Displacement Estimation Research Articles

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

Deformation Prediction of Cihaxia Landslide Using InSAR and Deep Learning

Slope deformation monitoring and analysis are significant in the geological survey of hydraulic engineering. However, predicting future slope deformation is a vital and challenging task for engineers. The accurate estimation of slope displacement is required for the risk assessment of slope stability. This study was conducted using slope deformation data obtained by interferometric synthetic aperture radar. Five typical points of the slope in different zones were selected to establish the prediction model. Based on the observed data, a prediction model based on long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) was proposed. Firstly, ARIMA and LSTM models were used separately to predict slope deformation. Root mean square error, mean absolute error, and R2 were used to evaluate the performance of the models, and the results showed that LSTM is more effective than ARIMA. It denotes that the LSTM model can catch the trend in the data sequence with time, and ARIMA is good at predicting the bias in the stationary data sequence. Then, the predictions of ARIMA were added to the original data while the new data were fed to the LSTM model. For most data points, our LSTM-ARIMA model achieved good performance, indicating that the model is robust in slope deformation prediction. The effectiveness of the proposed LSTM-ARIMA model will enable engineers to take corresponding measures to prevent accidents before landslides occur.

A Tsunami Generated by a Strike‐Slip Event: Constraints From GPS and SAR Data on the 2018 Palu Earthquake

AbstractA devastating tsunami struck Palu Bay in the wake of the 28 September 2018 Mw = 7.5 Palu earthquake (Sulawesi, Indonesia). With a predominantly strike‐slip mechanism, the question remains whether this unexpected tsunami was generated by the earthquake itself, or rather by earthquake‐induced landslides. In this study we examine the tsunami potential of the co‐seismic deformation. To this end, we present a novel geodetic data set of Global Positioning System and multiple Synthetic Aperture Radar‐derived displacement fields to estimate a 3D co‐seismic surface deformation field. The data reveal a number of fault bends, conforming to our interpretation of the tectonic setting as a transtensional basin. Using a Bayesian framework, we provide robust finite fault solutions of the co‐seismic slip distribution, incorporating several scenarios of tectonically feasible fault orientations below the bay. These finite fault scenarios involve large co‐seismic uplift (>2 m) below the bay due to thrusting on a restraining fault bend that connects the offshore continuation of two parallel onshore fault segments. With the co‐seismic displacement estimates as input we simulate a number of tsunami cases. For most locations for which video‐derived tsunami waveforms are available our models provide a qualitative fit to leading wave arrival times and polarity. The modeled tsunamis explain most of the observed runup. We conclude that co‐seismic deformation was the main driver behind the tsunami that followed the Palu earthquake. Our unique geodetic data set constrains vertical motions of the sea floor, and sheds new light on the tsunamigenesis of strike‐slip faults in transtensional basins.

Quaternion-Based Iterative Extended Kalman Filter for Sensor Fusion of Vision Sensor and IMU in 6-DOF Displacement Monitoring

As civil structures are exposed to various external loads, their periodic evaluation is paramount to ensure their safety. By estimating the 6-degree-of-freedom (DOF) displacement of structures, structural behavior can be monitored directly. Therefore, this study aims to develop a translational and rotational displacement estimation method by fusing a vision sensor and inertial measurement unit (IMU) using a quaternion-based iterative extended Kalman filter (QIEKF). The QIEKF algorithm was applied to reduce the nonlinear influence on the measurement model. The 6-DOF displacement is predicted using the integral of the gyroscope output and updated via a combination of an accelerometer and a magnetometer through a vector matching process in the Kalman filter framework. Subsequently, the 6-DOF displacement estimation result is updated through a vision sensor using a 2-D planar marker and homography transformation in the Kalman filter framework. The performance of the proposed sensor fusion method was verified with experiments using a motorized motion stage, and the results show that the displacements can be estimated with high accuracy regardless of measurement noise and slowly varying signal drift.

