Sensor Trajectory Research Articles

Cross-Polarization Amplitudes of Obliquely Orientated Buildings With Application to Urban Areas

Buildings that are rotated with respect to the sensor trajectory could be erroneously classified as vegetated areas in the Pauli basis, and subsequently in many decomposition theorems despite the considerable amount of work done to solve that issue. This misjudgement is linked to the high level of their cross-polarized contribution. Using electromagnetic simulation tools and image analysis, we study the value of these cross-polarization components. We show that forested areas and cities exhibit significantly different cross-polarization levels; indeed, the origin of these components is actually distinct. Based on that, to discriminate between the two environments, we introduce an extension to the Pauli basis where the cross polarization is split into two classes, one for rotated dihedrals and the other for random scatterers. This approach is then tested on two synthetic aperture radar images: the first acquired at C-band using RADARSAT-2 over Downtown San Francisco and the second using RAMSES at X-band over an industrial area near Paris.

ESTIMATION OF TREE POSITION AND STEM DIAMETER USING SIMULTANEOUS LOCALIZATION AND MAPPING WITH DATA FROM A BACKPACK-MOUNTED LASER SCANNER

Abstract. A system was developed for automatic estimations of tree positions and stem diameters. The sensor trajectory was first estimated using a positioning system that consists of a low precision inertial measurement unit supported by image matching with data from a stereo-camera. The initial estimation of the sensor trajectory was then calibrated by adjustments of the sensor pose using the laser scanner data. Special features suitable for forest environments were used to solve the correspondence and matching problems. Tree stem diameters were estimated for stem sections using laser data from individual scanner rotations and were then used for calibration of the sensor pose. A segmentation algorithm was used to associate stem sections to individual tree stems. The stem diameter estimates of all stem sections associated to the same tree stem were then combined for estimation of stem diameter at breast height (DBH). The system was validated on four 20 m radius circular plots and manual measured trees were automatically linked to trees detected in laser data. The DBH could be estimated with a RMSE of 19 mm (6 %) and a bias of 8 mm (3 %). The calibrated sensor trajectory and the combined use of circle fits from individual scanner rotations made it possible to obtain reliable DBH estimates also with a low precision positioning system.

Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors

This paper presents a hand gesture based control of an omnidirectional wheelchair using inertial measurement unit (IMU) and myoelectric units as wearable sensors. Seven common gestures are recognized and classified using shape based feature extraction and Dendogram Support Vector Machine (DSVM) classifier. The dynamic gestures are mapped to the omnidirectional motion commands to navigate the wheelchair. A single IMU is used to measure the wrist tilt angle and acceleration in three axis. EMG signals are extracted from two forearm muscles namely Extensor Carpi Radialis and Flexor Carpi Radialis and processed to provide Root Mean Square (RMS) signal. Initiation and termination of dynamic activities are based on autonomous identification of static to dynamic or dynamic to static transition by setting static thresholds on processed IMU and myoelectric sensor data. Classification involves recognizing the activity pattern based on periodic shape of trajectories of the triaxial wrist tilt angle and EMG-RMS from the two selected muscles. Second order Polynomial coefficients extracted from the sensor trajectory templates during specific dynamic activity cycles are used as features to classify dynamic activities. Classification algorithm and real time navigation of the wheelchair using the proposed algorithm has been tested by five healthy subjects. Classification accuracy of 94% was achieved by DSVM classifier on ‘k’ fold cross validation data of 5 users. Classification accuracy while operating the wheelchair was 90.5%.

EXPLORATION AND DATA REFINEMENT VIA MULTIPLE MOBILE SENSORS BASED ON GAUSSIAN PROCESSES.

We consider configuration of multiple mobile sensors to explore and refine knowledge in an unknown field. After some initial discovery, it is desired to collect data from the regions that are far away from the current sensor trajectories in order to favor the exploration purposes, while simultaneously, exploring the vicinity of known interesting phenomena to refine the measurements. Since the collected data only provide us with local information, there is no optimal solution to be sought for the next trajectory of sensors. Using Gaussian process regression, we provide a simple framework that accounts for both the conflicting data refinement and exploration goals, and to make reasonable decisions for the trajectories of mobile sensors.

On simultaneous electromagnetic articulography and electroglottography data acquisition

Simultaneous measurements of speech articulator movement and laryngeal activity are desirable for obtaining a picture of the coarticulatory behavior between oral articulators and laryngeal vocal fold behavior. However, since electroglottography (EGG) use the application of a weak current across the larynx, which is in close proximity to the oral vocal tract, there is a possibility that its use may interfere with the sensor voltage fluctuation measurements used in electromagnetic articulography (EMA). In order to investigate any hypothetical interference effect, data was collected using Northern Digital Instruments’ Wave articulograph with Glottal Enterprises’s EG2-PCX electroglottograph. Datasets were collected from one male and one female speaker—both with simultaneous EMA and EGG and, for comparison, with EMA alone. Means and standard deviations of inter-sensor distances for static (i.e., reference) and moving (e.g., tongue tip, lip, and jaw) EMA sensor trajectories were compared across the two collection conditions for possible interference effects. Preliminary results find no discernable differences in inter-sensor distances for either static or moving EMA sensors between the with- and without-EGG conditions. If this finding is maintained for additional speakers, NDI EMA data and GE EGG signals may be collected simultaneously without adverse effects on the measurement. [Work supported by NIH.]

