Search Results (82)

Photoacoustic (PA) velocimetry offers a promising solution to the limitations of conventional techniques for measuring blood flow velocity. Given its moderate penetration depth and high spatial resolution, PA imaging is considered suitable for measuring low-velocity blood flow in capillaries located at moderate depths. High-resolution measurements based on PA signals from individual blood cells can be achieved using a high-frequency transducer. However, high-frequency signals attenuate rapidly within biological tissue, restricting the measurable depth. Consequently, low-frequency transducers are required for deeper measurements. To date, PA flow velocimetry employing low-frequency transducers remains insufficiently explored. In this study, we investigated the effect of the concentration of particles that mimic blood cells within vessels under low-concentration conditions. The performance of flow velocity measurement was evaluated using an ultrasonic transducer (UST) with a center frequency of 10 MHz. The volume fraction of particles in the solution was systematically varied, and the spatially averaged flow velocity was assessed using two different distinct analysis methods. One method employed a time-shift approach based on cross-correlation analysis. Flow velocity was estimated from PA signal redpairs generated by particles dispersed in the fluid, using consecutive pulsed laser irradiations at fixed time intervals. The other method employed a pulsed Doppler method in the frequency domain, widely applied in ultrasound Doppler measurements. In this method, flow velocity redwas estimated from the Doppler-shifted frequency between the transmitted and received signals of the UST. For the initial analysis, numerical simulations were performed, followed by experiments based on displacement measurements equivalent to velocity measurements. The target was a capillary tube filled with an aqueous solution containing particles at different concentration levels. The time–domain method tended to underestimate flow velocity as particle concentration increased, whereas the pulsed Doppler method yielded estimates consistent with theoretical values, demonstrating its potential for measurements at high concentrations. Full article

Satellite tracking has dramatically improved research on wide-ranging large marine vertebrates such as sea turtles. Traditionally, sea turtle tracking has relied on Argos-based satellite telemetry tags, which estimate location via Doppler shift and can also transmit sensor data. GPS-equipped Argos satellite tags represented a significant evolution, offering higher location accuracy. More recently, GPS-equipped satellite tags transmitting via the Iridium satellite network have become available for sea turtle tracking, and this study aims to assess whether they offer additional advantages. The performance of three satellite tag types—Argos-only, Argos-GPS, and Iridium-GPS (Iridium)—was assessed using data on 116,074 positions from 48 sea turtles representing five species and multiple ocean basins. Performance was evaluated using three indicators: the proportion of days with location data, the duration of gaps between locations, and the number of positions per day. Bayesian generalized linear mixed models assessed the effect of satellite tag type, technical settings, species, and activity (migration, foraging, internesting). Results indicate that Iridium satellite tags generally perform similarly to both Argos-based satellite tags, but performance improves significantly when programmed with high-frequency GPS acquisition (>24 positions/day), a result made possible by their tenfold higher transmission capacity compared to Argos. This capacity also enables transmission of more sensor data. Performance, however, varied by species and activity. These findings highlight the potential of Iridium tags to enhance fine-scale movement studies by improving the spatial and temporal resolution of sea turtle tracking, with important implications for ecological research and conservation planning. Full article

The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation accuracy and susceptibility to azimuth ambiguity, hindering accurate measurements. To address these limitations, this paper proposes a method for high-resolution radial current velocity estimation. This approach employs Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. This method achieves better Doppler frequency shift estimation accuracy than ACCC and effectively mitigates the azimuth ambiguity, substantially enhancing the precision of radial ocean surface velocity estimation. The algorithm was validated using raw Sentinel-1 Strip-map mode real data and HYCOM data acquired over the Seychelles Islands on 23 April 2023, and the central Indian Ocean (south of the equator) on 20 May 2023. Compared with the Sentinel-1 Level 2 ocean Surface Radial Velocity (RVL) product, the method demonstrates the improvements in both spatial resolution and retrieval accuracy. Specifically, the quantitative comparison with HYCOM data showed a reduction in Root Mean Square Error (RMSE) of up to 34.3% and an improvement in Mean Absolute Error (MAE) of up to 32.1%. Moreover, its ability to suppress the azimuth Doppler ambiguity is demonstrated in the real-data experiment. Full article

