Target Localization Accuracy Research Articles

Study on GNSS‐R multi‐target detection and location method based on consistency checking

The use of GNSS reflected signals to carry out passive detection of space targets is an emerging technical field. Unlike the traditional GNSS-R in large-scale environments such as oceans, vegetation, ice and snow, it is required to identify different targets within the detection range, which is one of the biggest challenges. To solve this problem, a hyperboloid observation equation based on the TDOA location model is constructed, which can eliminate the GNSS satellite orbit error, the satellite atomic clock error and the detection receiver clock error via differential technique. The consistency checking theory based on hypothesis and test is used to detect and distinguish different targets while locating. The simulation results show that the target detection and location accuracy can reach tens of metres, and when the different space targets are separated by 50 m, 100 m, 150 m, 200 m, and 250 m in sequence, the detection success rate of the method is 67.25%, 87.50%, 95.75%, 99.25%, and 99.75%, respectively, which indicates the feasibility and effectiveness of the method. The study provides a feasible technical approach for solving the multi-target problem in GNSS-R space target passive detection.

Auditory cues facilitate object movement processing in human extrastriate visual cortex during simulated self-motion: A pilot study

Visual segregation of moving objects is a considerable computational challenge when the observer moves through space. Recent psychophysical studies suggest that directionally congruent, moving auditory cues can substantially improve parsing object motion in such settings, but the exact brain mechanisms and visual processing stages that mediate these effects are still incompletely known. Here, we utilized multivariate pattern analyses (MVPA) of MRI-informed magnetoencephalography (MEG) source estimates to examine how crossmodal auditory cues facilitate motion detection during the observer's self-motion. During MEG recordings, participants identified a target object that moved either forward or backward within a visual scene that included nine identically textured objects simulating forward observer translation. Auditory motion cues 1) improved the behavioral accuracy of target localization, 2) significantly modulated the MEG source activity in the areas V2 and human middle temporal complex (hMT+), and 3) increased the accuracy at which the target movement direction could be decoded from hMT+ activity using MVPA. The increase of decoding accuracy by auditory cues in hMT+ was significant also when superior temporal activations in or near auditory cortices were regressed out from the hMT+ source activity to control for source estimation biases caused by point spread. Taken together, these results suggest that parsing object motion from self-motion-induced optic flow in the human extrastriate visual cortex can be facilitated by crossmodal influences from auditory system.

Visual display for surgical targeting: concepts and usability study

PurposeInteractive image-guided surgery technologies enable accurate target localization while preserving critical nearby structures in many surgical interventions. Current state-of-the-art interfaces largely employ traditional anatomical cross-sectional views or augmented reality environments to present the actual spatial location of the surgical instrument in preoperatively acquired images. This work proposes an alternative, simple, minimalistic visual interface intended to assist during real-time surgical target localization.MethodsThe estimated 3D pose of the interventional instruments and their positional uncertainty are intuitively presented in a visual interface with respect to the target point. A usability study with multidisciplinary participants evaluates the proposed interface projected in surgical microscope oculars against cross-sectional views. The latter was presented on a screen both stand-alone and combined with the proposed interface. The instruments were electromagnetically navigated in phantoms.ResultsThe usability study demonstrated that the participants were able to detect invisible targets marked in phantom imagery with significant enhancements for localization accuracy and duration time. Clinically experienced users reached the targets with shorter trajectories. The stand-alone and multi-modal versions of the proposed interface outperformed cross-sectional views-only navigation in both quantitative and qualitative evaluations.ConclusionThe results and participants’ feedback indicate potential to accurately navigate users toward the target with less distraction and workload. An ongoing study evaluates the proposed system in a preclinical setting for auditory brainstem implantation.

A multiple closed-loops robotic calibration for accurate surgical puncture.

Robotic puncture system increasingly demands stringent standard in target location accuracy. The positional and orientational transformation relationships among all components of the system are supposed to be calibrated and identified preoperatively. The target location performance is directly determined by the calibration result. Therefore, a multiple closed-loops calibration approach is proposed to achieve high-level calibration accuracy in robotic puncture system. MATERIALS& METHODS: This method takes as input the three-dimensional position information of the retro-reflective markers mounted on the surgical tool, which is detected by the optical tracking system in real time during robotic movement. There is less complicated mathematical derivation and calculation in the presented algorithm by applying the closed-loop principle. Experimental results validate that it can achieve accurate robotic target location with less input data and computation-cost, satisfying the clinical puncture requirements. The spatial calibration between robotic arm and optical tracking system efficiently realised by the presented approach present an alternative which can be safely applied to the robotic puncture system for accurate insertion. Overall, a multiple closed-loops calibration approach is proposed in this work, which may increase surgical efficiency.

