Covariance Matching Research Articles

Cost Utility Analysis of Multidisciplinary Postacute Care for Stroke: A Prospective Six-Hospital Cohort Study.

BackgroundFew studies have compared the optimal duration and intensity of organized multidisciplinary neurological/rehabilitative care delivered in a regional/district hospital with the standard rehabilitative care delivered in the general neurology/rehabilitation ward of a medical center. This study measured functional outcomes and conducted cost-utility analysis of an organized multidisciplinary postacute care (PAC) project in secondary care compared with standard rehabilitative care delivered in tertiary care.MethodsThis prospective cohort study enrolled 1,476 patients who had a stroke between March 2014 and March 2018 and had a modified Rankin scale score of 2–4. After exact matching for age ± 1 year, sex, year of stroke diagnosis, nasogastric tube, and Foley catheter and propensity score matching for the other covariates, we obtained 120 patients receiving PAC (the PAC group) from four regional/district hospitals and 120 patients not receiving PAC (the non-PAC group) from two medical centers.ResultsAt baseline, the non-PAC group showed significantly better functional outcomes than the PAC group, including EuroQol-5 dimensions (EQ-5D), Mini-Mental State Examination (MMSE) and Barthel index (BI). During weeks 7–12 of rehabilitation, improvements in all functional outcomes were significantly larger in the PAC group (P < 0.001) except for Functional Oral Intake Scale (FOIS). Cost-utility analysis revealed that the PAC group had a significantly lower mean (± standard deviation) of direct medical costs (US$3,480 ± $1,758 vs. US$3,785 ± $3,840, P < 0.001) and a significantly higher average gain of quality-adjusted life years (0.1993 vs. 0.1233, P < 0.001). The PAC project was an economically “dominant” strategy.ConclusionsThe PAC project saved costs and significantly improved the functional outcomes of patients with stroke with slight to moderately severe disabilities. Randomized control trials are required to corroborate these results.

Phase Transition Analysis for Covariance-Based Massive Random Access With Massive MIMO

This paper considers a massive random access problem in which a large number of sporadically active devices wish to communicate with a base station (BS) equipped with massive multiple-input multiple-output (MIMO) antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem for which the sample covariance matrix of the received signal is a sufficient statistic. The goal of this paper is to characterize the feasible set of problem parameters under which this covariance based approach is able to successfully recover the device activities in the massive MIMO regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing probability of detection error as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching, which relates the phase transition analysis to a recently derived scaling law. Further, we provide a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way.

Abstract OT2-19-04: European treatment patterns and outcomes associated with first-line CDK4/6 inhibition and hormonal therapies assessed in a real-world non-interventional study (EUCHARIS)