This paper presents a novel continuous-wave Doppler RADAR system for real-time speed measurement of moving objects, implemented using software-defined radio (SDR). Unlike traditional high-cost solutions typically found in research centers or specialized laboratories, this prototype offers a low-cost, compact, and easily deployable platform that lowers the entry barrier for experimentation and research. Operating within the 70 MHz–6 GHz range, SDR enables highly flexible signal processing; in this implementation, a 5.5 GHz carrier is selected to improve the detection precision by exploiting its reduced bandwidth for more accurate observation of frequency shifts. The carrier is modulated with a 2 kHz signal, and Doppler frequency deviations induced by object motion are processed to calculate velocity. Using a Welch spectral estimator, the system effectively reduces noise and extracts the Doppler frequency with high reliability. The prototype achieves speed measurements up to 196.36 km/h with approximately 2% error in the 0–100 km/h range, confirming its suitability for road traffic monitoring. A key innovation of this work is its single-antenna cross-polarized configuration, which simplifies hardware requirements while maintaining measurement accuracy. Furthermore, the system’s portability and open-access design make it ideal for in-vehicle applications, enabling direct deployment for automotive testing, driver-assistance research, and educational demonstrations. All design files and implementation details are openly shared, eliminating patent restrictions and encouraging adoption in low-resource academic and research environments. Full article

Effective Cardiopulmonary Resuscitation (CPR) requires precise chest compression depth, but current out-of-hospital monitoring technologies face limitations. This study introduces a method using frequency-modulated continuous-wave (FMCW) radar to remotely and accurately monitor chest compressions. FMCW radar captures range, Doppler, and angular data, and we utilize micro-Doppler signatures for detailed motion analysis. By integrating Doppler shifts over time, chest displacement is estimated. We compare a regression model based on maximum Doppler frequency with deep convolutional neural networks (DCNNs) trained on spectrograms generated via short-time Fourier transform (STFT) and the Wigner–Ville distribution (WVD). The regression model achieved a root mean square error (RMSE) of 0.535 cm. The STFT-based DCNN improved accuracy with an RMSE of 0.505 cm, while the WVD-based DCNN achieved the best performance with an RMSE of 0.447 cm, representing an 11.5% improvement over the STFT-based DCNN. These findings highlight the potential of combining FMCW radar and deep learning to provide accurate, real-time chest compression depth measurement during CPR, supporting the development of advanced, non-contact monitoring systems for emergency medical response. Full article

An iterative fractional Doppler shift and channel joint estimation algorithm is proposed for orthogonal time frequency space (OTFS) satellite communication systems. In the algorithm, we search the strongest path and estimate its fractional Doppler offset, and compensate the Doppler shift to the nearest integer to estimate the coefficient of the path. Then signal of the path and its inter-Doppler interference are reconstructed and canceled from the received data with these two estimated parameters. The estimation and cancel process are iteratively conducted until the strongest path in the remained paths is less than the predetermined threshold. The channel information can be reconstructed by the estimated parameters of the paths. The normalized mean squared error (NMSE) of the proposed channel estimation algorithm is less than 1/5 of the available algorithms at a high signal-to-noise ratio (SNR) region, and its BER has about 4dB SNR gain compared with those of the available algorithms when the bit error rate (BER) is ${10}^{-3}$. Full article

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

With the advancement of 5G-Advanced Non-Terrestrial Network (5G-A NTN) mobile communication technologies, direct satellite connectivity for mobile devices has been increasingly adopted. In the highly dynamic environment of low-Earth-orbit (LEO) satellite communications, the synchronization of satellite–ground signals remains a critical challenge. In this study, a Doppler frequency-shift estimation method applicable to high-mobility LEO scenarios is proposed, without reliance on the Global Navigation Satellite System (GNSS). Rapid access to satellite systems by mobile devices is enabled without the need for additional time–frequency synchronization infrastructure. The generation mechanism of satellite–ground Doppler frequency shifts is analyzed, and a relationship between satellite velocity and beam-pointing direction is established. Based on this relationship, a Doppler frequency-shift estimation method, referred to as DFS-BP (Doppler frequency-shift estimation using beam pointing), is developed. The effects of Earth’s latitude and satellite orbital inclination are systematically investigated and optimized. Through simulation, the estimation performance under varying minimum satellite elevation angles and terminal geographic locations is evaluated. The algorithm may provide a novel solution for Doppler frequency-shift compensation in Non-Terrestrial Networks (NTNs). Full article

The localization of radio emitters is a fundamental task in reconnaissance systems, and it has become increasingly important with the evolution of mobile networks. The signal Doppler frequency (SDF) method, developed for dual-use applications, leverages Doppler frequency shifts (DFSs) in received signals to estimate the positions of radio transmitters. This paper proposes enhancements to the SDF method through advanced signal processing techniques, including dedicated filtering and a novel two-level overlapping approach, which significantly improve localization accuracy. The overlapping technique increases the number of DFS estimations per time unit by analyzing overlapping segments at both the signal sample level and within the DFS vector. Simulation studies using various filter types and overlapping parameters were conducted to evaluate the effectiveness of these enhancements in a dynamic scenario involving multiple stationary transmitters and a single moving receiver. The results demonstrate that the proposed approach minimizes localization errors. The application of low-pass filtering at the DFS vector level improves localization accuracy. In the study, three types of filters for different cutoff frequencies are considered. Each of the analyzed filters with an appropriately selected cutoff frequency provides a comparable reduction in localization error at the level of about 30%. The use of overlapping at the signal sample level with a factor of 10% allows for more than a twofold decrease in localization errors, while overlapping at the DFS vector provides an increase in the refresh rate of the position of localized objects. Comparative analysis with direct position determination techniques additionally showed high effectiveness of the SDF method, especially using data filtration and overlapping. The simulation studies carried out are of significant importance for the selection of the operating parameters of real localization sensors in unmanned aerial vehicle (UAV) equipment. Full article