Target Positioning Algorithm Based on RSS Fingerprints of SVM of Fuzzy Kernel Clustering

The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.

Target localization during respiration motion based on LSTM: A pilot study on robotic puncture system

In the needle biopsy, the respiratory motion causes the displacement of thoracic-abdominal soft tissues, which brings great difficulty to accurate localization. Based on internal target motion and external marker motion, the existing methods need to establish a correlation model or a prediction model to compensate the respiratory movement, which can hardly achieve required accuracy in clinic use due to the complexity of the internal tissue motion. In order to improve the tracking accuracy and reduce the number of models, we propose a framework for target localization based on long short-term memory (LSTM) method. Combined with the correlation model and the prediction model by using LSTM, we adopted the principal component of time-series features of external surrogate signals to predict the trajectory of the internal tumour target. Additionally, based on the electromagnetic tracking system and Universal Robots 3 robotic arm, we applied the proposed approach to a prototype of robotic puncture system for real-time tumour tracking. To verify the proposed method, experiments on both public datasets and customized motion phantom for respiratory simulation were performed. In the public dataset study, an average mean absolute error, and an average root-mean-square error of predictive results of 0.44 and 0.58 mm were achieved, respectively. In the motion phantom study, an average root mean square of puncturing error resulted in 0.65 mm. The experimental results demonstrate the proposed method improves the accuracy of target localization during respiratory movement and appeals the potentials applying to clinical application.

Cascaded Correlation Refinement for Robust Deep Tracking.

Recent deep trackers have shown superior performance in visual tracking. In this article, we propose a cascaded correlation refinement approach to facilitate the robustness of deep tracking. The core idea is to address accurate target localization and reliable model update in a collaborative way. To this end, our approach cascades multiple stages of correlation refinement to progressively refine target localization. Thus, the localized object could be used to learn an accurate on-the-fly model for improving the reliability of model update. Meanwhile, we introduce an explicit measure to identify the tracking failure and then leverage a simple yet effective look-back scheme to adaptively incorporate the initial model and on-the-fly model to update the tracking model. As a result, the tracking model can be used to localize the target more accurately. Extensive experiments on OTB2013, OTB2015, VOT2016, VOT2018, UAV123, and GOT-10k demonstrate that the proposed tracker achieves the best robustness against the state of the arts.

Optimal transmitter and receiver placement for localizing 2D interested-region target with constrained sensor regions

This paper presents a new transmitter and receiver placement method for sensing a target in a region of interest with distributed multiple input multiple output (MIMO) radar systems. Since the Cramer-Rao lower bound (CRLB) is the standard measurement for target localization accuracy, this paper divides the region of interest into grid points and formulates the transmitter and receiver placement problem via the minimax criterion. Thus we can ensure that the joint (x, y) CRLB corresponding to each grid point of the interested region is sufficiently small. To tackle such a nonconvex and nonlinear problem, we apply the alternating direction method of multipliers to divide the difficult problem into several solvable subproblems, respectively, with fewer variables. We also extend the algorithm to the separate (x, y) CRLB-based sensor placement problem and continuous distribution of uncertain target. Simulation results show that our proposed algorithms are efficient methods for transmitter and receiver placement in distributed MIMO radar systems.

Antifriction Mechanism of Longitudinal Vibration-Assisted Insertion in DBS

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. The insertion of brain tissue is an important procedure that affects the whole operation and the sequela. During the insertion process, the friction between the tissue and the needle shaft is the main factor affecting the degree of tissue damage and the accuracy of target location. Vibration-assisted needle insertion has been shown to reduce friction during needle insertion into biological tissue. LuGre model is a friction model that includes coulomb friction and viscous friction between two contact surfaces and accurately describes the Stribeck effect. This paper studies the influence of longitudinal vibration on the friction force during needle insertion. Based on LuGre model, the influence of longitudinal vibration parameters on friction force is discussed. Through experiments on porcine brain tissue and gel phantom, the friction force during insertion and the positive pressure of tissue against the needle under different vibration parameters were investigated. The experiment showed that the vibration can change the friction force by affecting the equivalent friction coefficient and the positive pressure of tissue against the needle. The equivalent friction coefficient showed a specific trend with the change of vibration parameters, while the positive pressure does not change with the vibration parameters.

