Sensor Performance Model Research Articles

Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results

UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values.

(Digital Presentation)Empirical Methods for Modeling NOx Sensor Performance Errors in Combustion Applications

Combustion technologies require gas sensors for monitoring and controlling the exhaust and to meet increasingly stringent environmental regulations. NOx compounds (NO and NO2) in diesel exhaust can lead to poor air quality and act as both pollutants and greenhouse gases. Aftertreatment strategies have been developed to mitigate NOx and usually require information about the NOx sensor output. The design and operation of aftertreatment strategies rely on an understanding of sensor behavior and deviations. However, sensor specifications provided by suppliers typically only reflect the minimum requirements necessary to meet basic application needs. Supplier information about sensor performance can therefore be too general whereas more detailed information could allow more flexibility and potentially cost savings during early development of the overall aftertreatment system and strategy.The approach and application of a methodology for predicting nominal sensor output and standard deviation is presented. The prediction is in the form of an empirical NOx sensor performance model that relies on experimental data for a specified population of sensors. Inputs include the concentration of exhaust components (e.g., NO, NO2, O2, etc.), physical state (e.g., temperature, pressure, etc.), as well as additional information about sensor condition and location (e.g., as-received brand-new sensors, tailpipe position, etc.). The output would be a dynamic real-time estimate for the NOx sensor broadcast signal and standard deviation for the defined population. Experimental data are primarily collected using a laboratory high-flow test bench that simulates exhaust conditions.We present an approach that includes understanding exhaust conditions to determine the initial factors of interest as well as reasonable ranges of values for those factors. We then investigate the correlation of the factors with the measured error of the NOx sensor broadcast value using a design of experiments (DOE) response surface methodology (RSM) with ten factors. The population was defined as nine as-received brand-new NOx sensors tested simultaneously in the laboratory high-flow test bench. Least squares fitting with a linear regression model seemed to adequately describe the correlation of mean sensor signal shifts with changes in the ten factors. Methods for validation of the model and planned next steps are discussed.

Cooperative trajectory optimization of UAVs in approaching stage using feedback guidance methods

This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles. Based on the optimal trajectory trends and feedback guidance methods, a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage. Based on UAV kinematics and sensor performance models, optimal trajectory trends of UAVs are analyzed theoretically. Then, feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage, producing trajectories with far less computational complexity and performance very close to the best-known trajectories. Next, the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented, which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage. Finally, the feedback guidance method is numerically simulated. Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field (LGVF) method and verify the effectiveness of the proposed method. Additionally, compared with the receding horizon optimization (RHO) method, the proposed method has the same optimization ability as the RHO method and better real-time performance.

Optimization and modeling of Zn2SnO4 sensitivity as gas sensor for detection benzene in the air by using the response surface methodology

In this paper, the performance of the benzene gas detection sensor in the air is optimized by an experimental design method. So in this work, Nanostructured thin films of ZnO and Zn2SnO4 were prepared in wurtzite form via a facile atmospheric pressure chemical vapor deposition (CVD) method, using metallic zinc and tin precursors. Characterization of the gas sensor was performed by using Powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and surface area analysis (using BET method). The results show that Zn2SnO4 nanowire network exhibited good sensitivity at 299 °C temperature to low concentrations (100 ppb) of Benzene which can be potentially used as a resistive gas sensor. Ultimately modeling and optimization of Zn2SnO4 sensor performance to detect benzene by surface response method in design expert11 software has been done. Also, the effect of each parameter on the sensitivity of the sensor was analyzed by analysis of variance (ANOVA). Moreover, the performance efficiency of the Zn2SnO4 sensor is estimated with the reliable correlations obtained in the modeling. The two parameters selected to optimize the performance of the gas sensor include the operating temperature of the sensor and the concentration of the sensor. Comparison of the modeling results and the predicted values for the sensor sensitivity to benzene shows 97.60% excellent agreement.

On use of image quality metrics for perceptual blur modeling: image/video compression case

Linear system theory is employed to make target acquisition performance predictions for electro-optical/infrared imaging systems where the modulation transfer function (MTF) may be imposed from a nonlinear degradation process. Previous research relying on image quality metrics (IQM) methods, which heuristically estimate perceived MTF has supported that an average perceived MTF can be used to model some types of degradation such as image compression. Here, we discuss the validity of the IQM approach by mathematically analyzing the associated heuristics from the perspective of reliability, robustness, and tractability. Experiments with standard images compressed by x.264 encoding suggest that the compression degradation can be estimated by a perceived MTF within boundaries defined by well-behaved curves with marginal error. Our results confirm that the IQM linearizer methodology provides a credible tool for sensor performance modeling.

Analysis of random sleep scheme for wireless sensor networks

Energy conserving design is the research focus to prolong the lifetime of wireless sensor networks. A simple and effective way to save energy is to place sensor nodes in sleep mode periodically. However, sleep mode corresponds to low power consumption as well as to reduced network capacity and increased latency. This paper develops an analytical framework to study the interaction between random sleep scheme and network performance. Our framework consists of the queueing model for sensor node and performance model for the whole network. We derived the network throughput, power consumption and packet delivery delay. Our analytical models shed light on the guidelines to design random sleep scheme and enable us to explore the trade-offs existing between sensor sleep/active dynamics and those performance measures.

