Inter-sensor Distance Research Articles

Clutter Reduction by Estimation of Echoes Direction of Arrival in Distributed Radar Sounders in Formation Flying

Spaceborne radar sounders are High Frequency (HF) / Very High Frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this paper, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting Direction of Arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g, inter-sensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones.

Array shape calibration with phase unwrapping techniques for highly deformed arrays

Flexible sensor arrays, such as towed and bottom-mounted arrays, typically suffer from large shape deformations, which seriously affect the results of array processing. In this article, an innovative array shape calibration method with phase unwrapping techniques for highly deformed arrays is proposed. The deformed array is divided into several subarrays, and the first subarray is assumed to suffer from slight shape deformations which can be pre-calibrated. With the consideration of highly deformed arrays, uncalibrated sensors in other subarrays would have serious position mismatches. The phase differences between uncalibrated and pre-calibrated sensors are probably larger than 2 π , which lead to phase ambiguity problems. A phase unwrapping technique is proposed to efficiently solve the phase ambiguity problem by considering the physical structure constraints of the deformed array, such as intersensor distance and bending angle. Then, sensor positions in highly deformed arrays are calibrated with multiple target directions. Simulation results evaluate the performance of the proposed calibration method, and the SwellEx-96 shallow water experiment data set is utilised to demonstrate the effectiveness of the proposed method.

Do static and dynamic activities induce potentially damaging breast skin strain?

Background/AimThis study aimed to quantify breast skin strain and strain rate and the effect of support garments at reducing strain and to determine characteristics that correlate with strain during static...

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001–−0.4611), and negligible to moderate positive correlations with ST (r = 0.004–0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters.

Joint calibration of array shape and sensor gain/phase for highly deformed arrays using wideband signals

A joint sensor position and gain/phase calibration method for highly deformed arrays is proposed. It applies to the case where the sensor gain is omnidirectional and frequency-invariant, and the sensor phase is directional and frequency-invariant. The method is initialized with the raw estimates of the sensor gains, which are simply obtained from the main diagonal elements of the sample covariance matrix. Then, it alternately updates either the sensor gain estimates or the sensor position and phase estimates while fixing the other. When the sensor gain estimates are available, the steering matrices for all the frequency bins are estimated using the constant modulus method. By combining all these steering matrices, the estimates of the sensor positions and the frequency-invariant sensor phases are straightforwardly derived. Then, the sensor gain estimates are refined for the next iteration by using the orthogonality of the subspaces. To resolve the phase ambiguities due to the large position mismatches of highly deformed arrays, the nominal intersensor distance and the array bending angle are used. The Cramer–Rao lower bounds (CRLBs) for the unknown sensor gain/phase and position are also derived. The performance of the proposed method is evaluated using simulation data and compared with the CRLBs.

Optimizing movement in convex and non-convex path-networks to establish connectivity

We solve a movement problem in which there are n sensors in path network in plane, where any sensor communicates only with its two immediate neighbors and only at a given maximum communication distance λ. Initially, some of the inter-sensor distances may be more than λ. We need to move sensors so that each sensor is in the communication range of its two neighbors, keeping the path topology intact. The problem is to minimize the maximum movement of any sensor. We present an O(n3)-time algorithm to compute the new positions of sensors to establish transmission connectivity, called λ-connectivity in the paper, in a convex path-network which minimizes the maximum movement among the sensors. We also generalize our algorithm for ring, non-convex path, tethered and heterogeneous networks.

An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems.

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m and a standard deviation of 36.1 W/m. The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.

Flight Time Minimization of UAV for Data Collection Over Wireless Sensor Networks

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV's total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV's speed and the sensors' transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor's transmit power follows a water-filling policy and the UAV's speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but inversely proportional to the data upload requirement.

Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor.