Target tracking based on approximate localization technique in deterministic directional passive sensor network

Outdoor localization of non-cooperative moving discrete target tracking is a demanding and challenging due to inherent constraints of “target tracking wireless sensor networks” such as battery capacity, processing capacity, memory capacity. Current methods use either an active sensor or perform additional processing of the data received from multiple passive sensor nodes that increases power consumption. The work presented here proposes approximate localization of non-cooperative moving target in large open secluded area with optimal power consumption and sufficient accuracy. Binary passive infrared (PIR) sensor is used, resulting in reduced power consumption by the sensor. In order to deal with sensor’s technical limitation, a node is developed using variable range binary PIR sensors which results in variable sensing sectors. Deterministic directional, spatial–temporal domain of network topology along with activation log of node and its sectors is mapped with a dynamic target motion trajectory, resulting in sufficiently accurate target localization. Simulation results of the algorithm for “position estimation of random direction linearly moving target with positive slope motion model” indicate significant tracking of target with sufficient accuracy and reduced sensing power compared to existing methods.

Interactive convolutional learning for visual tracking

Recent years have witnessed significant improvements of ensemble trackers based on independent models. However, existing ensemble trackers only combine the responses of independent models and pay less attention to the learning process, which hinders their performance from further improvements. To this end, we propose an interactive learning framework to strengthen the independent models in the learning process. Specifically, in the interactive network, we force convolutional filter models to interact with each other by sharing their responses during the learning. The interaction between the convolutional filter models can mine hard samples and prevent easy samples from overwhelming them, which improve their discriminative capacity. In addition, to achieve a more accurate target location, we develop a fusion mechanism based on the confidences of the independent predictions. We evaluate the proposed method on five public datasets including OTB-2013, OTB-2015, VOT-2016, VOT-2017, Temple-Color-128, and LaSOT. The comprehensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.

Simulation study of underwater intruder localization based on transfer learning

Forward scattering of the target could cause the amplitude and phase aberration of the received sound field, which received attentions in harbor monitoring and anti-submarine. However, the localization under forward scattering configuration is a challenging task due to the strong direct blast. The method based on sensitive kernel function which exploit the aberration of the received signals is sensitive to the environment mismatch and a localization method based on transfer learning framework is developed. The envelopes of aberrations caused by the forward scattering of intruder are firstly extracted by applying pulse compression technique on the received signals, and then normalized by comparing with the case of intruder absent. The data set near the first arrivals on the normalized aberrations are selected as the learning physical parameters. A convolution neural network is trained with these data generated by the forward scattering model to establish a mapping relationship between intruder’s localization and the aberrations of received signal, thus the localization problem is transformed into classification. In the second step, the parameters of the convolutional pooling layer in the pre-trained model are frozen in the transfer learning procedure, and the parameters of the fully connected layer in the pre-trained model are updated using a small amount of data under the fluctuated environment. Simulation of the localization of ellipsoidal targets with a signal-to-noise ratio of 0 dB under a shallow water environment is performed for a scenario to explore the robustness of the method. The results show that the accurate target localization could be achieved in the case of sound velocity profile mismatch. Also, the method is not significantly sensitive to the target scattering function, sound properties of sediment and deployment of transceivers. The sensitivities to the waveguide amplitude and phase fluctuations are further modeled. The results show that good localization accuracy can be obtained in a relatively stable environment, and results are distinguished between the presence and absence of the target. Since the proposed method is derived by the model and real data, the accurate scattering model and sufficient training data are not significantly necessary. The method may provide a promising way for forward scattering detection.

