The multidimensional parameter measurement of microwave signals, including temporal, spatial, and frequency, is essential for electronic warfare and radar systems. In this article, we present a photonic scheme for real-time microwave frequency and angle-of-arrival (AOA) measurement based on stimulated Brillouin scattering (SBS). In the proposed system, the unknown signal under test (SUT) received by adjacent antennas is injected into a dual-drive Mach–Zehnder modulator (DDMZM). Two branches of the SUT with phase difference interfere in the optical domain, converting phase difference into the power of optical sidebands. These optical sidebands are scanned by combining SBS with frequency-to-time mapping (FTTM) to achieve simultaneous measurement of the AOA and frequency. Consequently, the frequency and AOA of the SUT are mapped to the time interval and normalized amplitude of the output electrical pulses, respectively. Results show that the system can achieve the frequency measurement of multiple RF signals in the range of 5–15 GHz and AOA measurement in the range of −70° to 70°, with measurement errors of ±5 MHz and ±2°, respectively. Furthermore, the frequency measurement range can be flexibly adjusted by tuning the pump optical driving signals.

This paper presents Wi-Hand, a 3D hand construction system using WiFi. Our system leverages WiFi devices commonly available in smart home environments and is capable of operating under non-line-of-sight (NLoS) conditions, offering greater flexibility and ease of deployment compared to traditional computer vision and wearable-based approaches. In specific, our system leverages the spatial information of the reflected WiFi signals to mitigate the dynamic interference in indoor environments and extract the signal reflections of the target subject. We further utilize Doppler velocity information to distinguish WiFi signals reflected from hand motions from those of the human body, as they exhibit different movement speed characteristics. Moreover, our system leverages two-dimensional angle of arrival (2D AoA) information to represent the shape and deformations of the target hand for 3D hand mesh construction. In addition, we leverage the spatial-temporal coherence of the hand to handle the missing parts of the finger due to their weak and specular reflections. Finally, a deep learning model is utilized to digitize 2D AoA frames of the target hand into the 3D hand mesh. We conduct extensive experiments involving multiple participants across diverse environments. The results demonstrate that our system accurately reconstructs 3D hand meshes for free-form hand activities, achieving an average vertex error of 1.05cm. Additionally, we evaluate the performance of our system under various conditions, including different user positions, dynamic interference, hand shape diversity, randomly ordered gestures, and temporally discontinuous hand activities. These evaluations collectively demonstrate that Wi-Hand can effectively and robustly leverage WiFi signals to construct accurate 3D hand meshes.

Photonic-assisted radar for simultaneous detection of velocity, distance and angle of arrival of multiple targets

Split-beam echosounders are widely used in acoustical oceanography to measure the abundance and distribution of marine organisms and to study their movement and behavior in situ. Abundance estimates rely on target strength measurements, which depend on accurate angle of arrival (AoA) estimates to compensate for transducer directivity, and analyses of movement and behavior also depend on AoA to determine spatial positions. Recent years have seen the transition from narrowband to broadband systems, which offer improved acoustic detection and characterization of targets, yet the method for AoA estimation with split-beam echosounders has remained unchanged. The split-aperture correlator (SAC), originally developed for narrowband systems, remains the only available option despite inadequate assessment for broadband systems and fundamental restrictions to single-echo scenarios. In this work, we assess SAC's applicability to broadband systems, demonstrating its inaccuracy in estimating the AoAs of broadband single echoes from targets with highly frequency-dependent scattering responses, such as gas-bearing fish. An extended SAC is introduced to address this limitation. Furthermore, we introduce a broadband maximum likelihood estimator for estimating the AoAs and spectra of overlapping echoes. The estimator's efficacy and applicability to a broadband split-beam echosounder are demonstrated through controlled laboratory experiments.