Infrared sensor modeling for human activity discrimination tasks in urban and maritime environments

The United States military is increasingly using infrared (IR) sensors to discriminate the actions and intentions of people being observed in an area of interest; however, there is currently inadequate modeling capability to a priori determine the effectiveness of IR sensors for these types of tasks. The U.S. Army's NVTherm model is a military and industry standard sensor performance model that estimates target acquisition performance based on both the sensor design parameters and an empirically measured calibration factor to represent human observer performance. Historically the model has been calibrated by presenting static imagery to observers and measuring average probabilities of accomplishing the given task. The task of human activity discrimination, however, presents new challenges to the model, as "activity" inherently implies a dynamic scene where motion cues are essential in accomplishing the task. A series of studies has been completed in order to calibrate NVTherm for the task of human activity discrimination. The challenges involved in representing the human activity task, the establishment of new processing methods, and new standards for defining simple target metrics for complex imagery are discussed. The experiments that have supported the calculation of new calibration parameters are also described. These efforts have brought the U.S. Army significantly closer to having a sensor performance model validated for discriminating human activity with IR sensors.

Terahertz imaging system performance model for concealed-weapon identification

The U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) and the U.S. Army Research Laboratory have developed a terahertz (THz) -band imaging system performance model for detection and identification of concealed weaponry. The MATLAB-based model accounts for the effects of all critical sensor and display components and for the effects of atmospheric attenuation, concealment material attenuation, and active illumination. The model is based on recent U.S. Army NVESD sensor performance modeling technology that couples system design parameters to observer-sensor field performance by using the acquire methodology for weapon identification performance predictions. This THz model has been developed in support of the Defense Advanced Research Project Agencies' Terahertz Imaging Focal-Plane Technology (TIFT) program and is currently being used to guide the design and development of a 0.650 THz active-passive imaging system. This paper will describe the THz model in detail, provide and discuss initial modeling results for a prototype THz imaging system, and outline plans to calibrate and validate the model through human perception testing.

Midwave infrared and visible sensor performance modeling: small craft identification discrimination criteria for maritime security

The new emphasis on Anti-Terrorism and Force Protection (AT/FP), for both shore and sea platform protection, has resulted in a need for infrared imager design and evaluation tools that demonstrate field performance against U.S. Navy AT/FP requirements. In the design of infrared imaging systems for target acquisition, a discrimination criterion is required for successful sensor realization. It characterizes the difficulty of the task being performed by the observer and varies for different target sets. This criterion is used in both assessment of existing infrared sensor and in the design of new conceptual sensors. We collected 12 small craft signatures (military and civilian) in the visible band during the day and the long-wave and midwave infrared spectra in both the day and the night environments. These signatures were processed to determine the targets' characteristic dimension and contrast. They were also processed to band limit the signature's spatial information content (simulating longer range), and a perception experiment was performed to determine the task difficulty (N50 and V50). The results are presented and can be used for Navy and Coast Guard imaging infrared sensor design and evaluation.

Full-field likelihood ratio detection for multistatic sensors in uncertain environments

As small networked sonobuoys become cheaper and more viable in the submarine warfare community, there is a growing demand for techniques to automatically process detection data from dense fields containing O(100) or more sonobuoys. The presentation describes a Bayesian framework for the incoherent integration of automatic detections from these dense sonobuoy fields. This framework recursively integrates likelihood ratio functions to form a cumulative posterior over a target state space. To be most effective, these methods exploit either empirical sensor performance models or physics-based signal excess sensor performance models. The presentation describes the use of each type of performance model and presents one method for handling uncertainties associated with signal excess predictions. The method presented starts with a prior on a set of possible environmental conditions. When a target is present in the detection data, the cumulative likelihood ratio will peak in the environment most consistent with the true environment.

TOD performance theoretical model for scanning infrared imagers

Since the triangle orientation discrimination threshold (TOD) measure was proposed, good progress has been made in some aspects, such as experimental validation, field performance, and TOD sensor performance model. However, what is still missing is a TOD curve prediction model similar to the minimum resolvable temperature difference (MRTD) equation. In this paper, an equation-based TOD performance model was first developed for scanning thermal imagers. Specifically, the spatial distribution of the triangle standard test pattern is described quantitatively. The resolution transfer characteristic of the scanning infrared imagers is modeled with linear system theory, and system noise is characterized by introducing a three-dimensional noise model. The modulation effect of overall subsystems of scanning thermal imager on the non-periodic standard triangle pattern is analyzed. The matched filter idea is adopted to characterize quantitatively the spatial–temporal integration of the human visual system to signal and various noise components over the triangle pattern area, and the perceived signal-to-noise ratio for the scanning infrared imager is derived. From this, a TOD performance theoretical model is established. Comparisons with experimental results show that this theoretical model gives reasonable prediction of the TOD performance curve for scanning thermal imagers. Although more tests and modifications are required, these preliminary results suggest that this model can be developed into a model that predicts the TOD for all kinds of sensors.