The Kinect v2 sensor may be a cheap and easy to use sensor to quantify gait in clinical settings, especially when applied in set-ups integrating multiple Kinect sensors to increase the measurement volume. Reliable estimates of foot placement locations are required to quantify spatial gait parameters. This study aimed to systematically evaluate the effects of distance from the sensor, side and step length on estimates of foot placement locations based on Kinect’s ankle body points. Subjects (n = 12) performed stepping trials at imposed foot placement locations distanced 2 m or 3 m from the Kinect sensor (distance), for left and right foot placement locations (side), and for five imposed step lengths. Body points’ time series of the lower extremities were recorded with a Kinect v2 sensor, placed frontoparallelly on the left side, and a gold-standard motion-registration system. Foot placement locations, step lengths, and stepping accuracies were compared between systems using repeated-measures ANOVAs, agreement statistics and two one-sided t-tests to test equivalence. For the right side at the 2 m distance from the sensor we found significant between-systems differences in foot placement locations and step lengths, and evidence for nonequivalence. This distance by side effect was likely caused by differences in body orientation relative to the Kinect sensor. It can be reduced by using Kinect’s higher-dimensional depth data to estimate foot placement locations directly from the foot’s point cloud and/or by using smaller inter-sensor distances in the case of a multi-Kinect v2 set-up to estimate foot placement locations at greater distances from the sensor.

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.]

Relocating drifting sensor networks with ambient noise correlations

A network of drifting sensors, such as hydrophones mounted to freely drifting buoys, can be used as an array for locating acoustic sources underwater. However, for accurate localization of such a source using coherent processing, the positions of the sensors need to be known to a high degree of accuracy, typically more accurately than provided by dead reckoning or GPS alone. Past work has demonstrated the inter-sensor distances can be obtained from long-term ambient noise correlations on fixed arrays [Sabra et al., IEEE J. Ocean Engineering, 2005, 30]. Here, the approach was extended for tracking drifting sensor motion by combining a stochastic search algorithm with ambient noise correlation processing. Optimization of the stochastic search method was explored and performance compared to acoustic data collected from a volumetric hydrophone vs. vector sensor array deployed in the Long Island Sound.

Statistical parameters of the spatiotemporal variability of the wind direction in the surface layer

Multipoint measurements of wind direction were carried out during the expedition of the Institute of Atmospheric Physics, Russian Academy of Sciences (IPA RAS), in Tsimlyansk in 2012. Spatial correlation functions for the transverse direction and temporal correlation functions for the longitudinal direction are plotted under stable and unstable stratification of the atmosphere. The longitudinal correlation radius is much higher than the transverse one, and radii in daytime realizations are larger than in nighttime. To determine the stratification conditions, an ultrasonic anemometer–thermometer was used. Autospectra of wind direction fluctuations were plotted. They include long segments of power dependence on the frequency. The spectral correlation coefficients of variations in the wind direction versus intersensor distance in the transverse direction are also calculated. A set of fast-response thermometers was used in the experiment. They allowed temperature mapping, i.e., plotting the time variations in the isothermal surface altitude. That analysis was also applied to visualization of the spatiotemporal variability of wind direction. The resulting data were used for planning the helicity measurements in the Tsimlyansk expedition in 2014.

Cost‐effective, asynchronous inter‐sensor distance estimation using trigonometry

Precise estimation of inter-sensor distances is essential for reliable localisation in Internet of Things sensor networks. A cost-effective, scalable, asynchronous solution to estimate inter-sensor distances based solely on measurements of distances to a moving object is proposed. More specifically, the proposed solution estimates uncharted distances using trigonometry and processes these estimated distances with a distributed weighted multi-dimensional scaling algorithm for more precise localisation of sensors. It is demonstrated that the proposed solution achieves the localisation error of 4.8–33.9 cm when the measurement errors of sensor devices are in the range of 5–40%.

