Validity Of Method Research Articles

Automated detection of bone lesions using CT and MRI: a systematic review.

The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications. A literature search was conducted on PubMed and MEDLINE. Data were extracted and grouped into three main categories, namely baseline study characteristics, model validation strategies, and the type of AI algorithms. A total of 10 studies were selected and analyzed, including 2,768 patients overall with a median of 187 per study. These studies utilized various AI algorithms, predominantly deep learning models (6 studies) such as Convolutional Neural Networks. Among machine learning validation strategies, K-fold cross-validation was the mostly used (5 studies). Clinical validation was performed using data from the same institution (internal testing) in 8 studies and from both the same and different (external testing) institutions in 1 study, respectively. AI, particularly deep learning, holds significant promise in enhancing diagnostic accuracy and efficiency. However, the review highlights several limitations, such as the lack of standardized validation methods and the limited use of external datasets for testing. Future research should address these gaps to ensure the reliability and applicability of AI-based detection systems in clinical settings.

Cross-validation of pharmacokinetic assays post-ICH M10 is not a pass/fail criterion.

The ICH M10 guideline establishes global standards for bioanalytical method validation for pharmacokinetic assays, focusing on data reliability and accuracy across studies. A significant component is cross-validation, which should be performed to ensure data comparability when multiple methods or laboratories are involved in a single study or across studies where comparison will be performed. However, ICH M10 does not specify acceptance criteria for cross-validation, creating challenges for the industry because traditionally many laboratories have always utilized acceptance criteria to "pass" or "fail" the study. This editorial discusses how bioanalytical labs should conduct cross-validation for PK assays post-ICH M10, highlighting the role of statistical methods and the need for close collaboration with clinical pharmacology and biostatistics departments. Proper implementation and strategic focus on relevant studies are essential for effective cross-validation.

Contact Risk Assessment in Dynamic Indoor Settings through Agent‐Based Modeling: A Spatially Explicit and Reproducible Approach

This study introduces an agent‐based model (ABM) pedestrian simulation tool to assess the risk of close contact (6 feet) in dynamic indoor environments, specifically in urban settings with diverse social activities and spatial structures. Our approach uses machine learning‐based sensitivity analysis (SA) to identify factors impacting the number of individual contacts, such as individual stay time and area. In addition, we conducted an in‐depth quantitative analysis to evaluate how specific factors, such as the strategic placement of obstacles, dwell time, and stay time near the entrances, mitigate the number of contacts. This analysis provides valuable insights for developing practical guidelines to curb contact risks in indoor environments. Lastly, we share the model, validation methods, and associated data as an open‐source Python library, complete with comprehensive documentation. This aims at fostering collaborative research and enables the application of our model across various scenarios, contributing to the development of spatially explicit models. Such efforts enhance the understanding of contact risks in urban indoor settings and promote joint research efforts, thus advancing the field through shared knowledge and tools.

Validity and reliability of the TechPH scale in assessing Iranian older adults’ attitudes toward technology

Background and aimGiven the rapid technological advancements and increased usage of digital tools, understanding older people's attitudes toward technology is vital. Assessing their views can help identify barriers and facilitators to adoption. This understanding is essential for developing effective educational strategies and user-friendly technologies that enhance seniors' quality of life. Therefore, the present study aimed to psychometrically evaluate the scale for measuring attitudes—both willingness and anxiety—toward technology (TechPH) in Iranian older adults.MethodsThis methodological study was conducted on 420 older individuals (aged 60 and above) in Tehran in 2024 to perform a psychometric test of the attitudes toward technology scale (TechPH). Validation was carried out using translation validity methods, including translation-back translation with bilingual experts (n = 2), face validity with a sample of the older population (n = 10), content validity with a panel of 11 experts, and construct validity through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) (n = 420). Instrument reliability was determined through test–retest and internal consistency (Cronbach's alpha) methods with a sample of older individuals (n = 30). Data analysis was performed using SPSS version 16 and EQS 6.4 software.ResultsA comparison of Persian and English translations revealed acceptable translation validity and cultural adaptability for the scale. Each item's Content Validity Index (CVI) and Content Validity Ratio (CVR) were determined, with a total average CVI of 0.95. The results of EFA indicated that the sample size was adequate, as shown by the KMO value of 0.754. Additionally, Bartlett's sphericity test demonstrated a significant correlation between the items (χ2 = 221.819, df = 15, P < 0.0001). EFA revealed that two extracted factors explained 41.002% and 18.111% of the total variance. Furthermore, CFA yielded suitable estimates based on the general fit indices of the model (RMSEA = 0.061, IFI = 0.979, GFI = 0.983, CFI = 0.978, CMIN/DF = 1.769, MFI = 0.989, AGFI = 0.942). In evaluating test–retest stability and internal consistency, the values of the Intra-class Correlation Coefficient (ICC) and Cronbach's α were 0.85 and 0.77, respectively, indicating appropriate reliability for the scale.ConclusionP.TechPH, the first Persian version of the scale for measuring technophilia and technophobia among Iranian older people, has favorable psychometric properties. It can serve as a standard tool for assessing older people’s attitudes toward technology in various studies.

