Conventional Estimation Research Articles

Spatial and temporal predictions of mosquito potential breeding sites and densities: integration of satellite imagery, in-situ data, and process-based modeling

Abstract. Dengue fever, primarily transmitted worldwide by the mosquito Aedes aegypti, poses significant public health challenges in tropical and subtropical regions. While effective vector control is crucial in the absence of reliable dengue vaccines, traditional control methods face obstacles like mosquito resistance to insecticides and a very high cost. By combining geospatial data, including satellite imagery, as descriptors, and entomological surveys as target variables in a Random Forest model, we predicted the number of potential mosquito breeding sites, derived the associated environmental carrying capacity for larvae, and used the Arbocarto process-based model to predict Ae.aegypti population densities in an urban region of French Guiana, South America. Our findings highlight that remote sensing data may help predict the number of potential breeding sites over urban areas. Our simulations indicate higher mosquito densities in urban residential areas and a strong spatial and temporal heterogeneity. These densities fluctuate according to intra-annual variations in temperature and precipitation, with higher densities associated with intermediate housing. A comparison with the conventional estimation of environmental carrying capacity for larvae in the current Arbocarto procedure highlights the advantages of our approach. Our study demonstrates the utility of integrating remote sensing with predictive modeling to enhance vector surveillance and control strategies, and provides a replicable approach for monitoring a dengue vector mosquito population in dynamic urban landscapes.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling

In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

Coda-derived source properties estimated using local earthquakes in the Sea of Marmara, Türkiye

Abstract. Accurate estimates of the moment magnitude of earthquakes that physically measure the earthquake source energy are crucial for improving our understanding of seismic hazard in regions prone to tectonic activity. To address this demand, a method involving coda wave modelling was employed to estimate the moment magnitudes of earthquakes in the Sea of Marmara, northwestern Türkiye. This approach enabled us to model the source displacement spectrum of 303 local earthquakes efficiently recorded at 49 regional seismic stations between 2018 and 2020 in the region. The coda wave traces of individual events were inverted across 12 frequency ranges between 0.3 and 16 Hz. The resultant coda-derived moment magnitudes were found to be in good accordance with the conventional local magnitude estimates. However, the notable move-out between local magnitude and coda-derived moment magnitude estimates for smaller earthquakes less than a magnitude of 3.5 likely occurs due to potential biases arising from incorrect assumptions for anelastic attenuation and/or the finite sampling intervals of seismic recordings. Scaling relations between the total radiated energy and seismic moment imply a non-self-similar behaviour for the earthquakes in the Sea of Marmara. Our findings suggest that larger earthquakes in the study area exhibit distinct rupture dynamics compared to smaller ones, resulting in a more efficient release of seismic energy. Hence, we introduce an empirical relationship obtained from the scatter between local magnitude and coda-derived moment magnitude estimates.

Determination of Average Water Demand and Diurnal Consumption Pattern for a Multifamily Apartment Complex in Peshawar

Abstract: Accurately estimating and understanding domestic water demand and consumption patterns is essential for the efficient planning and management of drinking water supply systems. Traditional methods for estimating water demand are labor-intensive and often rely on assumptions. To overcome these limitations, this study employed a novel approach using highprecision data loggers to monitor water levels continuously in rooftop tanks at 5-minute intervals within a residential apartment complex in Peshawar, Pakistan. The findings indicate that per capita water consumption varies between 97 and 209 liters per capita per day (lpcd), with an average consumption of 173 lpcd. This methodology offers enhanced accuracy in quantifying water usage, providing a robust alternative to conventional estimation techniques.

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has a smaller variance than conventional OLS and RR estimators. The optimal value of the ridge parameter has a vital role in bias-variance tradeoff. This study proposes quantile-based estimation of ridge parameter in robust RR to deal with the joint issue of y-direction outliers and multicollinearity. The effectiveness of the proposed estimators is evaluated using intensive Monte Carlo simulation and two real data sets in terms of mean square error (MSE) and predictions sum of squared error (PSSE) criterion. Simulation findings reveal that the newly developed estimators of ridge parameter in robust RR have better performance than OLS, RR, and existing robust RR estimators when errors are normally and non-normally distributed. The results from two numerical examples of T20 Cricket sports and anthropometric data show that the new estimator with quantile probability 0.50 and 0.99 respectively has winning performance among all competing and proposed estimators.

