Estimation Accuracy Research Articles

Noise-Robust 3D Pose Estimation Using Appearance Similarity Based on the Distributed Multiple Views.

In this paper, we present a noise-robust approach for the 3D pose estimation of multiple people using appearance similarity. The common methods identify the cross-view correspondences between the detected keypoints and determine their association with a specific person by measuring the distances between the epipolar lines and the joint locations of the 2D keypoints across all the views. Although existing methods achieve remarkable accuracy, they are still sensitive to camera calibration, making them unsuitable for noisy environments where any of the cameras slightly change angle or position. To address these limitations and fix camera calibration error in real-time, we propose a framework for 3D pose estimation which uses appearance similarity. In the proposed framework, we detect the 2D keypoints and extract the appearance feature and transfer it to the central server. The central server uses geometrical affinity and appearance similarity to match the detected 2D human poses to each person. Then, it compares these two groups to identify calibration errors. If a camera with the wrong calibration is identified, the central server fixes the calibration error, ensuring accuracy in the 3D reconstruction of skeletons. In the experimental environment, we verified that the proposed algorithm is robust against false geometrical errors. It achieves around 11.5% and 8% improvement in the accuracy of 3D pose estimation on the Campus and Shelf datasets, respectively.

Over 100-fold improvement in the accuracy of relaxed eddy accumulation flux estimates through error diffusion

Measurements of atmosphere-surface exchange are largely limited by the availability of fast-response gas analyzers; this limitation hampers our understanding of the role of terrestrial ecosystems in atmospheric chemistry and global change. Current micrometeorological methods, compatible with slow-response gas analyzers, are difficult to implement, or rely on empirical parameters that introduce large systematic errors.Here, we develop a new micrometeorological method, optimized for slow-response gas analyzers, that directly measures exchange rates of different atmospheric constituents, with minimal requirements. The new method requires only the sampling of air at a constant rate and directing it into one of two reservoirs, depending on the direction of the vertical wind velocity. An integral component of the new technique is an error diffusion algorithm that minimizes the biases in the measured fluxes and achieves direct flux estimates.We demonstrate that the new method provides an unbiased estimate of the flux, with accuracy within 0.1% of the reference eddy covariance flux, and importantly, allows for significant enhancements in the signal-to-noise ratio of measured scalars without compromising accuracy. Our new method provides a simple and reliable way to address complex environmental questions and offers a promising avenue for advancing our understanding of ecological systems and atmospheric chemistry.

Enhancing capacity estimation of retired electric vehicle lithium-ion batteries through transfer learning from electrochemical impedance spectroscopy

The low economic feasibility caused by inefficient testing and inaccurate performance estimation is one of the main bottlenecks in the echelon utilization of large-scale retired batteries. This study proposes a fast and accurate capacity estimation method for retired batteries based on electrochemical impedance spectroscopy (EIS). Firstly, the EIS of the batteries that experience multi-condition aging in the laboratory are collected. EIS characteristic parameter sequences highly related to battery performance, including real part and magnitude, are directly extracted to establish a base bi-directional long short-term memory model. Secondly, a transfer learning method based on feature matching is designed, which applies a linear transformation layer to map the features between the source and target domains. The proposed transfer learning method has been effectively validated on laboratory battery data measured at different temperatures and retired battery datasets of different material types. The improvements are especially notable for retired batteries. The detection time has been reduced, with each cell requiring only 1.67 min. And using only a small amount of data as input for transfer learning can achieve an accuracy improvement of over 90 %, indicating an effective transfer channel from the base model established on laboratory small-capacity battery aging data to large-capacity retired battery data is successfully established for the first time. For retired nickel-cobalt-manganese batteries, the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) are 2.33 % and 2.75 %, respectively, while for retired lithium-iron-phosphate batteries, the MAPE and RMSPE reached 4.12 % and 5.04 %, respectively. The results demonstrate the proposed method reduces the cost of repeated testing, modeling, and training for specific retired batteries while maintaining the accuracy of capacity estimation. This advancement helps to improve the efficiency of large-scale retired battery grading, and injects new momentum into facilitating more effective decision-making processes.