The performance of photovoltaic (PV) systems is greatly influenced by the angle of arrival of sunlight and the geometric orientation of solar panels, especially in tropical regions with the potential for solar energy throughout the year. This study aims to evaluate the effect of tilt angle variation and tracking systems on energy output and performance indicators of standalone PV systems using PVsyst software. The simulation was conducted at the State University of Malang, Indonesia, by comparing four fixed-angle configurations (20°, 40°, 60°, and 80°) as well as a two-axis tracking system. The simulation results showed that the two-axis tracking system produced the highest normalized daily energy production of 6.8 kWh/kWp/day, with a performance ratio (PR) of 77.2% and a solar fraction (SF) of 97.1%, while a fixed configuration with an angle of 80° showed the lowest performance. These findings confirm the importance of selecting optimal panel orientation to maximize the efficiency of PV systems, as well as being the basis for the development of advanced research, such as field-based experiments, integration of adaptive MPPT algorithms, and economic feasibility studies in the application of PV systems in tropical and off-grid regions.

This paper presents a novel free-space optical Angle-of-Arrival (AOA) estimation method based on arrayed waveguide coherent mode decoding, aiming to surpass the inherent limitations of traditional AOA detection technologies, which face significant challenges in achieving miniaturization, low complexity, and high reliability. The method utilizes the AOA-related phase differences generated by the propagation and interference of incident light in an arrayed input waveguide, forming multi-beam interference fringes at the output end of the slab waveguide. This pattern is then sampled by an arrayed output waveguide to produce an intensity sequence, which is then fed into a trained CNN-Attention Regressor for AOA estimation. This study innovatively applies the method to decoding the spatial angular information of optical signals. Simulation results demonstrate the exceptional performance of our approach, achieving a Mean Absolute Error (MAE) of 0.0142° and a Root Mean Square Error (RMSE) of 0.0193° over a 40° field of view. This precision is significantly superior to conventional peak-linear calibration methods and other common neural network architectures, and exhibits remarkable robustness against simulated phase noise and manufacturing tolerances. This research demonstrates the powerful synergy between integrated photonics and deep learning, paving the way for a new class of highly integrated, robust, and high-performance on-chip optical sensors.

To address the issues of insufficient accuracy due to nonlinear error accumulation in traditional drone angle-of-arrival (AOA) positioning and the tendency of existing optimization algorithms to get stuck in local optima, this paper proposes a positioning error optimization method that integrates phased correction with an improved Starling optimization algorithm. By constructing a multi-source error propagation model, we analyze the error propagation characteristics of UAV position, attitude, and pod attitude. A phased optimization framework based on observation sequences is designed to suppress the nonlinear accumulation of errors. Subsequently, by integrating the improved Starling optimization algorithm with cubic chaotic mapping and a spiral search strategy, optimal allocation and compensation of error sources are achieved. Monte Carlo simulations demonstrate that the improved algorithm achieves a 73.29% reduction in positioning error distance compared to AOA positioning accuracy and a 58.12% improvement over the original Starling optimization algorithm. It significantly outperforms other comparison algorithms, proving this method effectively corrects nonlinear perturbations in electro-optical systems and provides a higher-precision solution for passive positioning of UAVs.

Correction: Investigating the Relationship Between Bolide Entry Angle and Apparent Direction of Infrasound Signal Arrivals

This paper presents a UAV-based communication system that integrates hybrid Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA) schemes, operating in the millimeter-wave (mm-Wave) frequency band and supported by Multiple-Input Multiple-Output (MIMO) technology. The proposed Hybrid OMA/NOMA-mm-Wave MIMO framework is designed to enhance overall system performance by delivering high-capacity wireless connectivity to ground users (GUs). UAVs act as aerial base stations (BSs), offering rapid and flexible communication services in diverse real-world scenarios, including natural disasters, areas lacking fixed infrastructure, and temporary coverage at large public events. To improve NOMA's performance, a Particle Swarm Optimization (PSO) algorithm is employed for optimizing power allocation (PA), ensuring fairness between near and far users. Furthermore, a user pairing mechanism integrated with optimized power allocation is introduced to enhance the UAV-BS performance in mm-Wave-NOMA scenarios. The channel model considers both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, incorporating angle of departure (AoD), angle of arrival (AoA), and Doppler effects. Simulation results demonstrate that NOMA outperforms OMA in specific scenarios, while OMA remains more effective in others. PSO-based power allocation significantly surpasses fixed PA schemes in NOMA systems.