James-Stein estimator improves accuracy and sample efficiency in human kinematic and metabolic data.

Human biomechanical data are often accompanied with measurement noise and behavioral variability. Errors due to such noise and variability are usually exaggerated by fewer trials or shorter trial durations, and could be reduced using more trials or longer trial durations. Speeding up such data collection by lowering number of trials or trial duration, while improving the accuracy of statistical estimates, would be of particular interest in wearable robotics applications and when the human population studied is vulnerable (e.g., the elderly). Here, we propose the use of the James-Stein estimator (JSE) to improve statistical estimates with a given amount of data, or reduce the amount of data needed for a given accuracy. The JSE is a shrinkage estimator that produces a uniform reduction in the summed squared errors when compared to the more familiar maximum likelihood estimator (MLE), simple averages, or other least squares regressions. When data from multiple human participants are available, an individual participant's JSE can improve upon MLE by incorporating information from all participants, improving overall estimation accuracy on average. Here, we apply the JSE to multiple time-series of kinematic and metabolic data from the following parameter estimation problems: foot placement control during level walking, energy expenditure during circle walking, and energy expenditure during resting. We show that the resulting estimates improve accuracy - that is, the James-Stein estimates have lower summed squared error from the 'true' value compared to more conventional estimates.

Inference in Experiments with Matched Pairs and Imperfect Compliance

This paper studies inference for the local average treatment effect in randomized controlled trials with imperfect compliance where treatment status is determined according to “matched pairs.” By “matched pairs,” we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Under weak assumptions governing the quality of the pairings, we first derive the limit distribution of the usual Wald (i.e., two-stage least squares) estimator of the local average treatment effect. We show further that conventional heteroskedasticity-robust estimators of the Wald estimator’s limiting variance are generally conservative, in that their probability limits are (typically strictly) larger than the limiting variance. We therefore provide an alternative estimator of the limiting variance that is consistent. Finally, we consider the use of additional observed, baseline covariates not used in pairing units to increase the precision with which we can estimate the local average treatment effect. To this end, we derive the limiting behavior of a two-stage least squares estimator of the local average treatment effect which includes both the additional covariates in addition to pair fixed effects, and show that its limiting variance is always less than or equal to that of the Wald estimator. To complete our analysis, we provide a consistent estimator of this limiting variance. A simulation study confirms the practical relevance of our theoretical results. Finally, we apply our results to revisit a prominent experiment studying the effect of macroinsurance on microenterprise in Egypt.

VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography.

Acne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process. In this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net. VP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net's proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets. This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

Surgical Outcomes After Preoperative Embolization in Hypervascular Spinal Metastases: A Propensity Scored Study of 191 Patients.

Retrospective propensity-score matched, case control study at 2 academic tertiary care centers. To assess the effect of PE on (1) intraoperative blood loss, defined as conventional estimates of blood loss (EBL) and hemoglobin mass loss, and (2) secondary outcomes in patients with spinal metastases from hypervascular histologies. Preoperative embolization (PE) intends to reduce blood loss during surgery for spinal metastases of hypervascular tumors such as renal cell carcinoma. However, studies investigating the effect of PE in hypervascular tumors often consist of small cohorts, do not correct for confounding factors, and have conflicting results. After propensity score matching (PSM), 46 PE patients were matched to 46 non-PE patients without baseline differences. The constraints of PSM did not allow analysis of patients with tumor volumes>9cm3. Multiple linear regression models were fitted for EBL and hemoglobin mass loss. Poisson regression models were fitted for both intraoperative and postoperative transfusions. There was no difference in EBL (948mL [IQR 500-1750] vs. 1100mL [IQR 388-1925], P=0.68) and hemoglobin mass loss (201g [IQR 119-307] vs. 232g [IQR 173-373], P=0.18) between PE and non-PE patients. Other than higher 1-year survival rates (65% vs. 43%, P=0.05) in PE-patients, there were no differences in secondary outcomes. In multiple regression analyses, PE was not associated with decreased intraoperative blood loss, hemoglobin mass loss or perioperative blood transfusions. Our study demonstrated that, for tumors <9cm3, PE did not reduce EBL, hemoglobin mass loss or perioperative blood transfusions in patients undergoing spine surgery for metastases from hypervascular histologies. These findings suggest that urgent spine surgeries indicated for hypervascular histologies should not be delayed based on the availability of PE and accurate detection of pre-operative hypervascularity, beyond histology, will likely be an important determination of future PE utilization for spinal metastases. Treatment Benefits, Level III.