SpatialRecruiter: Maximizing Sensing Coverage in Selecting Workers for Spatial Crowdsourcing

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, we investigate the integration of these two paradigms. We notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, we propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. We propose two coverage estimation functions to compute the coverage potential of a worker. Then, we design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms.

Quantifying kinematic aspects of reduction in a contrasting rate production task

Electromagnetic articulometry (EMA) was used to record the 720 phonetically balanced Harvard sentences (IEEE, 1969) from multiple speakers at normal and fast production rates. Participants produced each sentence twice, first at their preferred “normal” speaking rate followed by a “fast” production (for a subset of the sentences two normal rate productions were elicited). They were instructed to produce the “fast” repetition as quickly as possible without making errors. EMA trajectories were obtained at 100 Hz from sensors placed on the tongue, lips, and mandible, corrected for head movement and aligned to the occlusal plane. Synchronized audio was recorded at 22050 Hz. Comparison of normal to fast acoustic durations for paired utterances showed a mean 67% length reduction, and assessed using Mermelstein's method (1975), two fewer syllables on average. A comparison of inflections in vertical jaw movement between paired utterances showed an average of 2.3 fewer syllables. Cross-recurrence analysis of distance maps computed on paired sensor trajectories comparing corresponding normal:normal to normal:fast utterances showed systematically lower determinism and entropy for the cross-rate comparisons, indicating that rate effects on articulator trajectories are not uniform. Examples of rate-related differences in gestural overlap that might account for these differences in predictability will be presented. [Work supported by NSF.]

Smooth Sensor Motion Planning for Robotic Cyber Physical Social Sensing (CPSS).

Although many researchers have begun to study the area of Cyber Physical Social Sensing (CPSS), few are focused on robotic sensors. We successfully utilize robots in CPSS, and propose a sensor trajectory planning method in this paper. Trajectory planning is a fundamental problem in mobile robotics. However, traditional methods are not suited for robotic sensors, because of their low efficiency, instability, and non-smooth-generated paths. This paper adopts an optimizing function to generate several intermediate points and regress these discrete points to a quintic polynomial which can output a smooth trajectory for the robotic sensor. Simulations demonstrate that our approach is robust and efficient, and can be well applied in the CPSS field.

Single Sensor Path Design for Best Emitter Localization via Convex Optimization

We consider the design of a single moving sensor trajectory for the purpose of optimally localizing a stationary emitter based on time-of-arrival measurements, which are deteriorated by oscillator instability. The localization error covariance matrix is predicted by the Cramer–Rao bound. In order to optimize the localization, we propose the minimization of the largest eigenvalue of the bound matrix, or, equivalently, the maximization of the smallest eigenvalue of the associated Fisher information matrix. Two different cases are discussed. In the first case, a pre-mission path design is considered where the path is optimized for locating an emitter in the vicinity of a postulated location. In the second case, the path is designed in real time, as the sensor moves, using measurements collected online. Using robust optimization methods, the best next way-point is chosen, considering the uncertainty in the emitter location. Constraints rising from speed and maneuvering limitations, and restricted regions are treated. Since these optimization problems are non-convex, we use semi-definite relaxation. We demonstrate that in many scenarios, the proposed algorithms yield near optimal results.

Ambient noise correlation-based imaging with moving sensors

Waves can be used to probe and image an unknown medium. Passive imaging uses ambient noise sources to illuminate the medium. This paper considers passive imaging with moving sensors. The motivation is to generate large synthetic apertures, which should result in enhanced resolution. However Doppler effects and lack of reciprocity significantly affect the imaging process. This paper discusses the consequences in terms of resolution and it shows how to design appropriate imaging functions depending on the sensor trajectory and velocity.

Decentralized Dynamic Data-driven Monitoring of Atmospheric Dispersion Processes

Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

IJARCCE - Computer and Communication Engineering

2 Abstract: In the proposed paper the major issue is the target tracking in wireless sensor networks and to obtain exact position of the target as to be measured so that we can expect improvement in the tracking resolution. so there is need of finding accurately sensor and targets trajectory path as to be studied with special care and captured at regular basis. We also studied correlation properties and sensitivity in mobile sensors from the system parameters and maintained good resolution for tracking of mobile sensors at different speeds. Our simulation results gave satisfactory system parameters like sensor density, sensor and target mobility compared static sensor network environment.

Airborne Monitoring of Ground Traffic Using Unmanned Aerial Vehicles

This article presents an airborne surveillance methodology to monitor the ground traffic and identify suspicious or abnormal behaviours using unmanned aerial vehicles. To track moving targets using an on-board moving target indicator, the tracking filter is designed along with sensor fusion and trajectory smoothing techniques to improve the estimation accuracy. Using the estimated car states, two behaviour recognition algorithms are introduced: (i) string pattern matching to find pre-defined suspicious behaviours in the car driving mode history (expressed as a string of numbers) and (ii) a statistical learning approach with domain knowledge given by road-map information. To verify the feasibility and benefits of the proposed approaches, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.