Street Tree Inventory: A Case Study Comparing Systematic Sampling vs. Stratified Systematic Sampling in Piracicaba City, Brazil

AbstractBackgroundThe inventory of street tree populations has acquired new importance due to interest in the provision of ecosystem services. That said, this paper aims to compare systematic sampling with stratified systematic sampling using different sizes of sampling units to estimate the variables of interest: number of trees per kilometer of sidewalk (DF), basal area per kilometer of sidewalk (Dg), mean total height, volume per kilometer of sidewalk (DV), and number of species per kilometer of sidewalk (DE). An innovative contribution here is testing new alternative density variables.MethodsIn the densely urbanized area of Piracicaba (Sao Paulo State, Brazil), 90 sets of 4 blocks were systematically sampled. They were used to compose sampling units of 1, 2, 3, and 4 blocks. Stratification was based on the percentage of street tree cover obtained with geoprocessing tools. Only public trees with a circumference at breast height greater than or equal to 12 cm and planted on sidewalks or avenue medians were included.ResultsThe effect of sampling unit size and stratification on estimate accuracy, sample size, and sampling intensity were analyzed. The results show that stratified systematic sampling was the more accurate process, especially forDF,Dg, andDV.ConclusionsReductions in sample size were more significant when stratified systematic sampling of 2-block sampling units were used.

Online state of charge estimation of LiFePO4 battery based on EKF-AUKF algorithm with reference compensation for estimation results

The state of charge(SOC) is an important index in Battery Management System(BMS) for smart grids and electric vehicles, whose accuracy is affected by model and sensor errors. In this paper, an online SOC estimation method based on EKF-AUKF algorithm with reference compensation for estimation results is proposed. Firstly, a joint estimation method based on extended Kalman filter(EKF) and adaptive unscented Kalman filter(AUKF) algorithm is adopted to estimate SOC under Beijing bus dynamic stress test(BBDST) condition. The result shows that the estimation error is within 2 %. Secondly, the characteristics of EKF-AUKF algorithm under voltage and current sampling errors are analyzed over the whole SOC range. The results indicate that voltage measurement error increases SOC estimation error. Then, an online reference is built by calculating the sum of the estimation result of ampere-hour counting(AHC) method and constant deviation, and then the difference between the estimation result of EKF-AUKF algorithm and online reference can be obtained. The absolute value of the difference is used to compare with limit deviation value and compensate the SOC of EKF-AUKF method by different compensation coefficients. Finally, the effectiveness of the proposed method is verified under different operating conditions. The experimental results show that the proposed method can greatly improve the SOC estimation accuracy under large voltage error.

Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network

Accurate state-of-health (SOH) estimation is crucial for the lithium-ion battery industry, as it underpins the safety, durability, and reliability of lithium-ion batteries. Currently, most researchers use various methods of health indicator (HI) extraction for the SOH estimation of batteries. However, these methods may require certain expertise and prior knowledge to achieve accurate modeling, being affected by measurement noise and other factors. To solve the abovementioned problems, three Kullback–Leibler (KL) divergence features based on partial voltage sequences are proposed as new HIs that are independent of prior knowledge and strongly correlated with SOH. Moreover, a modified retentive network is proposed to enhance SOH estimation accuracy and better utilize HIs than traditional deep learning methods, which have high training costs and insufficient accuracy. To ensure consistent extraction of KL divergence features across various experimental conditions and time intervals, a B-spline algorithm is utilized for interpolation. The effectiveness of the proposed method is validated through analysis of Pearson correlation coefficients and experiments conducted in four dimensions. Additionally, the potential of using the proposed method to compress data on the cloud-side is explored.

Distribution System State Estimation Based on Power Flow-Guided GraphSAGE

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation.