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three‐dimensional (3D) conditions. Therefore, a Delaunay‐triangulation‐based WSN localization method, which can be adapted to two‐dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

Fast Localization of Underground Targets by Magnetic Gradient Tensor and Gaussian-Newton Algorithm With a Portable Transient Electromagnetic System

Differential evolution (DE) algorithm, which is a global convergence algorithm, is often used to estimate the position of underground targets detected with a portable transient electromagnetic (TEM) system. The DE algorithm is extremely time-consuming due to thousands of iterations. A new algorithm for fast localization of an underground target by magnetic gradient tensor and Gaussian-Newton algorithm with a portable TEM system is proposed. First, the gradient tensor of an underground target is constructed with the differential responses received by the portable sensor. Gradient tensor, commonly used in magnetic detection, is applied for the first time in TEM detection to estimate the target position for each measurement. Then, all the estimated positions are averaged to reduce the localization error. Taking the averaged position as the initial value, the Gaussian-Newton algorithm can complete iterations within dozens of times, which can effectively improve the speed and accuracy of the algorithm. Finally, the performance of the new method has been verified in the test-stand and field experiments. Results show that the errors of averaged positions by gradient tensor are no more than 8 cm in the horizontal direction. The errors of the estimated positions, inclination, and rotation angles by the Gaussian-Newton algorithm are no more than 4 cm, 6°, and 5°, respectively. The statistical running time of the proposed method takes approximately tens of milliseconds, accounting for about 7% of the DE algorithm. The proposed method can achieve fast and accurate localization and characterization of targets and has an important significance to the digging and recognition of underground targets.

Retracted] Mathematical Modeling and Simulation of Wireless Sensor Network Coverage Problem

The research on wireless sensor networks has achieved a lot in recent years and some of the results have been put into practical applications, but with the increasing demand and requirements for wireless sensor networks, many old and new problems need to be solved urgently. In this paper, a data topology optimization algorithm based on local tree reconstruction for heterogeneous wireless sensor networks is proposed for data transmission in wireless sensor networks that are easily affected by external instabilities. This heterogeneous network can accomplish better data transmission; firstly, the nodes are divided into different layers according to the hop count of nodes in the network, and a certain proportion of relay nodes are selected for different layer nodes; then, different initial energy is set for different layer nodes, and since the data packets of different nodes have different sizes, the corresponding data aggregation coefficients are used in this paper according to the actual data requirements of the network during data transmission; finally, the topology of the tree is dynamically updated in real time during the operation of the network to extend the lifetime of the nodes. The simulation results verify that the proposed heterogeneous network topology evolution algorithm effectively extends the network lifetime and improves the utilization of nodes. This paper establishes a modified least‐squares target localization model to achieve accurate 3D localization of targets in real scenes and proposes an optimal base station node selection strategy based on spectral clustering using the location distribution information of base station nodes in space. The simulation results show that the error of the terminal 3D coordinates calculated by the proposed algorithm is smaller than the real coordinates, and the error is smaller than other existing algorithms with the same simulation data.

A Python software to evaluate geometric discrepancies between stereotactic CT and MR images in radiosurgery

Due to its excellent soft tissue contrast, magnetic resonance imaging (MRI) is widely used for target delineation in stereotactic radiosurgery. However, because of a number of factors (inhomogeneity of the constant magnetic field, nonlinearity of the gradient fields, etc.), MR images are more susceptible to spatial distortion as opposed to Computed tomography (CT) images, which could be considered as the reference studies in terms of the accuracy of target localization. It is crucial to assess the geometric errors in MR images as the accuracy of dose delivery strongly depends on MRI quality. One of the ways of MRI distortion evaluation is to scan a phantom with multiple marker points on both CT and MRI scanner and compare coordinates of the points defined on CT and MR images, respectively. Performing this test as a routine part of MRI quality assurance (QA) program for stereotactic radiosurgery is complicated by lack of a software for the automatic calculations of the discrepancies.The aim of our work was to develop a software to calculate discrepancies between stereotactic coordinates of the phantom’s plastic rods defined on the MR and CT images of the Elekta MR phantom. A special Python software was developed and the spatial accuracy of the stereotactic MR images was assessed. The calculated total differences between the coordinates of the CT and T1 weighted MR images, CT and T2 weighted MR images exceeded 1 mm in 3,5%, 0.1% of the points, respectively. Using the software in clinic routines could both speed up the time needed for the test performance and eliminate subjective visual assessment of the discrepancies.