Development and validation of an LC‒MS/MS method for the determination of cyclocreatine phosphate and its related endogenous biomolecules in rat heart tissues

The cardioprotective drug cyclocreatine phosphate has been awarded Food and Drug Administration-orphan drug designation for the prevention of ischemic injury to enhance cardiac graft recovery and survival in heart transplantation. Cyclocreatine phosphate is the water-soluble derivative of cyclocreatine. Estimating the levels of Cyclocreatine phosphate, Adenosine triphosphate, Creatine Phosphate, Creatine and Cyclocreatine helps us in understanding the energy state as well as evaluating the heart cells’ function. The quantification of endogenous compounds imposes a challenging task for analysts because of the absence of a true blank matrix, whose use is required according to international guidelines. Recently, the International Council for Harmonization issued a new guideline that contains guidance on the validation of methods used to quantify endogenous components, such as the background subtraction approach that was employed in our current study. Specifically, we developed and validated a sensitive, reliable and accurate liquid chromatography-tandem mass spectrometry assay to determine simultaneously the levels of mentioned endogenous compounds in rat heart tissue. Tissue samples were prepared by protein precipitation extraction using water: methanol (1:1). Using Ultra Performance Liquid Chromatography, Chromatographic separation was achieved with ZORBAX Eclipse Plus C18 4.6 × 100 mm,3.5 μm column and conditions as following: ammonium acetate (pH 8.5): acetonitrile, 70:30 mobile phase, 0.7 mL/min flow rate and 25 °C temperature. Electrospray ionization mass detector with Multiple reaction monitoring mode was then employed, using both positive and negative modes, Analysis was carried out using 5.00–2000.00 ng/mL linear concentration range within 2 min for each analyte. According to Food and Drug Administration guidelines for bioanalytical methods, validation was carried out. We investigated the matrix effect, recovery efficiency and process efficiency for the analyte in neat solvent, postextraction matrix and tissue. The results stated mean percentage recoveries higher than 99%, accuracy 93.32–111.99%, and Relative Standard Deviation (RSD) below 15% within the concentration range of our study which indicated that target analytes’ stability in their real matrix is sufficient under the employed experimental conditions.

Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection

Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically performed using tools provided by the waveform database (WFDB) or custom methods. However, many studies fail to provide detailed information on the validation process. The literature also highlights inconsistencies in reporting window size, a crucial parameter used to compare predictions with expert annotations to distinguish false peaks from the true R-peak. Additionally, there is also a need for uniformity in reporting the total number of beats for individual or collective records of the widely used MIT-BIH arrhythmia database. Thus, we aim to review validation methods of various R-peak detection methodologies before their implementation in real time. This review discusses the impact of non-beat annotations when using a custom validation method, allowable window tolerance, the effects of window size deviations, and implications of varying numbers of beats and skipping segments on ECG testing, providing a comprehensive guide for researchers. Addressing these validation gaps is critical as they can significantly affect validatory outcomes. Finally, the conclusion section proposes a structured concept as a future approach, a guide to integrate WFDB R-peak validation tools for testing any QRS annotated ECG database. Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery.

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

Teleseismic virtual-source reverse time migration of upper crustal structures in the Three Gorges region, China

Summary The Three Gorges (TG) in central China is an earthquake prone region that lacks active-source seismic surveys, hence requiring high-resolution passive seismic imaging to reveal crustal structures in detail. While teleseismic virtual-source reflection (TVR) profiling is effective for imaging gently dipping upper crustal structures, it is unsuitable for mapping steep structures beneath rugged topography in the TG region. Here we develop a teleseismic virtual-source reverse time migration (TV-RTM) to improve the imaging of steep structures. Synthetic tests are conducted to demonstrate the validity and resolution of the TV-RTM method. To mitigate the imaging inaccuracy due to sparse station spacing, we interpolate direct-arrival waveforms to achieve a doubling of the minimally required station spacing from 2 km to 4 km. The TV-RTM images of the upper crustal structure beneath a 2D array in the TG region reveal significant fold and thrust structures that correlate well with the surface geology and tectonic framework. The resolution of the images is assessed using 2D and 3D synthetic models with the station and source geometry of field data. The TV-RTM method provides a new passive seismic solution for studying upper crustal structures in regions that lack active-source seismic data.