Autonomous Delivery Vehicles (ADVs) are increasingly used for transporting goods in 5G network-enabled smart factories, with the compute-intensive localization module presenting a significant opportunity for optimization. We propose ACCESS-AV , an energy-efficient Vehicle-to-Infrastructure (V2I) localization framework that leverages existing 5G infrastructure in smart factory environments. By opportunistically accessing the periodically broadcast 5G Synchronization Signal Blocks (SSBs) for localization, ACCESS-AV obviates the need for dedicated Roadside Units (RSUs) or additional onboard sensors to achieve energy efficiency as well as cost reduction. We implement an Angle-of-Arrival (AoA)-based estimation method using the Multiple Signal Classification (MUSIC) algorithm, optimized for resource-constrained ADV platforms through an adaptive communication-computation strategy that dynamically balances energy consumption with localization accuracy based on environmental conditions such as Signal-to-Noise Ratio (SNR) and vehicle velocity. Experimental results demonstrate that ACCESS-AV achieves an average energy reduction of 43.09% compared to non-adaptive systems employing AoA algorithms such as vanilla MUSIC, ESPRIT, and Root-MUSIC. It maintains sub-30 cm localization accuracy while also delivering substantial reductions in infrastructure and operational costs, establishing its viability for sustainable smart factory environments.

Atmospheric turbulence causes fluctuations in the angle-of-arrival (AOA) of sound waves. These fluctuations adversely affect the performance of sensor arrays used for source detection, ranging, and recognition. This article examines, from a theoretical perspective, the variance of the AOA fluctuations measured with two microphones. The AOA variance is expressed in terms of the propagation range, transverse distance between two microphones, acoustic frequency, and effective spectrum of quasi-homogeneous and isotropic turbulence, with parameters dependent upon the height above the ground. The effective spectrum is modeled with the von Kármán and Kolmogorov spectral models. In the latter case, the results simplify significantly, and the variance depends on the path-averaged effective structure-function parameter, which characterizes the intensity of temperature and wind velocity fluctuations in the inertial subrange of turbulence. The standard deviation of the AOA fluctuations is studied numerically for typical meteorological regimes of the daytime atmospheric boundary layer. For the cases considered, the standard deviation varies from a fraction of degree to around 1°-2°, and increases with increasing friction velocity and surface heat flux.

Sound field reproduction with undistorted sound quality and precise spatial localization is desirable for automotive audio systems. However, the complexity of the automotive cabin acoustic environment often necessitates a trade-off between sound quality and spatial accuracy. To overcome this limitation, we propose Spatial Power Map Net, a learning-based sound field reproduction method that improves both sound quality and spatial localization in complex environments. We introduce a spatial power map constraint, which characterizes the angular energy distribution of the reproduced field using beamforming. This constraint guides energy toward the intended direction to enhance spatial localization, and is integrated into a multi-channel equalization framework to also improve sound quality under reverberant conditions. To address the resulting non-convexity, deep optimization that uses neural networks to solve optimization problems is employed for filter design. Both in situ objective and subjective evaluations confirm that our method enhances sound quality and improves spatial localization within the automotive cabin. Furthermore, we analyze the influence of different audio materials and the arrival angles of the virtual sound source in the reproduced sound field, investigating the potential underlying factors affecting these results.