Abstract Background: Palbociclib is currently the most commonly used cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) for patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) in Europe since its approval in 2017. However, large-scale assessments of palbociclib utilization and subsequent clinical outcomes in real-world settings are limited. Documentation of longitudinal treatment patterns and effectiveness following initiation of first-line (1L) palbociclib + aromatase inhibitor (AI) in more heterogeneous, real-world patient populations are needed to complement clinical trial findings. Trial Design: This is an observational cohort study using retrospective data abstracted from the medical records of patients with HR+/HER2- ABC who initiated 1L treatment (index date) with palbociclib + AI (September 2016-July 2020) or AI monotherapy (January 2010 to July 2020) as 1L therapy for ABC. Patients' study index dates will be at least 12 months before the data abstraction date to allow a minimum potential follow-up of at least 12 months. Patients who died within &amp;lt; 12 months after the index date will remain eligible. Eligibility Criteria: Adult patients who received a de novo or a recurrent diagnosis of locally advanced BC (i.e., not amenable to curative therapy) or metastatic BC will be included. Patients who participated in a clinical trial for ABC, had evidence of prior other malignant neoplasm, received any CDK4/6i for early stage disease, and received other CDK4/6i for ABC will be excluded. Study Aims: This study aims to describe patient characteristics, treatment patterns, and clinical outcomes among patients with HR+/HER2- ABC in Europe who received 1L CDK4/6i treatment. Key outcomes of interest include real-world tumor response, real-world progression-free survival, and overall survival. An exploratory objective will be to compare clinical outcomes between patients treated with 1L palbociclib + AI versus AI monotherapy. Statistical Methods: All study measures will be summarized descriptively. Time-to-event outcomes will be estimated using the Kaplan-Meier method. Direct covariate matching at the site-level and inverse probability weighting using propensity scores is planned to account for differences in baseline patient and clinical characteristics between treatment groups in the exploratory comparative analysis. Target Accrual: Data abstraction is anticipated to begin in September 2021. Data will be abstracted for up to 2,400 eligible patients. Approximately 60 total sites from across Germany, Spain, and the United Kingdom and, if feasible, from other European countries will be recruited. Citation Format: Ernest H. Law, Elena Galve-Calvo, Achim Wöckel, Rohan Parikh, Samantha K. Kurosky, Valerie Derrien Ansquer, Gaelle Frugier, Keith L. Davis, Elizabeth J. Esterberg, Olga Oikonomidou. European treatment patterns and outcomes associated with first-line CDK4/6 inhibition and hormonal therapies assessed in a real-world non-interventional study (EUCHARIS) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT2-19-04.

Survival benefit of thermal ablation combined with chemotherapy for the treatment of stage IV nonsmall cell lung cancer: a propensity-matched analysis

Objective To investigate the survival benefit of thermal ablation (TA) plus chemotherapy for Stage-IV nonsmall cell lung cancer (NSCLC). Methods From the Surveillance, Epidemiology and End Results (SEER) database, data of Stage-IV NSCLC patients receiving different treatment modalities (TA plus chemotherapy vs. chemotherapy) from 2004 to 2016 were retrospectively analyzed using propensity-score matching (PSM) for covariates. Kaplan–Meier curves and the log-rank test for intergroup comparison of overall survival (OS) and lung cancer-specific survival (LCSS) and subgroup analyses in the PSM cohort evaluated possible survival benefits. Cox proportional risk models evaluated independent prognostic factors. Results Among 52,574 patients, 152 received TA plus chemotherapy. After PSM, the TA plus chemotherapy and chemotherapy groups included 150 and 445 patients, respectively. Compared to the chemotherapy group, the TA plus chemotherapy group had better OS (p = 0.042) and LCSS (p = 0.031), especially in patients aged 70 and older in age-stratified subgroup analysis; no statistically significant beneficial trend was noted for patients younger than 70 years. Subgroup analysis by tumor size showed superior OS and LCSS with TA plus chemotherapy than chemotherapy for tumors ≤3.0 cm; however, no significant difference was found in subgroups with larger tumors. Multivariate analysis showed that TA plus chemotherapy was an independent prognostic factor for OS and LCSS (hazard ratio 0.70 [95% confidence interval 0.59–0.84] and 0.70 [0.58–0.84], respectively; p < 0.001). Conclusion TA plus chemotherapy is a potential treatment option for Stage-IV NSCLC, especially for patients aged 70 or older with tumor size ≤3 cm.

On the Method of Polarimetric SAR Calibration Using Distributed Targets

In the companion paper (Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020), we identified two types of calibration models (CMs) that have been widely used in polarimetric calibration algorithms with distributed targets. An optimal method based on the covariance matching estimation technique (COMET), which we refer to as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> method, was used therein but without proof of its optimality. This article supplies the details on parameter estimation using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> method and proves that it is optimal in the mean-squared sense: No other estimate has a smaller mean-squared error. For data affected by the Faraday rotation (FR), the feasibility of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> method is analyzed. Numerical experiments are presented to compare the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> method with existing methods and prove its optimality. The differences between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> method and another COMET-based calibration method are discussed. Moreover, we prove that estimating the distortion parameters while preserving the orientation angle is impossible, contrary to what is found by Ainsworth <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2006).