Robust thermal error estimation for machine tools based on in-process multi-point temperature measurement of a single axis actuated by a ball screw feed drive system

In machine tools, thermal errors account for 70 % of the total machining errors. The latest improvements in sensor technology have enabled the robust estimation of thermal displacement using the multi-point temperature data of an entire structure. The complex geometry and constant motion of a feed drive system make the direct measurement of temperature difficult because of the sensor power supply and data transmission. In this study, a wireless multi-point temperature sensor system was developed, and the temperature of a ball screw was measured. The ball screw was developed with a built-in temperature sensor array and was installed in an actual machining center. Thermal error measurement tests were conducted, wherein the temperatures of 277 points on the structure and 25 points on the Z-axis ball screw were measured during machine operation. Using finite element analysis (FEA) with multi-point temperature mapping of the drive system, the difference between the relative displacement of the tool center point (TCP) along the Z-axis, measured by an eddy current sensor, and the value estimated by the FEA was calculated to be less than 10 μm. However, the Z-axis ball screw temperature distribution contributed little to the accuracy of thermal displacement estimation along other axes. Therefore, a superposition of the thermal displacements of the structure and drive system provided TCP relative displacement, suggesting the effect of temperature-dependent geometric errors on the volumetric error model of machine tools.

Generalized algorithm for horizontal thermal displacement at the pier top of a railway simple-supported girder bridge

Pier top displacement caused by temperature changes is a critical parameter for the design of railway simple-supported girder bridge to ensure its safety. To date, the existing formulas could only be used to calculate such displacement of pier tops with regular shapes, such as round-ended or rectangular cross-sections. Instead, estimation of displacement involving complex cross-sections and arbitrarily temperature fields usually utilize finite element modeling, which is less efficient. Therefore, to solve the aforementioned deficiency, this work proposes a set of general algorithms named horizontal thermal displacement at the pier top algorithm (HTDPT) which are universally applicable, precise and efficient. The special theory solution for a regular cross-section demonstrates that HTDPT provides a theoretical solution to displacement estimation of pier top. And the numerical simulations for regular and irregular cross-sections indicate that for piers height greater than 20 m, the displacements’ error between simulation and this proposed algorithm is less than 5 %. Thus, the horizontal displacement calculated by HTDPT could estimate the overall displacement, which meets the engineering requirements.

Accurate sensorless displacement control based on the electrical resistance of the shape memory actuator

This paper aims to implement the controllable deformation of a structure using Shape Memory Alloys (SMA) actuators. A sensorless displacement estimation method is proposed. This method is tested on a prototype composed of a disc, beams, and SMA actuators. By measuring the variation of electrical resistivity in SMA springs, as a feedback signal in the closed-loop position control, the surface displacement is obtained without any external displacement sensor. The proposed method is validated by comparing the displacement values estimated by the electrical resistivity measurement with those measured by a laser sensor. The estimated displacement and the measured displacement follow the reference displacement with steady-state errors, respectively of 1.14% and 0.42%.

스핀들의 가속도 데이터와 이송계의 변위 데이터를 이용한 스핀들의 변위 추정 알고리즘

In the existing machine tool field, the focus was on the displacement of the feed system from the viewpoint of the motion of the machine tool. The displacement of the tool or spindle of a machine tool is useful for developing various functions. In this study, using the acceleration data of the spindle, we proposed an algorithm that tracked the displacement of the spindle with respect to the pseudo-step waveform motion. In order to solve the bandwidth problem of the pseudo-step waveform, the displacement data measured by the motor encoder of the feed system was used. In addition, in order to solve the drift problem due to double integration, a new drift removal filter was proposed and a displacement estimation algorithm was implemented. In order to examine the performance and possibility of the proposed spindle displacement estimation algorithm, it was applied to a gantry-type engraver and its excellent performance was confirmed compared to other algorithms.

A simplified model for predicting the accumulated displacement of monopile under horizontal cyclic loadings

Monopile-supported offshore wind turbines are continuously subjected to stochastic cyclic loadings during its lifetime of 25–30 years and it is challenging to accurately predict the accumulated displacement of monopile. In this paper, a simplified model with two cyclic loading factors is proposed to predict the accumulated displacement of monopile in soft clay. These two cyclic loading factors could consider the effects of the pile diameter, number of cycles, loading profile and undrained shear strength profile on the accumulated displacement. The model is validated by comparing with the centrifuge data and results obtained from the hyperplastic accelerated ratcheting model. Furthermore, the evolution of the accumulated displacements of monopile for the ultimate limit state (ULS) storm loading history and the long-term fatigue limit state (FLS) loading history are also investigated. This paper could provide a method for design codes requiring an accurate estimation of monopile displacements.