Wireless Sensor Placement for Bridge Health Monitoring Using a Generalized Genetic Algorithm

The optimal placement of wireless sensors is very different from conventional wired sensor placement due to the limited transmission range of the wireless sensors. This constraint on the inter-sensor distance makes the optimization problem difficult to solve with conventional gradient-based methods. In this paper, an improved generalized genetic algorithm (GGA) based on a self-adaptive dynamic penalty function (SADPF) is proposed for the optimal wireless sensor placement (OWSP) in bridge vibration monitoring. The mathematical model of the OWSP problem is established, and it considers both the bridge vibration monitoring requirements and the constraints of the data transmission range in wireless sensor networks (WSNs). SADPF, which can automatically adjust the amount of penalization for constraint violations according to the evolution generation number and the degree of violation, is then developed so that the wireless sensor placement can be optimized using GGA. Subsequently, the GGA is improved by implementing an elite conservation strategy, a worst elimination policy and a dual-structure coding system. Finally, a numerical experiment is presented with a long-span suspension bridge to demonstrate the feasibility and efficiency of the proposed method, and some indispensible discussions are also given. The results indicate that the wireless sensor configurations that are optimized by the improved SADPF-based GGA can simultaneously meet the data transmission demands in a WSN and fulfill the requirements for structural condition assessment. The developed SADPF can minimize the influence of the limited data transmission range on the search process for the OWSP. The improved SADPF-based GGA quickly and robustly converges to the global optimal solution.

A Novel Approach to Joint Flexion/Extension Angles Measurement Based on Wearable UWB Radios

In this paper, a new method for measuring and monitoring human body joint angles, which uses wearable ultrawideband (UWB) transceivers mounted on body segments, is proposed and investigated. The model is based on providing a high ranging accuracy (intersensor distance) between a pair of transceivers placed on the adjacent segments of the joint center of rotation. The measured distance is then used to compute the joint angles based on the law of cosines. The performance of the method was compared with a flexible goniometer by simultaneously measuring joint flexion-extension angles at different angular velocities, ranging between 8 and 90(°) /s. The measurement errors were evaluated by the average differences between two sets of data (ranging from 0.8(°) for slow movement to 2.8(°) for fast movement), by standard deviation (ranging from 1.2(°) to 4.2(°) for various movement speeds) and by the Pearson correlation coefficient (greater than 0.99) which demonstrates the very good performance of the UWB-based approach. The experimental results have shown that the system has sufficient accuracy for clinical applications, such as rehabilitation.

Improving noisy sensor positions using accurate inter-sensor range measurements

In this paper, we consider the problem of improving noisy sensor positions using accurately measured inter-sensor distances. A novel two-step algorithm is proposed. In Step-1, the multidimentional scaling (MDS) technique is applied to the range measurements and a shifted, rotated and possibly reflected version of the true sensor positions is obtained. Step-2 converts the original problem into a generalized trust region sub-problem (GTRS) that can be solved globally via simple bisection searching. The proposed algorithm is shown to be asymptotically efficient. Simulations verify the efficiency of the proposed algorithm.

The Applicability of Incoherent Array Processing to IMS Seismic Arrays

The seismic arrays of the International Monitoring System (IMS) for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) are highly diverse in size and configuration, with apertures ranging from under 1 km to over 60 km. Large and medium aperture arrays with large inter-site spacings complicate the detection and estimation of high-frequency phases lacking coherence between sensors. Pipeline detection algorithms often miss such phases, since they only consider frequencies low enough to allow coherent array processing, and phases that are detected are often attributed qualitatively incorrect backazimuth and slowness estimates. This can result in missed events, due to either a lack of contributing phases or by corruption of event hypotheses by spurious detections. It has been demonstrated previously that continuous spectral estimation can both detect and estimate phases on the largest aperture arrays, with arrivals identified as local maxima on beams of transformed spectrograms. The estimation procedure in effect measures group velocity rather than phase velocity, as is the case for classical f–k analysis, and the ability to estimate slowness vectors requires sufficiently large inter-sensor distances to resolve time-delays between pulses with a period of the order 4–5 s. Spectrogram beampacking works well on five IMS arrays with apertures over 20 km (NOA, AKASG, YKA, WRA, and KURK) without additional post-processing. Seven arrays with 10–20 km aperture (MJAR, ESDC, ILAR, KSRS, CMAR, ASAR, and EKA) can provide robust parameter estimates subject to a smoothing of the resulting slowness grids, most effectively achieved by convolving the measured slowness grids with the array response function for a 4 or 5 s period signal. Even for medium aperture arrays which can provide high-quality coherent slowness estimates, a complementary spectrogram beampacking procedure could act as a quality control by providing non-aliased estimates when the coherent slowness grids display significant sidelobes. The detection part of the algorithm is applicable to all IMS arrays, with spectrogram-based processing offering a potential reduction in the false alarm rate for high-frequency signals. Significantly, the local maxima of the scalar functions derived from the transformed spectrogram beams are robust estimates of the signal onset time. High-frequency energy is of greater importance for lower event magnitudes and in the cavity decoupling detection evasion scenario. There is a need to characterize both propagation paths with low attenuation of high-frequency energy and situations in which parameter estimation on array stations fails.

