STEALTHsense: Secure Sensing & Robust Control

• STEALTHsense is a multifaceted concept combining advanced research in stealth, secure sensing, and control across diverse physical and cyber domains.
• It employs IRS-based electromagnetic techniques, optical detection, sensor micro-distortion, and reconfigurable sensor protocols to enhance low-power stealth and covert communications.
• These methodologies enable dynamic suppression of signals, robust attack detection, and resilient control in various applications from UAVs to smart human-computer interfaces.

STEALTHsense is a multifaceted term encompassing a set of recent research systems and methodologies addressing stealth, anti-detection, secure sensing, and robust control across domains such as electromagnetic stealth (via passive metasurfaces), optical/information-theoretic sensor detection, human-computer interaction, and adversarial resilience in cyber-physical systems. Implementations bearing the name STEALTHsense deploy a spectrum of techniques including intelligent reflecting surfaces (IRS), micro-distortion–based authentication, sensor bus-level reconfiguration, and contact-microphone signal processing. Below, these diverse technical paradigms are unified through detailed characterization of their physical, algorithmic, and security-theoretic properties, as established in peer-reviewed arXiv literature.

1. Intelligent Reflecting Surfaces for Electromagnetic Stealth

STEALTHsense architectures integrating IRS or reconfigurable metasurfaces fundamentally re-engineer electromagnetic scattering profiles of physical targets, surpassing traditional electromagnetic wave absorbing materials (EWAM) and static coatings. An IRS consists of a planar array of $N$ electronically tunable elements, each programmable with a complex reflection coefficient $\phi_n = \beta_n e^{j\theta_n}$, $\beta_n\in[0,1]$, $\theta_n\in[0,2\pi)$, enabling fine-grained manipulation of impinging wavefronts (Xiong et al., 2024, Zheng et al., 2024, Shao et al., 2023, Xu et al., 26 Jan 2025).

The core mechanism is the adaptive coordination of IRS phase–amplitude profiles to destructively interfere with EWAM-induced reflections at the monostatic radar, thereby minimizing the radar’s received signal-to-noise ratio (SNR). The governing received SNR at the radar is

$$\mathrm{SNR} = \frac{\|\alpha^2 a_R a_R^T\|_F^2}{\sigma^2} \left|a_I^T \Theta a_I + a_{E_w}^T \Gamma a_{E_w}\right|^2$$

where $\Theta = \operatorname{diag}(\boldsymbol{\theta})$, $\Gamma = \operatorname{diag}(\boldsymbol{\gamma})$, and the terms correspond to IRS and EWAM reflection paths, respectively (Xiong et al., 2024). By solving the convex quadratic minimization

$\min_{\|\theta_n\|\leq 1} |d^H\theta + c|^2$

for $d^H = a_I^T \operatorname{diag}(a_I)$ and $c = a_{E_w}^T\operatorname{diag}(a_{E_w})\gamma$, one derives a semi-closed-form solution for $\theta^*$ via the KKT approach.

Case and simulation analyses confirm deep nulling (SNR $\to$ 0) can be achieved with only $N_1\geq 20$ surface elements, and that phase-only control nearly matches the performance of full amplitude–phase reconfigurability. The IRS layer counter-phases residual EWAM reflections, enabling dynamic, low-power, angle- and frequency-adaptive suppression of backscatter (Xiong et al., 2024, Zheng et al., 2024).

2. Secure Sensing and Communication via IRS

STEALTHsense extends IRS utility to joint secure sensing and covert communications. In multi-radar or multi-user contexts, IRS phase patterns are optimized to focus energy toward authorized nodes (legitimate radar or legitimate receiver) and suppress it toward unauthorized observers (intruding radar, warden/Willie).

This is formalized as a constrained optimization of the IRS phase vector $\theta$ to maximize the received signal at an authorized node while simultaneously bounding the unauthorized receiver’s signal below a suppressive threshold $\gamma$:

$$\max_{\theta: |\theta_n|=1}\enspace \sum_{i} |q_i^H \theta|^2 \quad \text{s.t.}\quad \sum_{j} |h_j^H \theta|^2 \leq \gamma$$

The penalty dual decomposition (PDD) method efficiently solves the resulting non-convex quadratic fractional program with significant numerical gains: up to $+16$ dB enhancement at the legitimate receiver and nearly zero power at the unauthorized sensor, scalable to large $N$ (Shao et al., 2023). In 6G-intended ISAC (Integrated Sensing and Communication) networks, STEALTHsense leverages geometric closed-form projection algorithms to maximize adversarial AoA estimation error, maintaining strict SNR constraints for communication utility (Xu et al., 26 Jan 2025).

3. Alternative Sensing: Optical and Signal-Obscured Detection

In the near-field aerial domain, STEALTHsense can signify a mesh network of dynamically steered, highly collimated Gaussian laser beams. Such an architecture provides simultaneous detection, localization, classification, and tracking (SDCLT) of small or stealth aerial targets through direct obstruction-based metrics, rather than traditional backscatter (Khawaja et al., 2021).

Each laser "net" cell is defined by the intersection of two or more steerable Gaussian beams. Blockage events yield high-SNR detection, with sub-meter 3D localization (RMS error $<$1.2 m) at up to $450$ m range, and a zero-clutter false-alarm profile due to complete absence of background echoes. Rich geometrical feature sets are passed to machine-learning classifiers (Random Forest, SVM, Neural Networks), achieving $98$–$100\%$ test accuracy across 11 UAV/aircraft classes (Khawaja et al., 2021).

