Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

A Method for the Reduction of Out-of-Band Measurement Errors in Multi-band Instruments using Synthetic Source Distributions

A method to reduce multi-band sensor measurement biases due to finite out-of-band response is described. The method takes advantage of the fact that out-of-band measurement errors cancel if the calibration source and the measured source have the same spectral distributions—independent of their spectral distributions or the magnitude of a sensor band’s out-of-band response. Using a known spectral responsivity, a synthetic, arbitrary source spectral distribution can replace a realized spectral distribution in the measurement equation and the signal can be calculated rather than measured. Given the freedom to select any arbitrary distribution for the synthetic source, the efficacy of the approach depends on the fidelity of the replication of the measured spectrum by the synthetic source spectrum. To illustrate the method, an example application is given of top-of-the-atmosphere measurements of water-leaving radiance by multi-band filter radiometers on celestial Earth-viewing sensors.

Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

Is 7-days home BP measurement comparable to 24-hours Ambulatory BP Measurement?

High blood pressure (BP) or hypertension is a significant risk factor for the global burden of cardiovascular diseases. Home blood pressure measurements (HBPM) have been recommended for hypertension diagnosis, treatment initiation and medication titration, but guidelines for the number of measurements and duration are inconsistent. This study compared the accuracy of 3 home BP measurements per day for seven days with 24-hour ambulatory BP measurements. We examined 24-hour ambulatory BP measurements (ABPM) and HBPM during-morning, afternoon, and evening each day for seven days in healthy community living volunteers. Standardized Bland-Altman scatterplots and limits of agreement (LOA) were used to assess absolute reliability and the variability of measurement biases. We used nonparametric Mann-Whitney U-tests to compare the mean (SD) of the devices. Correlations between HBPM and 24-hour ABPM measurements were statistically significant at p<0.05. The high correlation coefficient (r=0.75) was observed between the systolic BP retrieved from two devices compared to moderate correlation (r=0.46) among diastolic BP. A significant difference was found for systolic BP (P<0.05) between the HBPM and ABPM but was non-significant for diastolic BP (P>0.05). In Bland-Altman plots, the LOA between HBPM and ABPM was 0.07-26.23 mmHg for SBP and 11.24 -16.20 mmHg for DBP. The overall mean difference (bias) in SBP and DBP was 13.08 and 2.48, respectively. Our results suggest that HBPM three times per day for seven days can potentially be used where ABPM is unavailable. Further studies in a diverse group of people with hypertension are needed.

Adjusting for Measurement Noninvariance With Alignment Optimization in Growth Modeling

Longitudinal measurement invariance, the consistency of measurement in data collected over time, is a prerequisite for any meaningful inferences of growth patterns. When one or more items measuring the construct of interest shift its measurement properties over time, it leads to biased parameter estimates and inferences on the growth parameters. In this paper, we extended the recently-developed alignment optimization (AO) technique to adjust for measurement biases for growth models. The proposed AO method does not require identification of noninvariant items, and it can adjust for measurement biases even when all items are mild to moderately biased. We demonstrate how the proposed method can be implemented in the R statistical language using a textbook example, and conduct a Monte Carlo simulation study to compare its performance with the partial invariance modeling method. The simulation results show that alignment largely reduces biases in growth parameters and gives better control of Type I error rates, especially when the sample size is at least 1,000. It also outperforms the partial invariance method in conditions when all items are noninvariant. Based on the simulation results, we conclude that AO is a viable alternative to the partial invariance method in growth modeling when it is not clear whether longitudinal measurement invariance holds. Future research can further explore the potential of AO in other longitudinal models, such as alternative growth shapes and change score models.

Point-to-point ICESat-2 vs CryoSat-2 comparison

&lt;p&gt;For measuring the mass balance of mountain glaciers, the combination of ICESat-2 and CryoSat-2 data holds the potential to improve both spatial and temporal coverage and measurement quality. Both satellite missions are dedicated to measuring the surface elevation in the cryosphere and complement each other. While CryoSat-2 brings a high spatiotemporal coverage, ICESat-2 measures at high accuracy. CryoSat-2 on its own suffers from measurement biases because its radar waves partly penetrate the firn layer and because of the size of its footprint. The laser altimeter ICESat-2, on the other hand, cannot provide the desired spatiotemporal coverage at all locations. By combining the data, we gain surface elevation estimates with a reduced bias (for the CryoSat-2 data) and an improved coverage at the same time. A combined dataset could provide us with better insight into the effects of extreme weather events and the impact of climate change on glacier dynamics on a sub-regional scale and its influence on the inter-seasonal variability. Here, we present insights into the bias of CryoSat-2 data, which we retrieve using different swath processors, with regards to the ATL06 ICESat-2 data. We compare the data point-to-point to study how the surface conditions influence the observed bias for two test regions featuring a complex topography and a high track density.&lt;/p&gt;

On Neglecting Free-Stream Turbulence in Numerical Simulation of the Wind-Induced Bias of Snow Gauges

Numerical studies of the wind-induced bias of precipitation measurements assume that turbulence is generated by the interaction of the airflow with the gauge body, while steady and uniform free-stream conditions are imposed. However, wind is turbulent in nature due to the roughness of the site and the presence of obstacles, therefore precipitation gauges are immersed in a turbulent flow. Further to the turbulence generated by the flow-gauge interaction, we investigated the natural free-stream turbulence and its influence on precipitation measurement biases. Realistic turbulence intensity values at the gauge collector height were derived from 3D sonic anemometer measurements. Large Eddy Simulations of the turbulent flow around a chimney-shaped gauge were performed under uniform and turbulent free-stream conditions, using geometrical obstacles upstream of the gauge to provide the desired turbulence intensity. Catch ratios for dry snow particles were obtained using a Lagrangian particle tracking model, and the collection efficiency was calculated based on a suitable particle size distribution. The collection efficiency in turbulent conditions showed stronger undercatch at the investigated wind velocity and snowfall intensity below 10 mm h−1, demonstrating that adjustment curves based on the simplifying assumption of uniform free-stream conditions do not accurately portray the wind-induced bias of snow measurements.

Impact of DIC biases on the selection process of a unique test for anisotropic plasticity characterization

The exploitation of field measurements with inverse identification methods may reduce the number of required tests to characterize complex material constitutive models, provided that the generated stress field is sensitive enough to the targeted material parameters. For anisotropic elastoplastic material, the objective is to generate various stress states in the specimen through a single test. In this study, the effect of Digital Image Correlation measurement biases on the selection of the most suitable specimen geometry for characterisation of a complex anisotropic plasticity criterion using a unique uniaxial tensile test is investigated. To this aim, finite element (FE) based synthetic images are generated and DIC is used on these images. The biases in DIC measurement result in biased stress states that may cause errors in identification results.