Improved surface displacement estimation through stacking cross-correlation spectra from multi-channel imagery

Studying sporadic and complex geophysical surface flows, like earthquakes or sea surface circulation, are challenging cases. If a satellite is able to image an event, it becomes essential to pull out as much information as possible. In this contribution we demonstrate a method to increase the coverage and signal-to-noise ratio for displacement estimation, making such surface flow estimates more complete. We leverage upon the redundant offset information acquired by multi-channel push-broom imagery. The individual cross-correlation spectra (cross power spectral density; Fourier transform of the cross-correlation function) of different spectral bands are averaged in the frequency domain before sub-pixel offset-estimation by phase-plane fitting. The method is demonstrated near Kaikōura, where in 2016 a surface rupture occurred. RapidEye data from two different dates were used to reconstruct the displacement. In addition, the circulation along the coast is estimated from data from a single date where multiple spectral bands were acquired within seconds which made stacking of cross-correlation spectra possible. The demonstrated methodology is applied to data from the already decommissioned RapidEye constellation, but can be adopted to other pushbroom systems, such as the Landsat legacy or Sentinel-2.

Low-Cost GNSS and Real-Time PPP: Assessing the Precision of the u-blox ZED-F9P for Kinematic Monitoring Applications

With the availability of low-cost, mass-market dual-frequency GNSS (Global Navigation Satellite System) receivers, standalone processing methods such as Precise Point Positioning (PPP) are no longer restricted to geodetic-grade GNSS equipment only. However, with cheaper equipment, data quality is expected to degrade. This same principle also affects low-cost GNSS antennas, which usually suffer from poorer multipath mitigation and higher antenna noise compared to their geodetic-grade counterparts. This work assesses the quality of a particular piece of low-cost GNSS equipment for real-time PPP and high-rate dynamic monitoring applications, such as strong-motion seismology. We assembled the u-blox ZED-F9P chip in a small and light-weight data logger. With observational data from static experiments—which are processed under kinematic conditions—we assess the precision and stability of the displacement estimates. We tested the impact of different multi-band antenna types, including geodetic medium-grade helical-type (JAVAD GrAnt-G3T), as well as a low-cost helical (Ardusimple AS-ANT2B-CAL) and a patch-type (u-blox ANN-MB) antenna. Besides static tests for the assessment of displacement precision, strong-motion dynamic ground movements are simulated with a robot arm. For cross-validation, we collected measurements with a JAVAD SIGMA G3T geodetic-grade receiver. In terms of precision, we cross-compare the results of three different dual-frequency, real-time PPP solutions: (1) an ambiguity-float solution using the Centre National d’Études Spatiales (CNES) open-source software, (2) an ambiguity-float and an AR (ambiguity-resolved) solution using the raPPPid software from TU Vienna, and (3) and a PPP-RTK solution using the u-blox PointPerfect positioning service. We show that, even with low-cost GNSS equipment, it is possible to obtain a precision of one centimeter. We conclude that these devices provide an excellent basis for the densification of existing GNSS monitoring networks, as needed for strong-motion seismology and earthquake-early-warning.

Probability-based residual displacement estimation of unbonded laminated rubber bearing supported highway bridges retrofitted with Transverse Steel Damper

Predicting the post-earthquake residual displacement of isolated bridges is crucial in deciding their operational and recovery strategies after a seismic event. This study proposes a probability-based approach to estimate the residual displacement of a trilinear hysteretic system representing the unbonded laminated rubber bearing (ULRB) supported highway bridge retrofitted with transverse steel dampers (TSDs), considering the uncertainty of structural parameters and the variability of ground motions. To this end, this study systematically derives the quasi-static residual displacement of a trilinear hysteretic system, and presents the statistical distribution model for the related structural parameters, especially for the pier height and the TSD model parameters. After that, the dynamic residual displacement property of the ULRB supported highway bridge retrofitted with TSDs is investigated considering the uncertainty of various parameters, including model parameters of TSD and ULRB, the mass of superstructure, the height of piers, as well as the ground motions with and without pulse. The maximum possible residual displacement of the trilinear hysteretic system is then related to its quasi-static residual displacement and peak displacement demand. Finally, an innovative probability-based approach is developed in this study to estimate the conditional probability of the dynamic residual displacement of the trilinear system exceeding a threshold at the given ground motion intensity, and this innovative approach is further validated by comparing it with the incremental dynamic analysis (IDA) method. Results show that the dynamic residual displacement of the TSD retrofitted ULRB supported highway bridge system is random, and the ratio between the dynamic residual displacement and the maximum possible residual displacement approximately satisfies a uniform distribution bounded by zero and one. The probability-based approach can obtain a close result with the IDA method and significantly reduce computational efforts. This research represents a first step toward developing a rapid post-earthquake assessment approach for seismically isolated bridges and the seismic design of these bridges considering the residual displacement.