Study on the analytical verification method of geometric error models

Abstract Geometric error compensation is a widely used and effective approach for enhancing the accuracy of Coordinate Measuring Machines (CMMs) and Computer Numerical Control (CNC) machines. The research in this field primarily focuses on the challenging task of developing geometric error modelling techniques. Currently, there are several well-established methods for modelling geometric errors. However, there is scarce availability of methods that can validate the established geometric error models. Therefore, this article proposes an analytical validation method for geometric error models based on the error transformation equation. The principle of the method is elaborately explained, and its correctness is verified through specific model verification example. The method proposed in this article enables rapid and accurate model correction and iteration, effectively reducing the economic and time costs of model validation in the current geometric error compensation process. Therefore, the method provides guidance for standardising the modelling process and could be widely applied to various structural forms of measurement and machining systems.

Ergonomics in Bicycle Saddle Design: Application of TRIZ Innovation System Method with IPA-Kano Model Validation

This study investigates the innovative design of a bicycle saddle by incorporating sustainable ergonomics, universal design principles, and systematic innovation methods. Initially, the literature related to bicycle saddle design and its impact on the human body during riding was analyzed. The TRIZ contradiction matrix was then used to identify relevant invention principles, which served as references for the innovative design of the bicycle saddle. Biomechanics and the human–machine system analysis within human factors engineering were applied to ensure the innovative design is ergonomic and user-friendly. The design features a horizontally expandable and foldable bicycle saddle, enhancing its adaptability and sustainability. Universal design principles were applied to make the innovative design more accessible to the general public, and the prototype was simulated using Inventor drawing software. The research results include: (1) An innovative bicycle saddle design with horizontal expansion and folding functions is proposed. This design divides the saddle into three components, enabling the left and right parts to expand or retract based on user preferences. (2) A bicycle backrest design featuring vertical adjustability is introduced. It incorporates a quick-release adjustment mechanism at the junction of the backrest and saddle, allowing users to freely adjust the backrest height. (3) A quick-operation bicycle saddle design is presented, utilizing quick-release screws to facilitate the swift operation of the horizontal expansion and folding mechanisms. This validation method confirmed that the innovative design meets both sustainable ergonomic standards and user expectations. The systematic innovation approach used in this study can serve as a valuable reference for future research and design applications.

New Experimental Approaches for the Determination of Flammability Limits in Methane–Hydrogen Mixtures with CO2 Inertization Using the Spark Test Apparatus

This study presents a novel experimental method to determine the flammability limits and the minimum oxygen concentration in methane–hydrogen mixtures using the spark test apparatus (STA), by incorporating CO2 as an inert compound. The proposed methodology allows for the more accurate and efficient assessment of the safety of these flammable mixtures, which is crucial for industrial applications where hydrogen-enriched fuels are used. When comparing the literature data, the differences between methods are not significant, although the procedure, apparatus, and test conditions influence the results. Then, the proposed method is experimentally validated in the STA. Methane is enriched with hydrogen at different concentrations (10, 20, 30, and 50%). The results in the STA show good alignment with the literature data. Furthermore, literature data analysis allows for the generation of an empirical curve that shows the influence of hydrogen addition in methane–air mixtures. The theoretical flammability intervals are also presented as a result. Such representations, after method validation, are the base of the flammability interval test in the STA. The capability of the STA to define flammability ranges in ternary diagrams provides an innovative graphical approach to control explosive atmospheres and facilitates its application in the prevention of industrial accidents.

State-of-the-Art CFD Simulation: A Review of Techniques, Validation Methods, and Application Scenarios

This review examines recent advancements in Computational Fluid Dynamics (CFD) simulation, focusing on state-of-the-art techniques, validation methods, and their application across various fields. CFD techniques, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), Reynolds-Averaged Navier-Stokes (RANS), and hybrid methods, are compared in terms of computational demands, accuracy, and suitability for different fluid flow scenarios. Effective validation methods such as wind tunnel testing, analytical solutions, and field measurements are highlighted to ensure simulation reliability and align models with experimental data. Additionally, this review explores application scenarios in aerospace, automotive, environmental, and biomedical engineering, discussing how each field adapts CFD techniques to optimize performance and ensure realistic simulation outcomes. The comparative analysis offers insights into the trade-offs between accuracy and computational cost. It underscores the need for careful technique selection and robust validation to enhance CFD's applicability in both research and industry. The review aims to guide practitioners in selecting the most appropriate CFD approaches for their specific engineering challenges.

Understanding the role of machine learning in predicting progression of osteoarthritis.

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.

Measurement of Pt consumption from Pt electrodes during water electrolysis using the quartz crystal microbalance (QCM) method: Effect of the water electrolysis method and the concentration of chloride ions in the aqueous solution

The consumption of the Pt electrocatalyst, which occurs when aqueous solution with neutral pH is electrolyzed with a Pt electrode to produce hydrogen or oxygen, was measured from the change in resonance frequency using the QCM method. First, it was found that the Pt consumption from the electrode during water electrolysis while applying a steady positive potential was very small, but it was almost proportional to the time the current was applied. Second, when electrolysis was performed by polarity reversal electrolysis of the anode and cathode periodically during electrolysis, the amount of Pt consumed increased in proportion to the number of polarity reversal was exchanged. The Pt consumption obtained by the QCM method was in close agreement with the value obtained by EDX method, proving the validity of the measurement method. The QCM measurement method was also proven to be a reliable method of measurement.