Target Localization Geometry Gain in Distributed MIMO Radar

In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information matrix (FIM), which is equivalent to minimizing the error ellipse area. The square root of the average determinant of the FIM can be expressed as a product of three parameters, namely the equivalent single radar gain, coherency gain and geometry gain. It is shown that the coherency gain of coherent MIMO radar is greater than the non-coherent one, while the geometry gain of coherent MIMO radar is always smaller than or equal to the non-coherent case. The maximum value of the geometry gain for a MIMO radar system with N transmit and M receive antennas is proportional to MN for coherent while it is √2MN for the non-coherent case.

Evaluation of patient-specific MR distortion correction schemes for improved target localization accuracy in SRS.

This work aims at promoting target localization accuracy in cranial stereotactic radiosurgery (SRS) applications by focusing on the correction of sequence-dependent (also patient induced) magnetic resonance (MR) distortions at the lesion locations. A phantom-based quality assurance (QA) methodology was developed and implemented for the evaluation of three distortion correction techniques. The same approach was also adapted to cranial MR images used for SRS treatment planning purposes in single or multiple brain metastases cases. A three-dimensional (3D)-printed head phantom was filled with a 3D polymer gel dosimeter. Following treatment planning and dose delivery, volumes of radiation-induced polymerization served as hypothetical lesions, offering adequate MR contrast with respect to the surrounding unirradiated areas. T1-weighted (T1w) MR imaging was performed at 1.5T using the clinical scanning protocol for SRS. Additional images were acquired to implement three distortion correction methods; the field mapping (FM), mean image (MI) and signal integration (SI) techniques. Reference lesion locations were calculated as the averaged centroid positions of each target identified in the forward and reverse read gradient polarity MRI scans. The same techniques and workflows were implemented for the correction of contrast-enhanced T1w MR images of 10 patients with a total of 27 brain metastases. All methods employed in the phantom study diminished spatial distortion. Median and maximum distortion magnitude decreased from 0.7mm (2.10ppm) and 0.8mm (2.36ppm), respectively, to <0.2mm (0.61ppm) at all target locations, using any of the three techniques. Image quality of the corrected images was acceptable, while contrast-to-noise ratio slightly increased. Results of the patient study were in accordance with the findings of the phantom study. Residual distortion in corrected patient images was found to be <0.3mm in the vast majority of targets. Overall, the MI approach appears to be the most efficient correction method from the three investigated. In cranial SRS applications, patient-specific distortion correction at the target location(s) is feasible and effective, despite the expense of longer imaging time since additional MRI scan(s) need to be performed. A phantom-based QA methodology was developed and presented to reassure efficient implementation of correction techniques for sequence-dependent spatial distortion.

A Simple Modified Technique for Frameless Brain Lesion Biopsy.

Published articles pertaining to possible ways to increase the accuracy of image-guided frameless surgery are abundant in the literature. Accurate target localization is dependent on many factors, of which noteworthy is the meticulous registration and constant fixation of instruments during the procedure. Frequent changing of instruments’ application or inadvertent destabilization of its fixation during surgery after registration might disrupt the preset navigation measurements, leading to inaccurate targeting. Technical wise, we managed to avoid the drawback of moving the aiming device repeatedly during the procedure, as we will discuss later.This retrospective study aims to evaluate the feasibility and reliability of a simple frameless technique we used in navigation-guided brain biopsy and to show how it refines the accuracy of frameless biopsy procedures. All procedures were performed at our institution in the period from 2018 to 2019 and included 10 patients with different brain lesions. The mean operative time using our technique was noticeably short (18 minutes) and the standard deviation was 2.1. The used technique was easy, undemanding, and reliable in obtaining samples from brain tumors, guaranteeing more precision by applying an all-time fixed and stable navigation reference hardware.

NLOS Error Mitigation in Hybrid RSS-TOA-Based Localization Through Semi-Definite Relaxation

This letter focuses on the accurate target localization in non-line of sight (NLOS) environment through effective hybridization of time of arrival (TOA) and received signal strength (RSS) measurements. A non-linear weighted least square (NLWLS) problem is formulated from general approximation of hybrid maximum-likelihood (ML) estimation problem using balancing parameter i.e., mean NLOS effect. The NLWLS problem is then solved using semi-definite relaxation (SDR) technique which employs a novel convex hull constraint. The proposed method requires neither prior NLOS statistics nor NLOS path identification. The numerical simulations validate that the proposed method significantly outperforms the existing methods and achieves the performance comparatively closer to that of Cramer-Rao lower bound (CRLB).