For shallow underwater acoustic sources in deep-sea environments, the received signals in the direct wave zone exhibit distinct multipath arrival structures during large-depth reception, which can be exploited for passive source localization. This paper presents a method for estimating source depth and range through joint matching of arrival angles and time-delay differences between the direct wave and the first-order sea-surface reflected wave. The arrival angles are obtained from deep-sea particle velocity fields based on ray theory, while the time-delay differences are extracted from the autocorrelation function of the received signals. Simulation results show that both arrival angles and time-delay differences can be accurately estimated simultaneously at signal-to-noise ratios (SNRs) above 10 dB, allowing high-precision passive source localization with the proposed method. Under fixed source-receiver geometry, seabed topography and sea-surface roughness significantly alter acoustic propagation paths. Seabed variations affect paths of both direct and bottom-interacted waves, and sea-surface roughness specifically distorts the path of the first-order sea-surface reflected wave, thereby reducing the accuracy of time delay difference estimation in matched field processing. Validated using data from a 2020 deep-sea acoustic propagation experiment in the Western Pacific—where a single vector hydrophone captured signals from a towed source—the method effectively estimated the depth and range of shallow underwater acoustic sources within a 15 km range, with mean relative errors of 5.5% in depth and 6.2% in range. Compared to conventional methods that use only arrival angles or time-delay differences, this joint-matching strategy offers an efficient and robust solution for passive localization of underwater acoustic sources in the deep-sea direct wave zone.

A informação de ângulo de chegada (AOA, Angle of Arrival) em sensores RWR (Radar Warning Receiver) que utilizam o método de comparação de amplitude é suscetível a distintas fontes de erro que podem afetar a acurácia e a precisão deste parâmetro. A variação da razão sinal-ruído (SNR), a qual é relacionada diretamente à amplitude do sinal obtido pelo receptor, é um dos fatores que podem comprometer a determinação de AOA. Neste artigo, apresenta-se um experimento conduzido em laboratório para se avaliar o erro na determinação de AOA proveniente da variação da SNR. Realiza-se uma análise teórica, baseada em um modelo em que a SNR é função do ângulo de detecção da ameaça e da resposta do receptor, incluindo o padrão e a posição das antenas. A análise experimental valida os resultados teóricos obtidos pelo modelo considerado. Por fim, destaca-se que os testes realizados demonstram a possibilidade de se avaliar o processamento de AOA de sensores RWR dispostos em cadeia reduzida por meio de testes conduzidos, sendo uma alternativa para ensaios de campo ou em câmara anecoica.

This paper investigates the performance of optical intelligent reflecting surface (IRS)-enhanced free-space optical (FSO) communication systems over a Gamma-Gamma turbulence-induced fading channel with pointing errors, considering dynamic unmanned aerial vehicle (UAV)-assisted jamming scenarios. We propose a comprehensive channel model that integrates the cascaded impairments from atmospheric turbulence (AT), pointing errors (PE), and path loss for both legitimate IRS-reflected signals and UAV-induced jamming channels. What we believe to be a novel angle-of-arrival (AoA) fluctuation model is developed to characterize the spatial dynamics of a mobile jammer. The expressions for probability density functions (PDFs) of the end-to-end legitimate FSO channel and jamming FSO channel are provided. Furthermore, the closed-form expressions for outage probability (OP), average bit error rate (ABER), and ergodic capacity are derived, accommodating both heterodyne detection (HD) and intensity modulation/direct detection (IM/DD) techniques. The asymptotic expressions of the aforementioned performance metrics are also derived to obtain the diversity order of the considered system. Finally, a comprehensive analysis is conducted to investigate the impacts of key parameters such as the number of IRS reflecting elements (N), detection modes (IM/DD versus HD), and environmental conditions on the system performance. Theoretical models are validated through Monte Carlo (MC) simulations. Results indicate that multi-element IRS configurations can significantly enhance anti-jamming and anti-fading capabilities through spatial diversity gains. Increasing the number of IRS elements can achieve performance gain, with the ABER reduced by up to six orders of magnitude and ergodic capacity enhanced by a factor of 13. HD significantly outperforms IM/DD under jamming scenarios due to its coherent detection mechanism. Environmental impairments, such as fog attenuation, and geometric constraints, including receiver aperture size, can be effectively mitigated through IRS optimization.