Equation-based triangle orientation discrimination sensor performance model based on sampling effects

An equation-based triangle orientation discrimination (TOD) performance model was first developed to focus on staring thermal imagers. Specifically, the spatial spectra distribution of a standard triangle pattern is determined. The modulation effects of the overall components of the system on a nonperiodic standard triangle pattern are analyzed and modeled. The matched filter idea is used to characterize quantitatively the spatiotemporal integration of the eye and brain to signal, aliasing, and various noise components over a triangle pattern area, and the perceived signal-to-noise ratio of the staring thermal imager is derived. Further, the TOD performance theoretical model is established. Comparison with the experimental results shows that this theoretical model gives a reasonable prediction of the TOD performance curve for staring thermal imagers. Although more tests and modifications are required, these preliminary results suggest that this model can be developed into a model that predicts the TOD for a wide range of sensors.

TOD Performance Theoretical Model for Staring Thermal Imagers

The TOD (triangle orientation discrimination threshold) metric, as a promising sensor performance measure, is suitable for all types of thermal imaging systems and corresponds to the field performance very well. Since this method was proposed, a great progress has been made in some aspects, such as experimental validation, field performance, and TOD sensor performance model. However, what is still missing is a TOD curve prediction model similar to MRTD (minimum resolvable temperature difference) equation. Here, an equation-based TOD performance model was first developed focusing on staring thermal imagers. Specifically, spatial distribution of the triangle standard test pattern is described quantitatively. Resolution transfer characteristic of the staring infrared imagers is modeled with linear system theory, and system noise is characterized with the introduction of three-dimensional noise model. The modulation effect of overall subsystem of staring thermal imager on the non-periodic triangle standard pattern is analyzed. The matched filter idea is adopted to characterize quantitatively the spatial integration of the eye to signal and various noise components over the triangle pattern area, and the perceived signal-to-noise ratio of the staring thermal imager is derived. Then, the TOD performance theoretical model is established. Comparing with experimental results, this theoretical model gives reasonable prediction of the TOD performance curve of staring thermal imagers. Although more tests and modifications are required, these preliminary results suggest that this model can be developed into a model that predicts the TOD for all kinds of sensors.

Compact Energetic Light Particle Detector and Spectrometer

We have developed a megaelectron-volt class energetic charged-particle spectrometer based on a novel configuration and processing algorithm. The National Space Development Agency of Japan has undertaken a program to develop a particle monitor capable of discriminating and measuring protons in the range from 0.9 to 150 MeV, electrons in the range from 0.5 to >10 MeV and alpha particles >8 MeV, all within a single sensor called the standard dose monitor. The goal is to utilize sensors with nearly identical design and performance on several simultaneous missions to develop a clearer understanding of particle energies and their variability as a function of solar activity, latitude, and altitude. To date, four flight model sensors have been delivered. The sensors are designed to detect accurately the higher-energy particles and high count rates present during active solar periods. In addition, this sensor exhibits extremely efficient discrimination between low-energy electrons and protons. The sensors have been calibrated over nearly their entire particle-energy range. The design is described and calibration data are compared with the results of a Monte Carlo sensor performance model.

Comparison of delta Eddington model calculations of contrast to measurements

Various atmospheric properties can impact sensor performance modeling. Transmission, visibility, and contrast can be used in relatively simple expressions to summarize the impact of the atmosphere on static images. Transmission and contrast can be used together in an expression called the sky-to-ground ratio. Experimental measurements of radiance and imagery are used to assess the applicability of the delta Eddington radiative transfer approximation for estimation of apparent contrast and sky-to-ground ratio. The experiments were performed in southern New Mexico near the autumnal equinox. Delta Eddington calculations are made for two types of aerosol phase functions: Henyey-Greenstein and Deirmendjian haze. Comparisons also include computations from a much simpler radiative transfer model, the Huschke model. These experiments support the use of a relatively simple contrast model that more accurately reflects nature than more empirical models.

Modeling and simulation in support of the design of a data fusion system

This paper uses a modeling and simulation approach in the engineering process of designing a data fusion system for military applications. Fundamental issues in the design of a data fusion system are the selection of appropriate architecture and efficient and dedicated real-time algorithms for the application of interest. Selection of data fusion algorithms is performed using a simulation test bed where realistic modeling of sensor performance is being performed. The test bed also allows the use of multiple algorithms in a dynamic sense based on the particular regime in which the fusion system is operating. This paper first presents the data fusion system engineering guidelines that are being used. Then the paper addresses the complex problem of the tactical picture compilation. Finally the paper shows a case study where the selection of data fusion algorithms is performed for an airborne platform.

Modeling battlefield sensor environments with an object database management system

The Visual Intelligence and Electronic Warfare Simulation (VIEWS) Workbench software system has been developed by Argonne National Laboratory (ANL) to enable Army intelligence and electronic warfare (IEW) analysts at Unix workstations to conveniently build detailed IEW battlefield scenarios, or “sensor environments”, to drive the Army's high-resolution IEW sensor performance models. VIEWS is fully object-oriented, including the underlying database.