Range Of Estimates Research Articles

Enhanced FFT–Root–MUSIC Algorithm Based on Signal Reconstruction via CEEMD–SVD for Joint Range and Velocity Estimation for FMCW Radar

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range–velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range–velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments.

Global patterns of species diversity and distribution in the biomedically and biotechnologically important fungal genus Aspergillus.

Aspergillus fungi are key producers of pharmaceuticals, enzymes, and food products and exhibit diverse lifestyles, ranging from saprophytes to opportunistic pathogens. To improve understanding of Aspergillus species diversity, identify key environmental factors influencing their geographic distributions, and estimate the impact of future climate change, we trained a random forest machine learning classifier on 30,542 terrestrial occurrence records for 176 species (~40% of known species in the genus) and 96 environmental variables. We found that regions with high species diversity are concentrated in temperate forests, which suggests that areas with mild seasonal variation may serve as diversity hotspots. Species range estimates revealed extensive variability, both within and across taxonomic sections; while some species are cosmopolitan, others have more restricted ranges. Furthermore, range overlap between species is generally low. The top predictors of mean species richness were the index of cumulative human impact and five bioclimatic factors, such as temperature and temperate vs non-temperate ecoregions. Our future climate analyses revealed considerable variation in species range estimates in response to changing climates; some species ranges are predicted to expand (e.g., the food spoilage and mycotoxin-producing Aspergillus versicolor), and others are predicted to contract or remain stable. Notably, the predicted range of the major pathogen Aspergillus fumigatus was predicted to decrease in response to climate change, whereas the range of the major pathogen Aspergillus flavus was predicted to increase and gradually decrease. Our findings reveal how both natural and human factors influence Aspergillus species ranges and highlight their ecological diversity, including the diversity of their responses to changing climates, which is of relevance to pathogen and mycotoxin risk assessment.

Range-Parameterized Range and Velocity Constrained State Estimation

Range-Parameterized Range and Velocity Constrained State Estimation

Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS

Unmanned aerial vehicle (UAV) remote sensing has found extensive applications in various fields due to its ability to quickly provide remote sensing imagery, and the rapid, even automated, geometric registration of these images is an important component of their time efficiency. While current geometric registration methods based on image matching are well developed, there is still room for improvement in terms of time efficiency due to the presence of the following factors: (1) difficulty in accessing historical reference images and (2) inconsistencies in data sources, scales, and orientations between UAV imagery and reference images, which leads to unreliable matching. To further improve the time efficiency of UAV remote sensing, this study proposes a fully automatic geometric registration framework. The workflow features the following aspects: (1) automatic reference image acquisition by using online map services; (2) automatic ground range and resolution estimation using positional and orientation system (POS) data; (3) automatic orientation alignment using POS data. Experimental validation demonstrates that the proposed framework is able to carry out the fully automatic geometric registration of UAV imagery, thus improving the time efficiency of UAV remote sensing.

Multi-scale feature fusion based DOA and range estimation for near-field sources

Multi-scale feature fusion based DOA and range estimation for near-field sources

Driving range estimation for electric bus based on atomic orbital search and back propagation neural network

AbstractAs urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiety is one of the main factors limiting its popularization. To solve this problem, an accurate estimation method for the driving range of electric buses based on atomic orbital search (AOS) algorithm and back propagation neural network (BPNN) was used, in which a long‐term bus operation dataset under different driving conditions is utilized to train BPNN, and then weight and bias are taken as the first generation provided for AOS approach to find a more appropriate parameter combination. Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet.

Full 2D optimized window coefficients for improved range velocity estimation in 5G joint communication and sensing

Abstract This work presents an approach for optimization of window coefficients for 5G user equipment side sensing, using orthogonal frequency division multiplexing radar-based range and velocity estimation, based on the sounding reference signal (SRS) from the 5G New Radio (NR) standard. The signal configuration and the corresponding waveform are generated in compliance with the 3rd Generation Partnership Project (3GPP) standard for 5G. The limitations of conventional signal processing for resources available for sensing with the SRS are highlighted. The proposed approach, which optimizes the window coefficients to improve the sensing capabilities, is implemented through two methods. The first method employs a decoupled optimization strategy for range and velocity, showing high computational efficiency. Our results demonstrate that this method significantly improves the peak sidelobe level (PSL) of the velocity profile by over $\mathrm{15}\,\mathrm{dB}$ , although it does not address the issue of diagonally located sidelobes, which occur due to non-uniform resource distribution. The second method adopts a comprehensive full 2D optimization technique. While it requires more computational resources and does not improve the PSL beyond the first method’s achievements, it mitigates the diagonally located sidelobes issue. The level of these have been improved by more than $\mathrm{3}\,\mathrm{dB}$ .

Examining Whole Blood, Total and Free Plasma Tacrolimus in Elderly Kidney Transplant Recipients.