Verifying the Benefits of Radical Treatment in Posttransplant Hepatocellular Carcinoma Oligo-recurrence: A Propensity Score Analysis.

This study verified whether radical treatment for hepatocellular carcinoma (HCC) oligo-recurrence after liver transplantation conveys survival benefits. A retrospective study of 144 patients with posttransplant HCC recurrence was performed. Propensity score matching was performed to adjust for baseline covariates between patients who received radical and palliative treatments. The primary endpoint was postrecurrence survival. A total of 50 patients (35%) received radical treatment for recurrence, and 76 (53%) and 18 (13%) patients received palliative and supportive treatments, respectively. Compared with the radical group, patients who received palliative treatment had more early recurrences (time from transplant 17 versus 11months; P=0.01) and more extensive disease in terms of tumor numbers (1 versus 4; P<0.001), size of largest tumor (1.8 versus 2.5cm; P=0.046), numbers of involved organs (interquartile range [IQR], 1-1 versus 1-2; P=0.02), and alpha-fetoprotein (AFP) level (7 versus 40ng/mL; P=0.01). Multivariate Cox regression analysis revealed that early recurrence (time from transplant hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.01-1.03; P=0.001), larger recurrent tumor (HR, 1.12; 95% CI, 1.03-1.23; P=0.01), liver recurrence (HR, 1.84; 95% CI, 1.17-2.90; P=0.01), and log10 AFP level at recurrence (HR, 1.27; 95% CI, 1.07-1.52; P=0.01) predicted poor survival. Mammalian target of rapamycin inhibitor (HR, 0.331; 95% CI, 0.213-0.548; P<0.001) and radical treatment (HR, 0.342; 95% CI, 0.213-0.548; P<0.001) were associated with improved survival. After 2-to-1 propensity score matching for covariates, the 50 patients who received curative treatment survived significantly longer than the 25 matched patients who received palliative treatment (median survival time, 30.9±2.4 versus 19.5±3.0months; P=0.01). Radical treatment conveys survival benefits to HCC oligo-recurrence after liver transplantation.

Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

As for the cell-to-cell inconsistency of packing lithium-ion batteries, accurate equivalent modeling plays a significant role in the working characteristic monitoring and improving the safety protection quality under complex working conditions. In this work, a novel covariance matching–electrical equivalent circuit modeling method is proposed to realize the adaptive working state characterization by considering the internal reaction features, and an improved adaptive weighting factor correction-differential Kalman filtering model is constructed for the iterative calculation process. A new parameter named state of balance is introduced to describe the cell-to-cell variation mathematically by forming an effective influence correction strategy. An adaptive covariance matching method is investigated to update and transmit the noise matrix for high-power energy supply conditions, in which the weighting factor correction is conducted by considering the coupling relationship to improve the prediction accuracy. Experimental tests are conducted to verify the estimation effect, in which the closed-circuit voltage responds well corresponding to the battery state variation. The maximum closed-circuit voltage traction error is 1.80%, and the maximum SOC estimation error for packing lithium-ion batteries is 1.114% for the long-term experimental tests with the MAE value of 0.00481 and RMSE value of 5.44085E-5. The improved covariance matching-electrical equivalent circuit modeling method provides a theoretical foundation for the reliable application of lithium-ion batteries.

On efficient gridless methods for 2-D DOA estimation with uniform and sparse L-shaped arrays

In recent years, some gridless methods have been developed for two-dimensional direction-of-arrival (DOA) estimation due to their superiority over on-grid or off-grid methods. However, the existing gridless methods treat every sparse planar array as a generalized planar array, regardless of some particular geometries of the arrays, e.g., the L-shaped arrays. Moreover, computational efficiency is another concern in designing gridless methods. In this paper, we propose two gridless methods for 2-D DOA estimation in the cases of both uniform and sparse L-shaped arrays by exploiting the cross-covariance matrix (CCM) of the array output. The first one is formulated based on the concept of atomic norm while the second one is developed by employing the covariance matching criterion. It is shown that the two methods become identical in uniform L-shaped array while exhibit different properties in sparse L-shaped array. Two computationally efficient algorithmic implementations are provided to make the proposed methods practically feasible. We finally carry out extensive experiments to verify the properties of our proposed methods.

