & Cloud
SQLite (for local metadata)HDF5 (for efficient storage of large signal arrays)

Killer Features to Impress the SIH Jury

Automated 'Signal Fingerprinting' to identify the source sensor.
Real-time Waterfall visualization with interactive zoom/pan.
One-click 'Auto-Demodulate' pipeline that chains FEC and de-interleaving.
Synthetic data generator to train models on various noise levels.
View Official SIH 2026 Portal Raw Text
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•Background The raw data for analysis of signal collected off the air typically range from few Khz to Ghz bands. The analysis is being carried out manually to identify the signal parameters and the resultant data is then utilised for processing signals in the designated sensors. This data is often insufficient for fine grain analysis for parameter extraction such as modulation type, sampling rate, FEC, interleaving, etc. This creates a need for advanced data processing to extract the observation data.
•Description The terrestrial signals received from various sources includes data in HF, VHF and UHF bands. The raw data collected in the form of .wav or .IQ format to retain the characteristics of wave form. The analysis of signals is primarily dependent on the basic characteristics of data points selected during recording of these signals. Since the data point are recorded from different sensors and different locations, the parameters may vary. Therefore, the data available for analysis is often insufficient to clearly identify fine details such as sampling rate, modulation type, interleaving, FEC etc. This limitation reduces the accuracy and confidence of interpretation and data analysis that require detailed information. The data saved as .IQ and .wav have different parameters and therefore they store the raw information in different format. These files have to be processed in different ways for signal analysis to extract signal parameters. The problem can be addressed using advanced models such as GNU Radio, python, C++ to enhance the parameter extraction capability and more information rich inputs. The spectral relationship from training data containing both .IQ and .wav formats can be utilised for identifying signal parameters and carry out deeper analysis. The expected solution should be able to demodulate signals.

The GUI based model will have features to take .IQ or .wav file as input data and perform following tasks.

i.Identify signal parameters (Sampling frequency, Modulation, FEC, Interleaving). Additional features if feasible may be included.

ii.Demodulate signals (FSK, QAM PSK)

iii.Carry out de-interleaving (Block, Convolution, Diagonal, Pseudo Random).

iv.FEC (short-constrained convolution codes with Viterbi decoding, RS block codes, Concatenated codes, LDPC).

v.Bit stream correlation.

•Expected Solution The expected system should improve feature visibility of signals with the help of GUI, enable automated signal analysis to identify spectral features such as sampling frequency, constellation plot, water fall (time-frequency domain), demodulate signals, carry out de-interleaving and error correction. The output can then be used to carry out correlation of bit stream for identification of header and payload.