Lithium battery state of charge estimation based on improved variable forgetting factor recursive least squares method and adaptive Kalman filter joint algorithm

As one of the core functions of the battery management system, battery state of charge (SOC) estimation is crucial to battery life and safety. As the traditional recursive least squares method cannot adapt well to complex variable current conditions, this paper proposes a SOC estimation method that combines the improved variable forgetting factor recursive least squares (IVFFRLS) and the adaptive extended Kalman filter (AEKF). First, IVFFRLS removes the rounding operation of the original algorithm to improve the sensitivity to errors and uses a genetic algorithm to optimize the upper and lower bounds of the variable forgetting factor and the sensitivity coefficient to improve the accuracy of the VFFRLS algorithm. Meanwhile, AEKF introduces a noise covariance adaptive update link based on the covariance matching method to correct the system's process noise Q and measurement noise R in real time to improve the SOC estimation accuracy. Based on the second-order RC equivalent circuit model, the method proposed in this paper has good accuracy and robustness under four different working conditions. The experimental results show that compared with traditional methods, the proposed IVFFRLS-AEKF algorithm has higher SOC estimation accuracy and better robustness.

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

Effects of increasing spatial resolution on the spatial information content and accuracy of downward surface shortwave radiation

Downward Surface Shortwave Radiation (DSSR) plays a crucial role in ecological, hydrological, and solar photovoltaic studies. The estimation of DSSR at higher spatial resolutions is increasingly popular. However, the impact of increasing spatial resolution on spatial information content and accuracy of DSSR estimates is not well understood. Addressing this knowledge gap, an all-sky DSSR dataset at varying spatial resolutions (0.8° to 0.01°) in China was estimated using MODIS top-of-atmosphere reflectance data. Subsequently, the spatial heterogeneity and accuracy of the DSSR dataset as well as its driving factors were analyzed. The results indicate that the magnitude of the increase in spatial heterogeneity varies across different regions as the spatial resolution increases. On the edge of the Qinghai-Tibet Plateau, the spatial heterogeneity experiences the most significant increase. To quantify the factors driving the spatial heterogeneity of DSSR, the Correlation Coefficients (CC) were calculated between the Coefficient Of Variation (COV) of DSSR and the COVs of relevant factors. In cloudy-sky conditions, the CC between the COV of DSSR and cloud optical thickness ranged from 0.54 to 0.62, whereas the CC between DSSR COV and Column Water Vapor (CWV) COV was between 0.72 and 0.78. Under clear-sky conditions, the COV of CWV, aerosol optical depth, and PM2.5 showed more significant correlations with DSSR COV compared to altitude COV. Validation using ground-based measurements revealed that the accuracy of DSSR decreased at coarser spatial resolutions due to the presence of mixed pixel samples. Moreover, it was demonstrated that a spatial resolution of 0.05° offered the highest cost-effectiveness when estimating DSSR. These insights significantly advance our understanding of the role of spatial resolutions in DSSR estimation and are instrumental in guiding the selection of optimal resolutions for such studies.

Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

Model-based humidity observer for vehicle proton exchange membrane fuel cell based on a new sliding mode observer estimation technique

ABSTRACT The internal water content of the proton exchange membrane fuel cell (PEMFC) stack significantly affects its performance and duration. To achieve optimal humidity level, real-time monitoring of the humidity of the cathode and the anode is crucial for the PEMFC. As far as we know, the relative humidity of the PEMFC can be estimated via an observer based on the measurable output voltage. However, the rapid changes in vehicle operating conditions pose a challenge to the estimation accuracy. Different from the existing sliding mode observers (SMOs), this study pays more attention to the testing noise of voltage measuring sensor. To reduce the noise influence and improve the estimation accuracy, a new sliding mode observer (NSMO) combined with an online Kalman filter is presented based on the dimensionless model of the PEMFC in this study. The applicability of the proposed NSMO in estimating the cathode relative humidity and the anode relative humidity is illustrated by the simulation results. At the same time, comparisons between the NSMO and the online Kalman filter are provided which show a substantially higher estimation accuracy and lower cost for the NSMO. Furthermore, the final results indicate the mean absolute deviations of the cathode and the anode relative humidity for the NSMO are, respectively, reduced to 0.39% and 0.28%.