High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter.

Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.

Mid-term outcomes of off-pump versus on-pump coronary artery bypass graft surgery; statistical challenges in comparison

BackgroundDespite several studies comparing off- and on-pump coronary artery bypass grafting (CABG), the effectiveness and outcomes of off-pump CABG still remain uncertain.MethodsIn this registry-based study, we assessed 8163 patients who underwent isolated CABG between 2014 and 2016. Propensity score matching (PSM), inverse probability of weighting (IPW) and covariate adjustment were performed to correct for and minimize selection bias.ResultsThe overall mean age of the patients was 62 years, and 25.7% were women. Patients who underwent off-pump CABG had shorter length of hospitalization (p < 0.001), intubation time (p = 0.003) and length of ICU admission (p < 0.001). Off-pump CABG was associated with higher risk of 30-days mortality (OR: 1.7; 95% CI 1.09–2.65; p = 0.019) in unadjusted analysis. After covariate adjustment and matching (PSM and IPW), this difference was not statistically significant. After an average of 36.1 months follow-up, risk of MACCE and all-cause mortality didn’t have significant differences in both surgical methods by adjusting with IPW (HR: 1.03; 95% CI 0.87–1.24; p = 0.714; HR: 0.91; 95% CI 0.73–1.14; p = 578, respectively).ConclusionOff-pump and on-pump techniques have similar 30-day mortality (adjusted, PSM and IPW). Off-pump surgery is probably more cost-effective in short term; however, mid-term survival and MACCE trends in both surgical methods are comparable.

Matching-adjusted indirect treatment comparison of onasemnogene abeparvovec and nusinersen for the treatment of symptomatic patients with spinal muscular atrophy type 1

Objective Onasemnogene abeparvovec, a one-time intravenous gene replacement therapy, and nusinersen, an antisense oligonucleotide that requires ongoing intrathecal administration, have been evaluated as treatments for spinal muscular atrophy (SMA) type 1 in separate Phase III trials, but no head-to-head comparison studies have been conducted. Onasemnogene abeparvovec was compared with nusinersen using a matching-adjusted indirect comparison (MAIC) to estimate the treatment effect of onasemnogene abeparvovec relative to nusinersen for the treatment of symptomatic patients with SMA type 1 for up to 24 months of follow-up. Methods In the absence of studies for both onasemnogene abeparvovec and nusinersen with a common comparator, a Bayesian naïve indirect treatment comparison (ITC) and MAIC between onasemnogene abeparvovec and nusinersen were conducted to compare efficacy and safety of onasemnogene abeparvovec with nusinersen. Outcomes of interest were event-free survival (EFS), overall survival (OS), and motor milestone achievements (independent sitting and independent walking). Relative treatment effects were expressed as relative risk (RR) and risk difference. Results Pooled and weighted patient-level data illustrated a favorable effect toward onasemnogene abeparvovec, suggesting longer EFS for patients compared with nusinersen (HR of onasemnogene abeparvovec vs. nusinersen: 0.19 [95% CI: 0.07–0.54; 99% CI: 0.05–0.74]). At 24 months of follow-up, patients receiving onasemnogene abeparvovec were statistically significantly more likely to achieve the motor milestone of sitting independently compared with patients treated with nusinersen. Although statistically significant differences were not observed at 6 to 18 months between treatment options, the likelihood of sitting independently at 12 and 18 months numerically favored onasemnogene abeparvovec. A numerically greater likelihood of walking by 18 and 24 months was also observed for patients treated with onasemnogene abeparvovec compared with nusinersen. Onasemnogene abeparvovec therapy was also associated with a favorable (but statistically nonsignificant) outcome for OS and may be associated with prolonged survival compared with nusinersen (HR of onasemnogene abeparvovec vs. nusinersen: 0.35 [95% CI: 0.09–1.32; 99% CI: 0.06–2.01]). Bayesian naïve ITC results were similar to the MAIC analysis for EFS, OS, and motor milestone achievements. Small sample size limited covariate matching to baseline CHOP INTEND and nutritional support requirement, leading to wider CIs and statistically inconclusive outcomes for some of the results. Conclusions Despite limitations of the current MAIC analysis (mainly a small sample size for statistical testing, even for the pooled onasemnogene abeparvovec trials, and potential differences in prognostic and predictive factors between studies), the relative treatment effects in EFS, OS, and motor milestone achievement indicate that onasemnogene abeparvovec may offer continued benefit compared with nusinersen through 24 months of follow-up.