On The Modeling of Biomedical Data Sets with a New Generalized Exponentiated Exponential Distribution

Introduction: Many experts in the field of distribution theory have focused on extending probability distributions utilizing extended families of continuous distributions to improve the modeling adaptability of the conventional probability distributions. Methods: This research employed a continuous family of probability distributions as introduced in the literature using the T-X methodology. Through this, some of statistical and mathematical properties of the model were derived and studied Results: This study had introduced a brand-new, five-parameter generalized exponentiated exponential distribution, which is a continuous probability distribution. With the aid of the quantile function, moments, moment generating function, survival function, hazard function, mean, and median, among other mathematical and statistical aspects, the new distribution's shape was deduced and researched. It was also possible to derive the probability density function for the minimum and maximum order statistics for this distribution. The method of maximum likelihood estimate was used to produce a conventional estimation of the unknown parameters. A simulation study was carried out to assess the efficiency and consistency of the estimation method used. To evaluate the fit and adaptability of the new model, it was applied to four real-world datasets in the field of medicine. Conclusion: The analysis's findings demonstrated that the new model performs better than its counterparts and offers a better fit than the Topp-Leone exponentiated exponential, Topp-Leone Kumaraswamy exponential, exponentiated exponential, and exponential distributions.

Identifying optimal cloud cover for enhanced forest carbon uptake: Periodic-case NEE-overshoot modelling

On certain kinds of cloudy days, many forested ecosystems exhibit enhanced carbon uptake and water-use efficiency-the cloudy-day forest flux anomaly. Using ensemble methods to analyze eddy-covariance fluxes, we have diagnosed net ecosystem exchange (NEE) and water-use efficiency (WUE) of a temperate broadleaf forest and a tropical evergreen forest as they responded to natural fluctuating-light regimes. Here we apply average NEE and evapotranspiration solutions of a first-order dynamic model to describe the observed whole-canopy sensitivity to periodic light. On partly-cloudy days, maximum overall NEE enhancements over conventional steady-state equilibrium estimates are ≈ 25% for a midlatitude deciduous forest and ≈ 15% for a tropical evergreen forest. This finding supports our conclusion that in many cases the cloudy-day anomaly is a consequence of a dynamic response by the trees responding to fluctuating-light regimes occasioned by passing cumulus clouds.

Leveraging External Aggregated Information for the Marginal Accelerated Failure Time Model.

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small-scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank-estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non-negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance.

A regression-based approach to the CO2 airborne fraction

The global fraction of anthropogenically emitted carbon dioxide (CO2) that stays in the atmosphere, the CO2 airborne fraction, has been fluctuating around a constant value over the period 1959 to 2022. The consensus estimate of the airborne fraction is around 44%. In this study, we show that the conventional estimator of the airborne fraction, based on a ratio of changes in atmospheric CO2 concentrations and CO2 emissions, suffers from a number of statistical deficiencies. We propose an alternative regression-based estimator of the airborne fraction that does not suffer from these deficiencies. Our empirical analysis leads to an estimate of the airborne fraction over 1959–2022 of 47.0% (± 1.1%; 1σ), implying a higher, and better constrained, estimate than the current consensus. Using climate model output, we show that a regression-based approach provides sensible estimates of the airborne fraction, also in future scenarios where emissions are at or near zero.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.

Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.

Effect of Leaving Centrosymmetric Character on Spectral Properties in Mono-, Bi-, and Triphotonic Absorption Spectroscopies.