A mathematical modelling for solar irradiance reduction of sunshades and some near-future albedo modification approaches for mitigation of global warming

To address the global warming problem, one of the space-based geoengineering solutions suggests the construction of an occluding disc that can work as a solar curtain to mitigate solar irradiation penetration to the earth atmosphere. A widely discussed concept needs the construction of a large-scale sunshade system near the Sun–Earth L1 equilibrium point in order to control the average global temperature. However, to improve the accuracy of theoretical estimations, more consistent modeling of the Sun-Curtain-Earth system and solar irradiance reduction rate are required. This study revisits the mathematical modeling of the solar irradiance reduction system and considers the fundamentals of shading physics. Simplified mathematical modeling of solar irradiance reduction rate is derived based on the solar flux density. For the climate control, controllability of the reduction rate by using some physical parameters (e.g., flux reflection rate and angle of the curtain) is discussed. Based on the results of this model, the technical challenges and feasibility of constructing a sunshade system at L1 Lagrange point are evaluated. Some technologically feasible, near-future options for the warming problem are discussed briefly.

Target localization algorithm based on improved short-time Fourier transform

The properties of Doppler through-wall radar (TWR) in target localization applications mainly depend on the accuracy of instantaneous frequency (IF) estimations obtained from the time-frequency distribution (TFD) of target echo. However, when the time-frequency aliasing occurs in TFD, it is difficult to accurately estimate the target IF curves. To solve this problem, an improved short-Time Fourier transform is proposed in this paper. Firstly, a series of rough estimation time-frequency information (IF and chirp rate) of target echoes are obtained by fractional Fourier transform. Secondly, we update the time-frequency window according to the rough time-frequency information of the target signal. Meanwhile, we define a new distribution estimator based on the local maximum. Finally, the target IF curve is obtained by the local maximum operator as well as the updated window function. The experimental results show that the method significantly improves the target IF estimation and localization accuracy by 76.5 % and 74.6 %, respectively, compared with STFT.

Detector configuration optimization method considering response time for spatial source parameter estimation in complex urban wind environment

In source parameter estimation, improper arrangement of detectors may lead to ineffective concentration measurement or even failure in leakage detection, which would hold back emergency response efforts. However, current methods for optimizing detector configuration have inherent flaws and thus cannot be ideally applied in real-world scenarios. To address their limitations, this paper develops the method for optimizing detector configuration by considering response time. A novel cost function is proposed to balance response time and estimation accuracy based on specific demands. The optimum configuration is then obtained by genetic algorithm iteration. The wind tunnel test of an urban environment with buildings of varying heights is conducted for the research purpose. The superiority of the proposed method is demonstrated by comparing spatial source parameter estimations using optimum, uniform, and random configurations respectively. The results show that our method avoids placing detectors at locations with low concentrations, i.e., places where readings are invalid. It enables more detectors to obtain readings quickly, generating more accurate estimations in a shorter time. Only with the optimum configuration do detectors show readings for all 15 sources, with each unknown source having the highest number of detectors with readings. In contrast, the uniform configuration results in no readings for 4 sources and the random configuration results in no readings for 11 sources when leaking happens. The proposed method optimizes the detector configuration in three-dimensional space with complex flows, making it more feasible in practical applications.

Performance assessment of satellite rainfall estimates in rain detection capabilities at different thresholds over Nigeria

In Nigeria, where rainfall plays a pivotal role in agriculture and disaster management, assessing the accuracy of satellite rainfall estimates at various thresholds is imperative. This study assesses the areal averages of nine satellite precipitation estimates (SPEs) with 48 ground-based rain gauge observations in Nigeria. Employing categorical statistical metrics and compromise programming, the research assesses the performance of SPEs at seven rainfall thresholds. Results reveal that 82-94% of rainfall estimates align with ground truth, while 5-14% of rainy days are incorrectly detected by rain gauges. Notably, SPEs tend to overestimate low rain between 1mm and 5mm d−1 and underestimate low heavy rain rates (10mm ≤ rain < 20mm), impacting flood monitoring and disaster preparedness. GPCC, TAMSAT-TARCAT, and CHIRPS emerge as more reliable SPEs, particularly in highland regions. This research underscores the importance of choosing appropriate satellite precipitation estimates for enhanced disaster preparedness and resource management across different Nigerian regions.

6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments.

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera's focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.