Therapeutic monitoring is routinely performed to ensure tacrolimus whole-blood concentrations fall within a predefined target. Despite this, patients still experience inefficacy and toxicity that could be related to variability in free (unbound) tacrolimus exposure. Therefore, the aim of this study was to compare tacrolimus-free plasma (Cu), total plasma (Cp), and whole-blood (Cwb) concentrations in adult kidney transplant recipients and to characterize tacrolimus disposition across different matrices. Twelve-hour concentration-time profiling was performed in 15 recipients, allowing simultaneous measurement of Cu, Cp, and Cwb. Pharmacokinetic parameters were estimated using noncompartmental analysis. The relationship between Cwb and Cp were examined using a capacity-limited binding model, incorporating the hematocrit fraction (fHCT) to estimate maximum binding concentration (Bmax) and dissociation constant (Kd). The relationship between Cp and Cu was evaluated using a linear binding model to estimate the nonspecific binding parameter (Nplasma). Nonlinear regression analysis was used to obtain estimates of Bmax, Kd, and Nplasma. A total of 195 paired Cwb, Cp, and Cu values were collected. The median ratios of Cwb:Cp, Cp:Cu, and Cwb:Cu were 9:1, 20:1, and 138:1, respectively. Variability in free plasma exposure was large; free trough values ranged from 8 to 51 ng/L and free area-under-the-concentration-time-curve values ranged from 424 to 7160 ng·h/L. Median (range) estimates of Bmax, Kd, and Nplasma were 90.4 µg/L (22.4-752.5 µg/L), 2.36 µg/L (0-69.2 µg/L), and 0.05 (0.035-0.085), respectively. The interindividual variability (CV%) in binding parameters was considerable (Bmax 117.2%; Nplasma 32.5%). Large variability was observed in tacrolimus-free plasma exposure and binding parameters. Future research to characterize the relationship between tacrolimus Cu and patient outcomes may be of benefit.

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Accurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area

An High Efficiency Angle and Range Estimation Method for Three-Dimensional Near-Field Sources Based on Higher-Order Cumulants

An High Efficiency Angle and Range Estimation Method for Three-Dimensional Near-Field Sources Based on Higher-Order Cumulants

Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation.

We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal-potentially unattainable-classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.

Source Range Estimation Using Linear Frequency-Difference Matched Field Processing in a Shallow Water Waveguide

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To suppress the sidelobes and improve the method’s robustness, a linear frequency-difference matched field processing (LFDMFP) method for estimating the source range is proposed. A two-neighbor-frequency high-order cross-spectrum between the measurement and the replica of each hydrophone of the vertical line array is first computed. The cost function can then be derived from the dual summation or double integral of the high-order cross-spectrum with respect to the depth of the hydrophones and the candidate sources of the replicas, where the range that corresponds to the minimum is the optimal estimation. Because of the larger modal interference distances, LFDMFP can efficiently provide only one optimal range within the same range search interval rather than some conventional matched field processing. The efficiency of the presented method was verified using simulations and experiments. The LFDMFP unambiguously estimated the source range in two experimental datasets with average relative errors of 2.2 and 1.9%.

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics.

Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Context Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas. Aims The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats. Methods We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods. Key results We found that the α-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The α-hull used a single default parameter and required no subjective input, making it a more objective, superior method. Conclusions We recommend that the α-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs. Implications This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped.

Factors shaping home ranges of Eurasian lynx (Lynx lynx) in the Western Carpathians

Understanding how large carnivores utilize space is crucial for management planning in human-dominated landscape and enhances the accuracy of population size estimates. However, Eurasian lynx display a large inter-population variation in the size of home ranges across their European range which makes extrapolation to broader areas of a species distribution problematic. This study evaluates variations in home range size for 35 Eurasian lynx in the Western Carpathians during 2011–2022 based on GPS telemetry and explains how intrinsic and environmental factors shape lynx spatial behaviour when facing anthropogenic pressure. The average annual home range size of lynx ranged from 283 (± 42 SE) to 360 (± 60 SE) km2 for males and from 148 (± 50 SE) to 190 (± 70 SE) km2 for females, depending on home range estimator (95% MCP, KDE and AKDE). Females with kittens had smaller annual and summer home ranges compared to non-reproducing females and subadults had smaller home ranges compared to adults. Lynx home range size was explained by availability of roe deer, except for summer, when alternative prey was likely available. We also found clear evidence of human-induced changes in lynx home range size, in particular, forest cover significantly decreased the home range size of male lynx during summer while road density led to an expansion of both annual and summer lynx home ranges. Lynx exhibited consistent fidelity to their home ranges throughout consecutive seasons, showing no seasonal variations. Strong territoriality was observed among competing males maintaining relatively low home range overlaps and considerable distances between centres of activity. The most pronounced tendency for association was observed between males and females, maintaining relatively close proximity year-round. The insights into lynx spatial requirements provided by our study will greatly enhance the accuracy of population size estimates and effectiveness of mitigation measures across the Western Carpathians.

