Policy Driven RF Denoising for Adaptive Geolocation FFT-Domain Filtering

Policy Driven RF Denoising for Adaptive Geolocation FFT-Domain Filtering

Revolutionizing RF Signal Processing: A Smarter Way to Denoise with Reinforcement Learning

Hey everyone, Benjamin J. Gilbert here from the College of the Mainland. As someone waist-high in robotic process automation and signal processing, I’m excited to share my latest research paper: “Policy-Driven RF Denoising for Adaptive Geolocation: A Reinforcement Learning Approach to FFT-Domain Filtering”. If you’ve ever dealt with noisy radio frequency (RF) signals—whether in geolocation systems, cognitive radio, or even everyday wireless tech—you know how tricky it can be to filter out interference without losing critical data. This work tackles that head-on by blending classic signal processing with cutting-edge AI, specifically reinforcement learning (RL). Let’s dive in!

The Problem: Noisy Signals in a Jammed World

Imagine trying to pinpoint a target’s location using RF signals from multiple sensors. Techniques like Time-Difference-of-Arrival (TDoA) rely on super-precise timing measurements to get sub-meter accuracy. But real-world RF environments are messy: additive white Gaussian noise (AWGN) drops the signal-to-noise ratio (SNR), and narrowband jammers can spike specific frequencies, distorting correlations and throwing off your estimates.

Traditional fixes? Static filters like fixed low-pass for bandwidth control or manual notch filters for jamming. They’re efficient but rigid—they don’t adapt to changing conditions and might accidentally strip away useful signal bits. Machine learning has hinted at better ways, but many approaches (like full neural denoisers) are black boxes: hard to interpret, compute-heavy, and not always aligned with physical metrics like timing accuracy.

My Approach: RL as a Smart Filter Controller

Enter the policy-driven framework. I treat denoising as a sequential decision-making problem—a Markov Decision Process (MDP)—where an RL agent acts as a real-time controller for FFT-domain filters. Instead of static rules, the agent observes the signal’s state and picks actions to minimize errors directly tied to geolocation performance.

Here’s the breakdown:

State: A vector including normalized FFT power densities, recent TDoA residual error (in meters), and correlation entropy (a measure of peak sharpness).
Actions: Choose and tweak filters like low-pass cutoff frequency, notch center/bandwidth, or even “no-op” (do nothing).
Reward: Negative TDoA error minus λ times entropy. The λ weight lets you tune the balance between timing fidelity and spectral purity—I found λ=0.5 works best.
Learning: I used Deep Q-Network (DQN) with experience replay for stability. After training, the policy deploys online for adaptive filtering.

This keeps things interpretable (using familiar low-pass/notch primitives) while adding data-driven smarts. No end-to-end neural overhaul—just RL guiding the classics.

Key Results: Beating the Baselines

I tested on synthetic RF sequences: baseband-modulated signals with AWGN (-5 to 15 dB SNR) and optional jammers (5-10% of FFT bins). Over 50 Monte Carlo trials per setup, the RL policy trained in ~10^5 steps and was evaluated on unseen data.

TDoA Residuals: 28.6% average reduction across SNRs compared to static low-pass/notch baselines. In jammer scenarios, gains hit 45%!
Entropy and SNR: Lower entropy (sharper correlations) and improved SNR, especially at low SNRs.
Jammer vs. No-Jammer: Policy-driven beats static methods handily—e.g., residuals drop from 4.2m (low-pass with jammer) to 2.3m.

Check out these visuals from the paper:

Spectrogram Snapshots (Fig. 1): Raw signal shows jammer spikes; static notch helps but over-suppresses; policy-driven cleans it up while preserving structure.
Training Convergence (Fig. 2): Residuals and entropy drop steadily, policy stabilizes—proof of quick adaptation.
Performance vs. SNR (Fig. 3): Consistent outperformance, with biggest wins in noisy/jammed conditions.
Ablation on λ (Fig. 4): λ=0.5 minimizes residuals (1.8m) with balanced entropy (1.4)—too low prioritizes timing but blurs spectra; too high over-smooths.

Tables back this up:

Table I: Jammer performance edges out baselines by ~45% in residuals/entropy.
Table II: Uniform 28.6% reduction across SNRs.
Table III: Ablation confirms optimal λ.

Overall, it’s a win for adaptability without sacrificing efficiency.

Why This Matters and What’s Next

This isn’t just academic—think drone tracking, emergency response, or secure comms where jammers are a threat. By tying RL rewards to real physics (TDoA/entropy), we get deployable tech that’s interpretable and lightweight, perfect for edge devices.

Limitations? Simulations are synthetic; real hardware (like USRP radios) is next for validation. Compute overhead is low but needs quantifying, and multi-sensor extensions could enable team-based jamming defense.

Future plans: Hardware tests, multipath/fading models, and policy transfer across environments. If you’re in RF/AI, this could spark ideas for cognitive systems.

Grab the full paper here or hit me up at bgilbert2@com.edu. Let’s chat— what’s your take on RL in signal processing?

(Shoutout to foundational works like Haykin’s Adaptive Filter Theory and Clancy et al. on ML in cognitive radio for inspiring this hybrid approach.)

adaptive filtering approach: Topics by Science.gov

This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology …

science.gov

[PDF] The Sparse Fourier Transform: Theory & Practice – Haitham Hassanieh

The Fourier transform is one of the most fundamental tools for computing the frequency repre- sentation of signals. It plays a central role in signal …

haitham.ece.illinois.edu

Table of contents – IEEE Xplore

MAb-1: Learning and Optimization for Big Data. MAb-1.1: Flexible Selective Parallel Algorithms for Big Data Optimization .

ieeexplore.ieee.org

Sensors, Volume 25, Issue 17 (September-1 2025) – 392 articles