With the advancement of artificial intelligence, the inference capabilities of Autonomous Underwater Vehicles (AUVs) have significantly improved, leading to growing interest in AUV applications. To ensure reliable operations, the field of underwater communications demands robust schemes that account for AUV mobility and enable the formation of underwater cellular networks. Conventional approaches using Linear Frequency Modulation (LFM) and Zadoff–Chu sequence (ZCS) sequences for frame detection and Cell ID (CID) assignment degrade substantially under severe Doppler conditions. In particular, AUVs experience pronounced Doppler shifts due to their mobility in underwater channels. In this study, we propose a methodology in which distinct Superimposed Adjusted-HFM (SA-HFM) signals are assigned to multiple buoys, allowing AUVs to jointly perform frame detection, CID assignment, and Doppler estimation in challenging underwater environments. To validate the proposed scheme, an ocean experiment was conducted in the East Sea of the Republic of Korea. The results demonstrate that the SA-HFM-based signals successfully achieved frame detection, CID assignment, and Doppler estimation at distances ranging from 500 m to approximately 2 km, even when the AUV moved at speeds of 1.02–1.54 m/s. The experimental results indicate that the proposed approach can offer robust underwater communication and facilitate the deployment of underwater cellular networks for mobile AUV operations. Full article

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures. Full article

Inverse synthetic aperture ladar (ISAL) systems combine laser coherent detection technology with inverse synthetic aperture imaging methods, offering advantages such as compact size, long detection range, and high resolution. The traditional optical delay line technique is widely used in frequency-modulated continuous wave (FMCW) ISAL imaging systems, but its flexibility is limited, posing challenges for high-precision signal processing. Additionally, frequency modulation errors, atmospheric disturbances, and other errors inevitably affect image quality. Therefore, this paper proposes a signal processing method based on digital delay for FMCW ISAL, aiming to achieve the high-resolution imaging of targets across several kilometers. Firstly, the paper introduces the FMCW ISAL system. By introducing digital delay technology, it enables the flexible and real-time adjustment of reference signal delay. Next, to address the frequency offset issue caused by the introduction of digital delay technology, a preprocessing method for unified frequency shift correction is proposed to ensure signal consistency. Then, a set of internal calibration signal datasets is generated based on digital delay technology. Following this, a frequency modulation error iteration estimation method based on gradient descent is introduced. Without the need for target echo signals, the method accurately estimates the frequency modulation phase errors of both the transmitted and reference signals using only the internal calibration signals. Finally, this paper effectively decomposes the motion of the target, derives the echo model for the FMCW ISAL system, and constructs compensation functions to eliminate the intra-pulse Doppler shift and the residual video phase (RVP). Additionally, the Phase Gradient Autofocus (PGA) algorithm is used after two-dimensional imaging to eliminate the impact of atmospheric disturbances. We conducted two sets of experiments on point targets and surface targets to verify the effectiveness of error compensation in improving imaging quality. The results show that the two-dimensional resolution of point targets was optimized to 3 cm (range) × 0.6 cm (azimuth), while the resolution and entropy of the surface targets were both improved by 0.1. These results demonstrate that the proposed method effectively enhances target imaging quality and provides a new technical approach for high-precision signal processing in FMCW ISAL imaging. Full article

With the development of 5G new radio (NR) applications in high-speed scenarios, such as 5G non-terrestrial networks (NTN), the Doppler shift in the systems is significant. In this paper, an estimation scheme for frequency offset with large range in orthogonal frequency division multiplexing (OFDM) systems is proposed. The proposed scheme firstly takes advantage of the $2\pi$-periodicity of the phase offset between two pilot OFDM symbols to estimate a set of candidate frequency offsets. It then uses the autocorrelation of the pilot sequence to determine the final estimated frequency offset. This method allows for a large estimation range, independent of the symbol gap between the two pilot OFDM symbols. Moreover, the low-complexity implementation of the scheme is provided. The simulation results based on 5G NR physical uplink shared channel (PUSCH) show the effectiveness of the proposed scheme in both single-user and multi-user scenarios, where various Doppler shifts and numbers of configured resource blocks (RB) are considered. The simulation results also show that the proposed frequency-domain method outperforms the conventional time-domain method with additional computation complexity. Full article

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position. Full article