Numerical simulations of the absorption bands of photoswitch E-o-tetrafluoroazobenzene in DMSO solution under one-, two-, and three-photon absorption conditions combined with the analysis of the behavior of transition probability under distortion of planarity reveal many similarities between the mono- and triphoton spectroscopic behaviors with a two-photon spectrum being set apart. The position of the absorption peak for the studied nπ* and ππ* transitions appears shifted to lower energies (longer wavelengths) than the conventional estimate based on vertical excitation from the ground-state potential energy minimum.

Escalation of Human Resource Quality in National Health Resilience Through Estimating Multilevel Model Parameters with the Restricted Maximum Likelihood Approach to The 2019 UTBK Average

Multilevel modeling or Hierarchical Linear Modeling (HLM) is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of factors at both the individual and group levels to the response variable. One method commonly used in HLM is Restricted Maximum Likelihood (REML). REML is a parameter estimation method that is often applied in statistical models, especially linear models that incorporate random components. This allows more efficient parameter assessment compared to conventional estimation methods. In this research, multilevel model parameter estimation analysis was carried out using the limited maximum likelihood approach. The aim is to determine the multilevel linear regression model on the average UTBK score for health cluster study programs in 2019. This involves selecting the optimal node point based on the minimum Generalized Cross Validation (GCV) and identifying the factors that influence it. Predictor variables considered include interest and capacity of study programs at university level (Level-1), as well as the average UNBK and HDI scores at provincial level (Level-2). The findings of this research indicate that the most appropriate multilevel regression model is formed with three nodes with a minimum GCV value at Level-1 of 864.6593 and at Level-2 of 3.1816. At Level-1, the influencing factor is the interest variable and at Level-2 is the average provincial UNBK score in 2019 and the Human Development Index (HDI).

Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM

Accurate state-of-charge (SOC) estimation is a crucial part of the battery management system (BMS). However, conventional estimation methods are unable to capture the extremely complex dynamic characteristics of lithium-ion batteries. Besides, manually setting the optimal hyperparameters of models has many drawbacks. To address the problems, an (improved whale optimization algorithm) IWOA- (long short-term memory) LSTM model is proposed in this work. Utilizing the whale optimization algorithm (WOA) improved with four enhancement strategies (Gaussian chaotic mapping initialization, Nonlinear weight update, Lévy flight mechanism, and Elite opposition-based learning) to optimize the number of hidden layer nodes, the learning rate, and the number of iterations of LSTM model. It not only overcomes the shortcomings of artificially setting LSTM hyperparameters but also further boosts the learning ability of the IWOA-LSTM model, making the model more suitable for SOC estimation under different scenarios. The evaluation results show that the MAE of the proposed model for SOC estimation results is lower than 0.8 % under different temperatures and dynamic conditions. Compared with SOTA models, all MAE, RMSE, and MAPE of the proposed model substantially decline. Furthermore, the R2 of the estimation results using the LG dataset in Experiment Ⅲ is 98.65 %, suggesting the applicability of the proposed model to Li-ion batteries from various manufacturers. The experimental results demonstrate the proposed IWOA-LSTM model is suitable for accurate SOC estimation.

Radial velocity estimation method for a single hydrophone based on passive target line spectrum characteristics

Passive radial velocity estimation based on a single hydrophone has a wide range of applications in engineering, and can be used in small underwater platforms such as submerged buoys and underwater gliders. The conventional passive radial velocity estimation method based on a single hydrophone has poor performance due to the influence of noise. In this paper, the time delay coherence coefficient (TDCC) of passive target line spectrum is derived based on a single hydrophone. Further, the radial velocity can be obtained by the time delay required by the phase change of TDCC for one period. Based on the characteristics that the line spectrum phase is stable and the noise phase is random, coherent averaging (CA) is applied to suppress noise. The use of CA greatly reduces the influence of noise on TDCC and efficiently improves the accuracy of radial velocity estimation. The performance and system error of the proposed CA-TDCC method are analyzed through a series of simulations. Finally, the CA-TDCC method is used to process SwellEx-96 data, and the relative error of radial velocity estimation is less than 10%, which verifies the effectiveness of this method in practical applications.Abbreviation: CA, Coherent averaging; SNRs, Signal-to-noise ratios; TDCC, Time delay coherence coefficient; TMA, Target motion analysis.