Monitoring Moving Objects Using Aerial Mobile Sensors

We propose an optimization-based path-planning framework for an aerial mobile sensor network. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides collision-free mobile sensor trajectories that are feasible with respect to user-defined vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the augmented state and parameter estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming problem using the direct transcription method known as simultaneous collocation. The optimization problem is solved with a receding horizon and both a field experiment and a numerical simulation illustrate the approach.

A syntactic two-component encoding model for the trajectories of human actions.

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

Passive bistatic SAR imaging — Challenges and limitations

One widely used imaging technique in Earth observation nowadays is the synthetic aperture radar (SAR) method. It was proposed for the first time in 1951 by Carl Willey from the Goodyear Aircraft Corporation [1], [2], [3]. Wiley found that the Doppler history of the signals received by a radar mounted on a moving platform can be exploited to obtain a high resolution in the cross-range dimension (along the path of sensor movement). Another explanation of this technique is that the results of an observation taken from subsequent points on a sensor's trajectory may be interpreted as if they came from different antennas in a huge array. Combining this concept with pulse compression in the range domain, it is possible to obtain a high-quality, two-dimensional image of the Earth's surface.

Optimal deployment of mobile sensors for target tracking in 2D and 3D spaces

This paper proposes a control strategy to autonomously deploy optimal placements of range-only mobile sensors in 2D and 3D spaces. Based on artificial potential approaches, the control strategy is designed to minimize the intersensor and external potentials. The inter-sensor potential is the objective function for optimal sensor placements. A placement is optimal when the inter-sensor potential is minimized. The external potential is introduced to fulfill constraints on sensor trajectories. Since artificial potential approaches can handle various issues such as obstacle avoidance and collision avoidance among sensors, the proposed control strategy provides a flexible solution to practical autonomous optimal sensor deployment. The control strategy is applied to several optimal sensor deployment problems in 2D and 3D spaces. Simulation results illustrate how the proposed control strategy can improve target tracking performance.

Optimal Sensor Trajectories for Mobile Underwater Target Positioning with Noisy Range Measurements

There is considerable interest in reducing the number of sensors/beacons involved in underwater positioning/navigation systems since this has the potential to drastically reduce the costs and the time spent in deploying, calibrating, and recovering acoustic equipment at sea. Motivated by these considerations, we address the problem of single underwater target positioning based on acoustic range measurements between the target and a moving sensor at the sea surface. In particular, the goal of the present work is to compute optimal geometric trajectories for the surface sensor that will, in a well defined sense, maximize the range-related information available for underwater target positioning and tracking. To this effect, the Fisher Information Matrix and the maximization of its determinant are used to determine the sensor trajectory that yields the most accurate positioning of the target, while the latter describes a preplanned trajectory. It is shown that the optimal trajectory depends on the velocity of the sensor, the velocity and trajectory of the target, the sampling time between measurements, the measurement error model, and the number of measurements used to compute the FIM. Simulation examples illustrate the key results derived.

Robust Autofocusing Approach for Highly Squinted SAR Imagery Using the Extended Wavenumber Algorithm

For highly squinted synthetic aperture radar (SAR) imaging, the wavenumber domain SAR processing algorithm is commonly accepted as an ideal solution to SAR focusing in the case of an ideal straight sensor trajectory. However, airborne SAR is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, we propose a robust autofocusing approach for highly squinted airborne SAR imagery using the extended wavenumber algorithm, being capable of estimating the range-dependent phase errors. To apply the proposed autofocusing scheme, a detailed analysis of the motion error model in the conical reference system is presented, where the formulation of range-dependent phase errors for squinted SAR is given. The proposed autofocusing approach is performed by a three-step process: referring to the inevitable residual phase after deramping for highly squinted SAR, a modified squinted phase gradient autofocusing (SPGA) algorithm is put forward to retrieve the range-independent phase errors; based on the established motion error model, the residual range-dependent phase errors are estimated using a local maximum likelihood-weighted SPGA algorithm; and motion compensation is executed by a two-step approach to reach the range-independent and range-dependent corrections, respectively. Experiments based on measured data have shown that the proposed autofocusing approach performs well for highly squinted SAR imaging.

Dense scene reconstruction with points of interest

We present an approach to detailed reconstruction of complex real-world scenes with a handheld commodity range sensor. The user moves the sensor freely through the environment and images the scene. An offline registration and integration pipeline produces a detailed scene model. To deal with the complex sensor trajectories required to produce detailed reconstructions with a consumer-grade sensor, our pipeline detects points of interest in the scene and preserves detailed geometry around them while a global optimization distributes residual registration errors through the environment. Our results demonstrate that detailed reconstructions of complex scenes can be obtained with a consumer-grade camera.