Phylogenetic analyses show the Select Agent Coniothyrium glycines represents a single species that has significant morphological and genetic variation

ABSTRACT Soybean red leaf blotch (RLB), caused by the fungus Coniothyrium glycines, represents a foliar disease of soybean that is thus far restricted to Africa. The fungus is listed as a Select Agent by the Federal Select Agent Program because it could pose a severe threat to plant health were it to establish in the United States. Previous work uncovered tremendous molecular diversity at the internal transcribed spacer region, suggesting that there may be multiple species causing RLB. To determine whether multiple species cause RLB, we reconstructed the phylogeny of C. glycines and taxonomic allies using sequence data from four genes. We included 33 C. glycines isolates collected from six African countries and determined that all isolates form a well-supported, monophyletic lineage. Within this lineage there are at least six well-supported clades that largely correspond to geography, with one clade exclusively composed of isolates from Ethiopia, another exclusively composed of isolates from Uganda, and four composed of isolates from southern Africa. However, we did not detect any concordance for these clades between the four genes, indicating that all isolates included in this analysis are representative of a single species. Isolates in the Ethiopia clade are morphologically distinct from isolates in the other clades, as they produce larger sclerotia and smaller pycnida and more sclerotia in planta. Additionally, ancestral range estimations suggest that the C. glycines lineage emerged in southern Africa. These results show that there is significantly more genetic and morphological diversity than was initially suspected with this high-consequence fungal plant pathogen.

High Agreement Between 3D Motion Capture and 2D Video Analysis Systems and Raters for Knee Flexion Range of Motion During A Single Leg Drop Landing Task

OBJECTIVE: To determine the agreement between a 2-dimensional (2D) and 3-dimensional (3D) video capture system and between multiple raters during a single leg drop landing task. DESIGN: Cross-sectional observational study METHODS: Single leg drop landing trials of twenty-three males (n=12) and females (n=11) were recorded using the 2D and 3D systems. Reliability and agreement were quantified through intraclass correlation coefficients (ICC2, k), standard error of the measurement (SEM), and Bland-Altman plots. Analysis of variance (ANOVA) assessed systematic error between the 2D raters and 3D system. RESULTS: A one-way ANOVA on 35 drop-landing trials across raters detected a difference across the 3 estimates of knee flexion range of motion (p<0.001) where rater 1 did not differ (mean difference ± standard error: 1.15±0.48°, p=0.54) from the 3D system, rater 2 > 3D system (3.30±0.76°, p<0.001), and rater 1 and 2 differed (2.14±0.82°, p=0.04). There was high reliability between the two 2D raters (ICC2, k=0.89; SEM=3.44°). Rater 1 completed analyses on 87 additional trials (total n=117) and was comparable to the 3D system (0.87±0.47°, p=0.06; ICC2, k=0.90; SEM=3.52°). CONCLUSION: 2D video analysis can be used in a clinical setting where 3D capture of movement mechanics is not feasible.

Comparison and application of the far-field identification algorithms for multiple sound sources based on microphone array

Based on the conventional beamforming (CBF), some algorithms for far-field sound source identification have been proposed in the past few decades. Typically, the functional beamforming (FBF) and the deconvolution methods, such as CLEAN, CLEAN with source coherence (CLEAN-SC), CLEAN-SC with compressed grids (CLEAN-SC-CG), CLEAN with cross spectral matrix function (CLEAN-CSM), High-resolution CLEAN-SC (HR-CLEAN-SC), are frequently mentioned and their advantages are widely discussed in the previous literatures. To assess their efficacy and suitability in engineering applications, with a focus on spatial resolution, computational efficiency, and dynamic range, a comparative study by locating two types of sound sources is carried out. The first scenario represents the case where the distance between two sound sources is smaller than the Rayleigh limit, while the second scenario represents the situation involving multiple sound sources, such as four or more complex sound sources. The analysis demonstrates that CBF, CLEAN, and CLEAN-SC cannot surpass the Rayleigh limit. However, FBF, CLEAN-SC-CG, CLEAN-CSM, and HR-CLEAN-SC have the potential to overcome it. In FBF, the grid mismatch results in a compromise between its dynamic range and source strength estimation. Meanwhile, HR-CLEAN-SC requires prior knowledge of the number of sound sources, which is challenging in applications. Because of superiority in the fundamental acoustic image and searching strategy, CLEAN-CSM and CLEAN-SC-CG exhibits superior features compared to the others. By compressing the number of grids, the CLEAN-SC-CG can improve the computational efficiency up to at least 46%. By constructing the cross spectral matrix function related to the real source, CLEAN-CSM uses the power function to simultaneously enhance the spatial resolution, dynamic range and source strength estimation. The conclusions are further validated through sound-source identification experiments involving two loudspeakers and an engine. The findings presented in this paper serve to guide the selection of suitable approaches for multi-sound source identification in engineering applications.