Estimation of running biomechanical parameters using magneto-inertial sensors: a preliminary investigation

Many runners perform training sessions outdoors and on varying surfaces (e.g., track, paved path, trail). Training surface has been shown to influence running spatiotemporal measures and stiffness [1]. However, applied research in understanding training surface influence has been limited by lack of robust methodology to capture biomechanics in-the-field. The increasing availability of wearable magneto- inertial sensors (MIMUs) is now making it possible to perform in-field assessment of running biomechanics [2]. Validation of new inertial-based methods is vital to widespread field use. In this study, we compared inertial-based methods for running biomechanical analysis and against gold-standard stereo-photogrammetric system (SP). Ten recreational male runners (age: 32.3 ± 10.0 years) were enrolled and asked to run on a treadmill at 14 km/hr for 8 trials lasting 6 minutes. We simulated changes in surface in a controlled lab setting by using 8 different footwear conditions. Participants were instrumented with MIMUs (mod: Opal v2, APDM, Portland, USA; fs=200 sample/s) attached on the pelvis (L5) and the instep of both feet, and retroreflective markers on heels, toes, and each MIMU (recorded with a 9-camera Vero system, Vicon, Oxford, UK; fs=200 sample/s). The estimated relevant biomechanical parameters included: stride/stance/swing duration, stride rate, vertical oscillations of the center of mass (CoM), and vertical/leg stiffness. Gait events (initial and final contacts) were estimated using a modified version of the MIMU-based method proposed by Benson et al. [2]. CoM vertical displacement was estimated as the vertical component of the L5 MIMU trajectory. Vertical and leg stiffness were computed starting from CoM displacement, stance interval, and swing interval [3]. For each participant and trial condition, errors with respect to SP were computed (80 values for each parameter) and root mean squared errors (RMSE) across participants and conditions were then calculated. In addition, a Bland-Altman analysis was used to quantify the agreement between MIMU and SP outcomes in terms of 50% limits of agreement interval (LoAI). Differences between MIMU-based and SP-based parameters over all the runners are reported in Table 1. Results showed high accuracy especially in the estimation of stride duration, stride rate and CoM vertical displacement. The level of uncertainty associated to each biomechanical variable, as provided by the LoAIs, can be used to detect differences in surface conditions (footwear conditions). Future analysis and development of field methods will investigate the influence of unconstrained movements (outdoor, no treadmill) in addition to varying surface conditions and the influence of running speed on inertial-based method accuracy.

Deep estimation of viscoelastic and backscatter quantitative ultrasound

Deep learning is an ideal tool to solve inverse problems, which are often ill-posed and require incorporation of a priori information. We focus on solving two well-known inverse problems that entail the estimation of (1) viscoelastic (VE) and (2) backscatter quantitative ultrasound (QUS). These properties are of critical clinical value but are not currently available from B-mode images. On the first front, we propose a novel technique called PICTURE (Physically Inspired ConsTraint for Unsupervised Regularized Elastography) [Tehrani, Rivaz, MICCAI, (2022)], where we impose additional physics-based constraints on the deformation vector field within our loss function and show that it substantially improves the quality of lateral displacement estimation. We develop semi- and unsupervised methods to tackle the problem of lack of ground truth training datasets in real experiments. On the second front, we propose a novel method for segmenting regions of ultrasound images without any patching based on scatterer number densities [Tehrani et al. TUFFC (2022)]. Our segmentation maps can divide the image into irregular regions of fully developed speckle (FDS) or underdeveloped speckle. When moving from simulation to real datasets, we exploit domain adaptation methods using concepts similar to the popular reference phantom method in QUS.

Nanofabrication mechanism of [formula omitted]/20 features achieved by coupling field tip enhancement induced with nanosecond laser irradiating AFM probe tip

The nanofabrication mechanism of coupling field tip enhancement induced by laser-irradiated probe tip was investigated theoretically and experimentally. The coupling mechanisms and distribution characteristics of the enhanced electric, thermal, and stress fields were studied at wavelengths of 400–800 nm and tip-sample distances of 1–20 nm for 9 material combinations. The simulation results demonstrated that the electro-thermal coupling effect and thermal–mechanical coupling effect are the basic principles of this technique, independent of the materials. It was also demonstrated that probe damage due to mechanical scratching can be avoided through the estimation of probe displacement. The superposition of electromagnetic energy and thermal energy generates an extremely constrained total energy of up to 10–12 orders of magnitude near the tip end. The nano-line structures were processed at each tip-sample distance, and nanostructures with a feature size of approximately λ/20 were obtained. The experimental results and the total energy distribution demonstrated that the interaction of the coupling field tip enhancement with samples is in the form of total energy deposits on materials. The significance of this study will not only enable a refined understanding of the fundamental physics of surface nanostructuring but will also advance the scientific and industrial applications of the technology.