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems.

In recent years, WiFi-based tracking has gained significant attention owing to its non-invasive, cost-effective, and ubiquitous coverage. These systems eliminate the need for wearable devices, making them highly suitable for applications in smart homes, health monitoring, and security surveillance. However, existing WiFi-based tracking systems face notable challenges, particularly in complex environments. Single-transceiver systems often rely on the estimation of Angle of Arrival (AoA) and Time of Flight (ToF), which is limited by the number of antennas and available bandwidth. Alternatively, multi-device systems are exploited to estimate the target's velocity via Doppler Frequency Shift (DFS). For the multidevice cooperative tracking systems, it is essential to evaluate the velocity estimation quality across devices and effectively leverage their complementarity to improve tracking performance. To this end, we propose AdaptTrack, an innovative human tracking system utilizing commercial WiFi devices. Specifically, we derive a quantization analysis of DFS estimation errors from Channel State Information (CSI) quotient. Furthermore, we design an adaptive device selection strategy that jointly considers the velocity estimation performance and the complementarity of WiFi devices to optimize tracking accuracy. In addition, we implement a prototype system based on commercial WiFi devices. These innovations enable AdaptTrack to achieve the high-precision tracking in complex scenarios. Extensive real-world experiments demonstrate the advantage of AdaptTrack in various environments, compared to the baselines. These results highlight its robustness, scalability, and potential as a practical solution for privacy-friendly human tracking in intelligent environments.

Abstract In the evolving landscape of modern electronic warfare, radar systems frequently encounter significant challenges due to adversarial blanket jamming, which severely undermines their operational capabilities. To counteract these threats, this paper introduces a novel approach for jammer localization and suppression for a distributed radar system. Our method integrates Angle of Arrival (AOA) and Received Signal Strength (RSS) information to achieve precise localization of jamming sources. By accurately pinpointing the source of jammer, the system can implement targeted countermeasures to mitigate its impact. Building upon this localization framework, we propose a joint array optimization strategy that optimizes the layout of the distributed radar system by holistically maximizing multi-domain localization accuracy and enhancing anti-jamming performance. This optimization is facilitated by leveraging the Multi-Objective Particle Swarm Optimization algorithm (MOPSO), which enables the system to adaptively configure its array layout for optimal performance. Extensive simulations validate the efficacy of our proposed method. The results demonstrate that our approach not only achieves superior localization accuracy but also significantly improves the overall performance of jamming suppression in a distributed radar system.

The passive localization of dual targets composed of a surface ship and a submerged source located nearby beneath the ship is an intriguing problem. This study develops a passive localization method based on multipath arrival angles for dual targets, with similar source levels in the deep-ocean direct arrival zone, using a horizontal line array. Compared to the classical minimum variance distortionless response method, the sparse Bayesian learning method is used to improve resolution for multipath arrival angles under coherent signal conditions, enhancing both the effective range and localization accuracy. The effectiveness of the proposed method has been validated through simulation and experiment.

We present a method for measuring the angle of arrival of 37-GHz radio-frequency (rf) radiation by mapping the standing waves generated in a rectangular glass vapor cell. These standing waves have regular and well-defined structure from which we can infer the angle and sign of the wavevector of the rf field. We map the field using spatially resolved light sheet spectroscopy of Rydberg states of rubidium atoms in the cell. Unlike traditional phased arrays, this detection scheme is compact, has an active area of nearly 4π steradians, and is sensitive to all rf polarizations. For in-plane measurements (ϕ=0), we demonstrate quantitative angle-of-arrival measurements with an uncertainty of the order of 1∘ in an 11-s measurement, and for out-of-plane measurements (arbitrary θ,ϕ), we demonstrate angle-of-arrival detection with uncertainty of the order of several degrees.