{"id":3480,"date":"2025-09-16T19:53:40","date_gmt":"2025-09-16T19:53:40","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3480"},"modified":"2025-09-16T19:54:47","modified_gmt":"2025-09-16T19:54:47","slug":"policy-driven-rf-denoising-for-adaptivegeolocation-a-reinforcement-learning-approachto-fft-domain-filtering","status":"publish","type":"page","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3480","title":{"rendered":"Policy-Driven RF Denoising for Adaptive Geolocation: A Reinforcement Learning Approachto FFT-Domain Filtering"},"content":{"rendered":"\n\n\n

Policy-Driven RF Denoising for Adaptive
Geolocation: A Reinforcement Learning Approach
to FFT-Domain Filtering
Benjamin J. Gilbert\u2217
\u2217College of the Mainland Robotic Process Automation
ORCID: 0009-0006-2298-6538
Email: bgilbert2@com.edu
Abstract\u2014We propose a policy-driven RF denoising framework
in which reinforcement learning (RL) adaptively controls FFTdomain filters to minimize timing and correlation errors in
passive geolocation. Unlike static low-pass or notch filters, the
policy selects denoising actions in real time based on residual
time-difference-of-arrival (TDoA) error and correlation entropy,
providing a feedback loop that directly targets physical error
metrics. Experiments on synthetic RF sequences with and without
narrowband jammers demonstrate that the learned policies
converge rapidly and consistently outperform fixed filtering
strategies, yielding 28.6% reduction in TDoA residuals and 45%
improvement in jammer conditions across SNR sweeps. Ablation
on the entropy-weight \u03bb confirms its role in balancing timing
fidelity with spectral purity, with optimal performance at \u03bb = 0.5.
Before\/after spectrograms illustrate the qualitative suppression of
jammer tones and the restoration of signal structure. By framing
reinforcement learning as a controller for adaptive denoising, this
work extends classical signal processing approaches with datadriven adaptability, while retaining interpretability, deployability,
and tight alignment with RF timing accuracy.
Index Terms\u2014RF signal processing, adaptive denoising, reinforcement learning, time-difference-of-arrival, geolocation, FFT
filtering, jammer suppression
I. INTRODUCTION
Radio frequency (RF) geolocation systems rely on precise
timing measurements to estimate target positions from multiple sensor observations. Time-difference-of-arrival (TDoA)
techniques, in particular, require clean correlation peaks between received signals to achieve sub-meter accuracy. However, real-world RF environments present significant challenges: additive noise degrades signal-to-noise ratio (SNR),
while narrowband jammers can corrupt specific frequency
bands and distort correlation functions.
Traditional approaches to RF denoising employ static filtering strategies\u2014fixed low-pass filters for bandwidth control or manually-tuned notch filters for interference suppression [1]. While computationally efficient, these methods lack
adaptability to time-varying interference patterns and may
inadvertently remove signal components critical for timing
accuracy. Recent advances in machine learning suggest that
adaptive filtering, guided by direct feedback from downstream
tasks, could significantly improve performance in dynamic RF
environments [2].
This paper introduces a policy-driven RF denoising framework that employs reinforcement learning (RL) to adaptively
control FFT-domain filters. The key insight is to treat denoising as a sequential decision problem, where an RL agent
observes spectral features and selects filtering actions to minimize both TDoA residual error and correlation entropy. Unlike
end-to-end neural approaches that lack interpretability, our
framework retains classical signal processing primitives (lowpass and notch filters) while learning their optimal application
through data-driven policies.
Our contributions are threefold:
1) A novel formulation of adaptive RF denoising as a
reinforcement learning control problem, with rewards
directly tied to geolocation accuracy metrics.
2) Experimental validation showing 28.6% reduction in
TDoA residuals and 45% improvement in jammer conditions compared to static filtering baselines.
3) Ablation studies demonstrating the role of entropy
weighting in balancing timing fidelity with spectral
purity, with optimal performance at \u03bb = 0.5.
The remainder of this paper is organized as follows: Section II reviews related work in adaptive filtering and RL for
signal processing. Section III presents the policy-driven denoising framework and RL formulation. Section IV describes
experimental methodology and results. Section V concludes
with implications for RF system design.
II. RELATED WORK
Classical approaches to RF denoising and interference mitigation have relied on well-established adaptive filtering techniques. The Wiener filter provides an optimal linear estimator
under Gaussian assumptions, while recursive filters such as the
Kalman filter extend this framework to time-varying systems
with state-space models [3]. Adaptive algorithms such as
LMS and RLS [4], [1] further enable online adaptation to
changing signal statistics, and have long been applied to noise
suppression and channel equalization in communications.
In the RF domain, static filtering remains common, including fixed low-pass filters for bandwidth limitation and
manually-tuned notch filters for jammer suppression. While
computationally efficient, these methods lack adaptability to
non-stationary environments, often discarding information essential for timing accuracy in TDoA-based systems. Extensions such as adaptive notch filters or spectrum-sensing-driven
dynamic filters partially address this limitation but typically
rely on heuristics rather than end-to-end performance metrics.
More recently, machine learning has been explored as a
driver of adaptive signal processing. Reinforcement learning, in particular, has been applied to problems such as
dynamic spectrum access, power control, and cognitive radio adaptation [2], [5]. Within denoising contexts, RL has
been used to select filter parameters [6] or optimize channel estimation pipelines [7], demonstrating the potential of
data-driven control for classical primitives. Unlike end-to-end
neural denoisers, which often lack interpretability and impose
heavy computational costs, our framework leverages RL as
a lightweight controller for established FFT-domain filters,
directly optimizing physical error metrics (TDoA residuals and
correlation entropy). This positions our work at the intersection
of classical adaptive filtering and modern RL-driven control,
contributing a signal-processing-centric perspective to RF denoising.
III. METHODOLOGY
We frame adaptive RF denoising as a Markov Decision
Process (MDP), where an agent learns to select and parameterize FFT-domain filters in order to minimize geolocation
error metrics under noisy and adversarial conditions.
A. State Representation
At each time step t, the agent observes a feature vector
st =<\/p>\n\n\n\n

pFFT, eTDoA
t
, Ht<\/p>\n\n\n\n

,
where pFFT represents normalized FFT power spectral densities over N bins, e
TDoA
t
is the most recent time-difference-ofarrival (TDoA) residual error, and Ht is the correlation entropy
of the cross-correlation function. This combination ensures the
state reflects both spectral content and timing reliability.
B. Action Space
The agent chooses among filter primitives and their parameters:<\/p>\n\n\n\n