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

To improve the space-time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target-free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non-homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi-channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.

Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination.

This paper introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semi-definite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.

A case study of space-time performance comparison of wind turbines on a wind farm

This paper presents an academia-industry joint case study, which was conducted to quantify and compare multi-year changes in power production performance of multiple turbines scattered over a mid-size wind farm. This analysis is referred to as a space-time performance comparison. One key aspect in power performance analysis is to have the wind and environmental inputs controlled for. This research employs, in a sequential fashion, two principal modeling components to exercise tight control of multiple input conditions—a covariate matching method, followed by a Gaussian process model-based functional comparison. The analysis method is applied to a wind farm that houses 66 turbines on a moderately complex terrain. The power production and environmental data span nearly four years, during which period the turbines have gone through multiple technical upgrades. The space-time analysis presents a quantitative and global picture showing how turbines differ relative to each other as well as how each of them changes over time.

High Angular Resolution for 77GHz FMCW Radar via a Sparse Weighted Quadratic Minimization

Combining the covariance matching criterion with sparse representation, much effort was devoted to improve the angular resolution. However, the requirement of hyperparameters or the high sidelobe makes them unsatisfactory in practical radar applications. In this paper, we propose a Sparse Capon-like Weighted Quadratic Estimator (SCWQE) with hyperparameter-free and apply it to a 77GHz Frequency Modulated Continuous Wave (FMCW) radar for the high-resolution Direction Of Arrival (DOA) estimation. Under the assumption of the uncorrelated sources, SCWQE is formulated by converting the covariance matching criterion to a quadratic function with respect to the unknown power spectrum, in which a symmetrical and non-diagonal matrix called as the Capon-like spectrum is embedded into the quadratic form. The consequent multiple weighting operation would further promote the sparsity because multiple and different weighting values are exerted on each element of the spatial power spectrum. This is fundamentally different from the traditional weighted approach that employs the diagonal weighting matrix and assigns a single weighting value to each element. Therefore, SCWQE could further eliminate the spurious peaks and sharpen the desired peaks. Numerical examples including simulations and actual data collected from a 77GHz FMCW radar sensor demonstrate that, the proposed SCWQE algorithm produces better accuracy of DOA estimation and higher angular resolution compared to some typical sparse recovery methods.

A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider

In order to improve the accuracy of a single low-cost attitude observation sensor of the wave glider (WG), a new multi-sensor hierarchical data fusion algorithm is proposed. The bottom-level fusion strategy is the complementary data fusion of the accelerometer and the magnetometer, and the global optimal estimation of the bottom-level fusion result and the gyroscope based on the unscented Kalman filter (UKF) algorithm in the top-level fusion strategy. However, the estimation accuracy of the standard UKF algorithm depends on the accuracy of the noise model. Inaccurate estimation of the covariance matrix of the state noise and the measurement noise will cause the accuracy of the UKF algorithm to decrease or even diverge. By introducing the covariance matching technology, the innovation sequence and the residual sequence are used to realize the real-time estimation of the covariance matrix of state noise and measurement noise. At the same time, in view of the characteristic that the measurement equation is a strongly nonlinear equation, an improved covariance matching adaptive UKF algorithm is proposed. It effectively overcomes the limitations of the standard UKF and improves the adaptability of the multi-sensor hierarchical data fusion algorithm. Numerical simulation and experiment are carried out and the results demonstrate that the multi-sensor hierarchical data fusion algorithm is effective in solving the precision problem of the WG low-cost attitude observation unit.