Validation and implementation of whole-genome sequencing-based analytical methods for molecular surveillance and relatedness analysis of Mycobacterium tuberculosis Complex isolates at a National Reference laboratory

Whole genome sequencing-based methodologies have become extremely relevant for the molecular surveillance of human pathogens and are being increasingly introduced into national reference laboratory services. In this study, we describe the validation and implementation of core-genome Multi-Locus Sequence Typing (cgMLST) and whole genome single-nucleotide polymorphism (wgSNP) analysis at the Irish Mycobacteria Reference Laboratory, as a replacement for Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing. Concordance of clustering, discriminatory power, and ease-of-use of both WGS analytical methods were evaluated. Although wgSNP analysis (MTBseq) was the most discriminatory method (p<0,001), we recommend cgMLST (SeqSphere+), as the first-line approach for molecular typing of Mycobacterium tuberculosis isolates in the context of routine surveillance work due to its ease of use and decreased turnaround time, while reserving wgSNP-analysis for a more in-depth cluster analysis of new isolates that show a distance of ≤ 12 alleles to any other isolate(s) in the cgMLST database.

A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

The driving cycle (DC) is essential for establishing vehicle emission standards, transportation policies, and urban planning. However, existing driving cycles demonstrate poor representativeness and excessive randomness due to the insufficient capture of driving characteristics. Therefore, a novel framework for constructing and evaluating driving cycles of electric vehicles (EVs) based on energy consumption and emissions analysis is proposed to enhance the representativeness of the constructed driving cycles. First, based on road information, an improved dual-chain Markov chain method combined with the self-organizing mapping (SOM) neural network is introduced for clustering and constructing driving cycles. Subsequently, a double-layer evaluation model oriented towards energy consumption and emissions is established through a combination of model-driven and data-driven approaches to select a representative driving cycle (RDC). Finally, comparative experiments are conducted to evaluate the feasibility and scientific validity of the proposed method in multiple dimensions. The results indicate that the driving cycle constructed in this study demonstrates excellent representativeness, with an emission error of 2.04% and a comprehensive characterization parameter (CCP) value of 0.097. This study emphasizes the necessity of incorporating reasonable constraints in the driving cycle construction. This improved representativeness provides a reliable foundation for electric vehicle design and transportation policy development.

Development and validation of ultra-performance liquid chromatography tandem mass spectrometry methods for the quantitative analysis of the antiparasitic drug DNDI-6148 in human plasma and various mouse biomatrices

DNDI-6148 is a promising new oral drug for the treatment of cutaneous leishmaniasis (CL), a parasitic neglected tropical disease that affects impoverished populations worldwide. Preclinical target site pharmacokinetics (PK) studies are necessary to evaluate the actual exposure to DNDI-6148 of Leishmania parasites in the skin. To facilitate these investigations, we have developed and validated a reversed phase ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) method to quantify DNDI-6148 in relevant target site PK samples, adhering to the relevant International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M10 guideline on bioanalytical method validation. Full validation was performed for the surrogate biomatrices human K2EDTA plasma, enzymatic digestion buffer and skin microdialysate. Partial validation was conducted for mouse K2EDTA plasma and tissues. The tissue samples, including mouse skin, liver and spleen, were homogenized using a collagenase A-based enzymatic homogenization workflow. This method was found to be 2.9-fold more effective in extracting DNDI-6148 from skin than the commonly used mechanical homogenization. Protein precipitation was subsequently carried out for all biomatrices. A surrogate biomatrix was used for each method and the range was specifically developed for its intended application, resulting in a linear concentration range of 5.00–2,000 ng/mL, 2.00–1,000 ng/mL, and 3.00–600 ng/mL for human K2EDTA plasma, enzymatic digestion buffer and microdialysate, respectively. Each biomatrix had intra- and inter-run accuracy and precision within 15 % for all concentration levels. Matrix effects did not affect the determination of DNDI-6148, since the stable isotopically-labelled internal standard for DNDI-6148 effectively compensated for these matrix effects. Total recovery across all methods was between 73.5 % and 81.3 % (CV ≤4.5 %). DNDI-6148 was stable under various conditions in all the tested biomatrices. However, a decrease in its concentration was observed during homogenization, for which the internal standard corrected adequately. The suitability of the method for use in future preclinical research involving DNDI-6148 was demonstrated in a preclinical target site PK study using a CL-infected murine model.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.