A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening

When using digital images to obtain the displacement of a structure, noise is always a factor that greatly affects the accuracy of the displacement results. To overcome this problem, a phase-correlation improvement method, based on phase filtering by phase standard deviation (PSD), is proposed. We use PSD to remove the severe noise interference area in the cross-power spectrum matrix, then the singular value decomposition (SVD) is used to convert the two-dimensional phase to one-dimensional to reduce the difficulty of phase unwrapping. Finally, straight-line fitting is used to obtain the high-precision displacement results. Experiments were carried out using both simulated data and real data. For the simulated data, the absolute accuracy of the proposed method is around 0.3 pixels, while, for the real data, the absolute accuracy of the proposed method is 0.018 mm. The experimental results show that the method can effectively resist noise interference and still obtain accurate displacement estimation results in the case of severe noise.

Effective nonlocal behavior of peridynamic random structure composites subjected to body forces with compact support and related prospective problems

We consider a static problem for statistically homogeneous matrix linear bond-based peridynamic composite materials (CMs) subjected to body force with compact support. Estimation of the effective displacements is performed by the exploitation of the most popular tools and concepts used in conventional local elasticity of composite materials (CMs) with their adaptation to peridynamics. The method is based on estimation of a perturbator introduced by one inclusion inside the infinite peristatic matrix subjected to the body force. The statistical averages of both the displacements and stresses are estimated by summation of these perturbators for all possible locations of inclusions. It leads to obtaining of a new general integral equation (GIE) where a renormalizing procedure is not required for the involved absolutely convergent integrals. It allows us to estimate not only the effective nonlocal constitutive equation but also to evaluate the statistical field averages (inhomogeneous and nonlocal) inside the phases at the fine scale that is critically important for advanced modeling (e.g. for any nonlinear phenomena potentially considered in the future). In particular, in the generalized effective field method (EFM) proposed, the effective field is evaluated from self-consistent estimations using closing of a corresponding integral equation in the framework of the quasi-crystalline approximation. In so doing, the classical effective field hypothesis is relaxed, and the hypothesis of the ellipsoidal symmetry of the random structure of CMs is not used. The method proposed is a promising ingredient at the construction of provably robust data-driven surrogate equations describing both the effective behavior and effective displacement concentration operator inside inclusions. Some opportunities for generalizations of the method proposed are considered. Numerical results for the estimation of effective deformations are obtained for one-dimensional (1D) statistically homogeneous peridynamic composite bar with the prescribed self-equilibrated body forces.

Analysis of fracture mechanisms of ultrathin glass for flexible cover window under pen drop conditions

AbstractThe fracture mechanism of a chemically strengthened ultrathin glass (UTG) for the cover window of flexible display devices was investigated under pen loading conditions for the first time to better understand the UTG impact resistance characteristics. High‐speed camera analysis, fracture fragment analysis, and finite element modeling were employed to investigate the fracture mechanism of the UTG under pen drop conditions. A pen‐on‐plate (POP) test was also employed to examine the fracture characteristics under static loading condition, and its results were compared with those of the pen drop test (PDT) to find a correlation between the static and dynamic loading conditions. The correlation found between the POP and PDT tests indicates that a quantitative estimation of contact displacement under PDT can be obtained by measuring the first ring crack size, to more easily determine the impact resistance characteristics of UTG.

Nonconservative dipole forces on an excited two-atom system

We compute the nonconservative electric dipole forces between the atoms of a suddenly excited binary system. These forces derive from the time variation of the longitudinal electromagnetic momentum. In contrast to the conservative van der Waals forces, the nonconservative forces possess components orthogonal to the interatomic axis. Thus, despite being several orders of magnitude smaller than van der Waals', they might be accessible experimentally. For the case of a binary system of identical atoms in Dicke states, these forces are reciprocal. However, when only one of the atoms is initially excited, as with the van der Waals forces, the nonconservative ones are nonreciprocal, which results in a net force on the two-atom system. We offer an estimate of the spatial displacement caused by the nonconservative forces on a binary system of hydrogen atoms.

A wireless signal-based sensing framework for robotics

In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.