SIH26147/Space Technology

Space signal analysis and demodulation tool

A GUI-based software tool that takes .IQ or .wav files as input, extracts signal parameters, and performs demodulation, de-interleaving, and error correction.

National Technical Research Organisation (NTRO) · Software

Teams already in: Not read yet
Buildable in 36 hours: LowOur estimate, from their wording
Hard blockers: 2Restricted data, No dataset given
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

Why this fits you

This organisation declared winners on 13 problem statement(s) in SIH 2025 and on 13 in SIH 2024. It has awarded 4 joint winner(s), so it sometimes splits a prize between two teams.

What argues against it

Potential concern: needs data you may not get.
Potential concern: no dataset provided.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
File Input Interface
A graphical user interface to upload and ingest .IQ or .wav signal data files.
02
Parameter Extraction
An automated analysis module to identify sampling frequency, modulation type, FEC, and interleaving.
03
Demodulation Engine
A module to demodulate signals including FSK, QAM, and PSK.
04
De-interleaving and FEC
A module to carry out de-interleaving and forward error correction like Viterbi decoding and RS block codes.
05
Bit Stream Correlation
A component to perform bit stream correlation for identifying header and payload data.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The evaluators will check if the GUI can successfully take .IQ and .wav inputs, display spectral features like constellation plots and waterfall diagrams, and correctly perform parameter extraction, demodulation, de-interleaving, and FEC.

They never say

sample dataset files for training and testing
specific performance or accuracy targets

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Sample dataset files for training and testing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific performance or accuracy targets?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

9 more— the ones a panel gets to if there is time

The brief asks for file input interface. How would you build that?
Decoded from SIH26147 itself — A graphical user interface to upload and ingest .IQ or .wav signal data files. The brief asks for it by name.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
Needs data you may not get
The text mentions spectral relationship from training data containing both .IQ and .wav formats, which students may not possess.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Yes, and none is attached — finding or generating it is part of the work.

Also from National Technical Research Organisation (NTRO)

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26147, set by National Technical Research Organisation (NTRO).
Its official title, word for word: "Automated model for analysis of .IQ and .wav files along with signal parameter extraction"
Theme: Space Technology. Category: software.

In plain terms: A GUI-based software tool that takes .IQ or .wav files as input, extracts signal parameters, and performs demodulation, de-interleaving, and error correction.

## The organisers' full description, verbatim

• 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.

## What the brief never says

- sample dataset files for training and testing
- specific performance or accuracy targets
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- No dataset was attached, and the brief depends on data. Finding or generating it is part of the work.
- Known obstacles in the brief: Restricted data.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from National Technical Research Organisation (NTRO)'s published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (412 words)

• 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.