Energy efficiency of virtual multi-input, multi-output based on sensor selection in wireless sensor networks

In this paper, a weighted node selection technique in wireless sensor networks is proposed. It is an energy-efficient cooperative technique where a selected number of sensors at the transmitting end are connected with a selected number of sensors at the receiving end to form a virtual multi-input multi-output. The proposed technique is based on a weighted selection function that combines geographical location, inter-sensor distance in a cluster, channel estimate energy, power circuit, channel loss, mobility factor, and residual energy of each sensor. The weight of each of these parameters in the selection function depends on the degree that this parameter affects the energy consumption. Then, the cluster head selects the sensors with better selection parameters that reduce the overall energy consumption. The numerical results show that the proposed weighted node selection technique achieves a significant improvement in the energy consumption, delay, and network lifetime than the conventional techniques with and without the selected number of sensors. Its improvement reaches 15% in the energy consumption that leads to an increase in the network lifetime by four times the network lifetime of other techniques. Copyright © 2012 John Wiley & Sons, Ltd.

Evaluation of the measurement precision in three-dimensional Electromagnetic Articulography (Carstens AG500)

Three-dimensional Electromagnetic Articulography (EMA) measures location and orientation of the moving speech articulators in real time by means of small, wired sensors. We evaluated the measurement accuracy of the Carstens AG500 EMA system using data acquired simultaneously with the Vicon optical motion tracking system (OPT). EMA sensors and OPT markers were combined in a single rigid object to be able to predict location and orientation of the EMA sensors from OPT motion tracking data. The error was computed as the root mean squared (RMS) error. We found that deviations from constant inter-sensor distances (relative error) were in general below 1mm and 0.6° while the difference between the measured and estimated positions (absolute error) ranged between 1 and 2mm and 0.5° and 0.7°. By examining error patterns, four critical orientation regions were detected, but no discernible location dependent error patterning. Sensor velocity appeared to have little impact. The RMS error of the original position calculation has not been found to be a reliable predictor. In the absence of a clear error structure we recommend careful analysis of unexpected findings in speech production data acquired with EMA. Avenues for further improvement of the system are discussed.

The Accuracy Benefit of Multiple Amperometric Glucose Sensors in People With Type 1 Diabetes

OBJECTIVETo improve glucose sensor accuracy in subjects with type 1 diabetes by using multiple sensors and to assess whether the benefit of redundancy is affected by intersensor distance.RESEARCH DESIGN AND METHODSNineteen adults with type 1 diabetes wore four Dexcom SEVEN PLUS subcutaneous glucose sensors during two 9-h studies. One pair of sensors was worn on each side of the abdomen, with each sensor pair placed at a predetermined distance apart and 20 cm away from the opposite pair. Arterialized venous blood glucose levels were measured every 15 min, and sensor glucose values were recorded every 5 min. Sensors were calibrated once at the beginning of the study.RESULTSThe use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P < 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P < 0.001). Using only two sensors also significantly improved very large errors and accuracy. Intersensor distance did not affect the function of sensor pairs.CONCLUSIONSSensor accuracy is significantly improved with the use of multiple sensors compared with the use of a single sensor. The benefit of redundancy is present even when sensors are positioned very closely together (7 mm). These findings are relevant to the design of an artificial pancreas device.