Adaptive Central Difference Kalman Filter With Unknown Measurement Noise Covariance and Its Application to Airborne POS

Position and Orientation system (POS), a loosely integrated inertial navigation system (INS) and global positioning system (GPS), can provide high-accuracy motion information for the airborne remote sensing loads, which plays a crucial role in airborne remote sensing imaging. However, the airborne POS often suffers from the harsh environment, such as aircraft maneuver mode and other external disturbance, which will lead to measurement noise unknown and further affects the accuracy of motion parameters. In this paper, an adaptive central difference Kalman filter method based on expectation maximization algorithm is proposed, which can estimate measurement noise adaptively and further improve the performance of POS. A flight experiment is conducted and the results show that the proposed method achieves higher-accuracy motion information by compared with the traditional CDKF method and covariance matching.

Adaptively Robust Square-Root Cubature Kalman Filter Based on Amending

To solve the problem of decreased filtering accuracy and even filter divergence for the case that the model errors and measurement outliers exist simultaneously, an adaptively robust square-root cubature Kalman filter (SRCKF) based on amending is proposed in this paper. Based on the error analysis, the judgment criterion and the amending criterion related to the innovation are set. Then the filter could overcome the influence of the measurement outliers with the robust amendment of the measurement noise covariance matrix based on the principle of the innovation covariance matching. To further deal with model errors, the new method of the adaptive amendment of the predicted state based on the innovation is developed and combined. Finally, the proposed algorithm can balance the effect of the prior predicted value and the posterior feedback measurement value on the filtering process and reduce the state estimation error. The simulation results show that the proposed algorithm can effectively suppress the negative impact of the model errors and the measurement outliers and can obtain better estimation performance and obviously decreasing running time compared with the adaptively robust unscented Kalman filter (ARUKF) and the robust multiple fading factors cubature Kalman filter (RMCKF).

Impact of a Media Campaign on Consumers’ Purchasing Intentions of Legal Timber in Cameroon

Illegal logging is a key driver of deforestation in tropical countries and benefits a large number of stakeholders, including urban domestic buyers. Changing the latter’s purchasing behavior could produce significant shifts in the illegal trade market. Based on survey data (n= 463), this paper assesses the impact of a media campaign on consumers’ purchase intentions for legal timber and wooden furniture in Yaoundé and analyses the underlying psychological mechanisms. Combining covariate matching and mediation analysis, we found a significant positive effect of the media campaign on the declared intention to purchase legally-sourced timber and the mediation effect of two out of three precursors of the theory of planned behaviors, namely attitude and subjective norms. This study provides critical inputs to frame future media campaign messages and, by demonstrating some consumers’ interest in legal timber, to strengthen the domestic legitimacy of policies such as the Forest Law Enforcement, Governance and Trade.

Does an Income Gap between Farm and Nonfarm Households Still Exist? The Case of the European Union

AbstractThe paper compares the income conditions of farm and nonfarm households in the whole EU and within three geographical groups of countries for the period 2008–2016. Overcoming the simple comparison of raw means of the groups, we estimate the farm/nonfarm income differentials by using Regression Adjusted and Covariate Matching techniques, which allow to control for observable characteristics among groups. Three innovative features of our analysis are that we account for the whole income of farm households (i.e. not only farm income), for the presence of in‐kind incomes from self‐consumption of produced goods and imputed rents from properties, and for the complex survey design. We find that an income differential still exists but with relevant differences across countries and along the period. Most of it is due to differences in the households’ characteristics. Hence, comparing raw means of the two groups can be misleading. Nonmonetary sources of income play a not negligible role, improving the relative position of farm households. The role of agricultural and rural policy is discussed in the light of results.