4. Human–Computer Interaction and Wearable STEALTHsense

STEALTHsense also denotes a non-verbal, hands-free interface for smart glasses. This implementation leverages tri-axial accelerometers embedded in nose pads to detect craniofacial vibrations from teeth clicks, interpreted via a temporal-broadcasting neural network inspired by BC-ResNet. The 1-second input segment is processed into log-Mel spectrograms and temporal derivatives, resulting in $41$-dimensional feature frames (Mohapatra et al., 2024).

The network utilizes a time-to-feature broadcast architecture, with layer/instance normalization, depthwise temporal encoding, and feature-wise deconvolution before global pooling and softmax classification. Lightweight deployment ($\sim$88 k parameters, 7.14M MACs/inference) yields $0.93$ balanced accuracy in participant-blind cross-validation, outperforming conventional ML and large deep models, robust even at SNR as low as $-23$ dB relative to click energy (Mohapatra et al., 2024). Deployment guidelines include INT8 quantization, DSP offloading, and accelerometer duty-cycling for power efficiency.

5. Defensive Paradigms: Micro-Distortion and Observer-Based Schemes

STEALTHsense is further a family of detection schemes for stealthy sensor attacks in industrial control systems (ICS) and networked UAVs.

Micro-distortion Authentication employs secret-driven per-sample perturbations $\Delta(t)$ strictly below deadband/minimal detectable amplitude, derived from a pre-shared key. The detection algorithm partitions inter-sample differences $\delta'(t)$ based on pairs of PRNG output bits, running a filtered-Δ-mean-difference test with low false positive/negative rates ($<$1% at 50–200 sample windows). This method enables rapid, passive impersonation detection on legacy devices without cryptographic modifications (Sourav et al., 2022).

Centralized and Decentralized Observer Techniques: In formation-controlled UAV networks, model-based (centralized and decentralized) Luenberger/Kalman observers leveraging graph topology switching are proven to detect zero-dynamics and covert attacks. Local observers on each UAV monitor neighbor states, triggering alarms upon observer residual excursions; central monitors corroborate via global state estimation. The architecture detects actuator/sensor covert attacks with finite delay, provided switching ensures observability of the manipulated subspace (Bahrami et al., 2022).

Resilience to Stealthy Attacks in Nonlinear Systems: A system with incrementally exponentially stable closed-loop and incrementally unstable open-loop dynamics is theoretically vulnerable to stealthy attacks (i.e., attacks that are $KL$-divergence indistinguishable from nominal data at the detector). Residual-based sequential probability ratio tests tuned to KL bounds are necessary to detect or limit the impact of such $\epsilon$-stealthy attacks (Khazraei et al., 2022).

6. Sensor Reconfiguration for Stealthy Attack or Defense

STEALTHsense also characterizes a class of sensor deprivation attacks (SDA) for UAV manipulation. Bus-level "write" injections (I²C, SPI) can reconfigure MEMS IMUs into "suspend" mode or low-sample-rate operation, causing the control loop’s Extended Kalman Filter to stall or process default/stale/erroneous data. The formal model describes an adversary's strategy as a sequence of SDA pulses $\gamma_k$ optimized (e.g., via TRPO) to maximize mission impact subject to detection constraints (e.g., $<$1s continuous SDA phases) (Erba et al., 2024). Experimental results indicate full controller stalls and UAV crashes with minimal detection by on-board or MMIO monitors.

Countermeasures include periodic IMU configuration register verification, inter-arrival time anomaly detection, cryptographic bus authentication, and redundant filter-level monitoring, achieving sub-second detection latency at low false positive rates (Erba et al., 2024).

7. Instrumentation: Stealth-Enhanced Terahertz Ellipsometry

Lastly, STEALTHsense applies to THz frequency-domain ellipsometry, where standing-wave suppression in high-coherence spectroscopic measurements is achieved by stealth-inspired geometric canted surfaces, resistive/porous foam coatings, and modulation of the BWO cathode voltage (broadening spectral linewidth and reducing coherent artifacts). The resulting instrument attains sub-millimeter coherence control, MHz-class spectral resolution, and high-precision dielectric extraction in complex sample environments (8T magnets, in-situ gas, cavity resonance) (Kühne et al., 2017).

Table: Core STEALTHsense Modalities

Paradigm	Core Technology	Primary Domain
IRS-based electromagnetic	Intelligent reflecting surface	Radar stealth, secure sensing, covert communication
Optical mesh detection	Steered laser arrays	Aerial SDCLT, counter-UAV
Sensor micro-distortion	Secret, amplitude-limited PRNG	ICS, sensor attack detection
Wearable HCI	Teeth-click, accelerometry	Smart glasses, hands-free interface
Sensor reconfiguration	Bus-level attack/monitoring	UAV destabilization and resilience
Stealth-enhanced ellipsometry	Geometry, absorbing coatings	Materials diagnostics, THz spectroscopy

STEALTHsense, as systematized in the literature, is thus not a single protocol but a suite of high-performance, security- and stealth-driven architectures spanning physical, cyber, and cognitive domains. All claims and techniques are grounded in the referenced arXiv publications.