Multiple Target Localization Research Articles

Effective Passive Multitarget Localization Using Maximum Likelihood

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

Range-based target localization and pursuit with autonomous vehicles: An approach using posterior CRLB and model predictive control

We address the general problem of multiple target localization and pursuit using measurements of the ranges from the targets to a set of autonomous pursuing vehicles, referred to as trackers. We develop a general framework for targets with models exhibiting uncertainty in the initial state, process, and measurement noise. The main objective is to compute optimal motions for the trackers that maximize the range-based information available for target localization and at the same time yield good target pursuit performance. The solution proposed is rooted in an estimation-theoretical setting that involves the computation of an appropriately defined Bayesian Fisher Information Matrix (FIM). The inverse of the latter yields a posterior Cramér–Rao Lower Bound (CRLB) on the covariance of the targets’ state estimation errors that can be possibly achieved with any estimator. Using the FIM, sufficient conditions on the trackers’ motions are derived for the ideal relative geometry between the trackers and the targets for which the range information acquired is maximal. This allows for an intuitive understanding of the types of ideal tracker trajectories. To deal with realistic constraints on the trackers’ motions and the requirement that the trackers pursue the targets, we then propose a model predictive control (MPC) framework for optimal tracker motion generation with a view to maximizing the predicted range information for target localization while taking explicitly into account the trackers’ dynamics, strict constraints on the trackers’ states and inputs, and prior knowledge about the targets’ states. The efficacy of the MPC is assessed in simulation through the help of representative examples motivated by operational scenarios involving single and multiple targets and trackers.

Array resource allocation for radar and communication integration network

A radar and communication integration (RCI) system has great flexibility in allocating antenna resources to guarantee both radar and communication performance. This paper considers the array allocation problems for multiple target localization and multiple platforms communication in an RCI network. The objective of array allocation is to maximize the communication capacity for each channel and to minimize the localization error for each target. In this paper, we firstly build a localization and communication model for array allocation in an RCI network. Minorization maximization (MM) is then applied to create surrogate functions for multiple objective optimization problems. The projected gradient descent (PGD) method is further employed to solve two array allocation problems with and without a certain communication capacity constraint. Computer simulations are conducted to evaluate the performance of the proposed algorithms. The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network.

Electromagnetic Navigation-Guided Preoperative Localization of Small Malignant Pulmonary Tumors

Thoracoscopic resection of small pulmonary nodules can be challenging, which highlights the importance of preoperative localization. We report our experience with electromagnetic navigation-guided localization. The clinical, radiographic, surgical, and pathologic data of patients who underwent electromagnetic navigation-guided preoperative localization for pulmonary tumors smaller than 2 cm were reviewed. Successful localization was defined as successful identification of target lesions during the thoracoscopic procedure without palpation. Included were 30 patients with 35 nodules. There were 31 transthoracic and 5 transbronchial approaches performed. One patient received both approaches for the same tumor, and 3 received both approaches for localization of multiple targets. The median nodule size was 1.0 cm (interquartile range [IQR], 0.8-1.2 cm), and the median distance from the pleural surface was 1.1 cm (IQR, 0.6-2.0 cm). The most commonly used marker for localization was dye (n= 18), followed by microcoils (n=15). In nodules located with microcoils, the median distance between the microcoil and nodule was 1 mm (IQR, 0-3 mm). There were no complications related to the localization procedure. Successful localization was achieved in 27 of 30 patients (90.0%) and in 32 of 35 nodules (91.4%). The pathologic diagnosis was primary pulmonary malignancy in 29 nodules and secondary pulmonary malignancy in6. Our experience with electromagnetic navigation-guided transbronchial and transthoracic preoperative localization of small, malignant pulmonary tumors shows this technique is feasible and appears to be a viable option for preoperative localization of pulmonary nodules that may be difficult to locate thoracoscopically.

Target Localization Algorithm of Wireless Sensor Network Based on Fast Orthogonal Matching Pursuit in

Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning. Aiming at this problem, a multi-target localization algorithm based on QR decomposition for fast orthogonal matching pursuit is proposed. The algorithm meshes the wireless sensor coverage area to design an over-complete dictionary, which transforms the multi-target localization problem into a sparse signal recovery problem. The method utilizes the sensor to receive the sparse characteristics of the target signal strength, and then uses fast orthogonal matching pursuit to recover the measured values, thereby localization the target by sparsity. Through the QR decomposition of the column full rank matrix, the recursive form is used to invert the sub-dictionary matrix, thus avoiding the direct inversion of the matrix in the traditional method, so that the computational complexity is greatly reduced. The simulation results show that compared with the traditional orthogonal matching pursuit compressed sensing reconstruction method, this method does not lose the localization accuracy and improves the computational efficiency.

A Novel Estimator for TDOA and FDOA Positioning of Multiple Disjoint Sources in the Presence of Calibration Emitters

Multiple-target localization is extensively applied in wireless connected networks. However, sensor location uncertainty is known to degrade significantly the target localization accuracy. Fortunately, calibration emitters such as unmanned aerial vehicles (UAV) with known location can be used to reduce the loss in localization accuracy due to sensor location errors. This paper is devoted to the use of UAV calibration emitters for time differences of arrival (TDOA) and frequency differences of arrival (FDOA) positioning of multiple targets. The study starts with deriving the Cramer-Rao bound (CRB) for TDOA/FDOA-based target location estimate when several UAV calibration signals are available. Subsequently, the paper presents an iterative constrained weighted least squares (ICWLS) estimator for multiple-target joint localization using TDOA/FDOA measurements from both target sources and UAV calibration emitters. The newly proposed method consists of two stages. In the first phase, the sensor locations are refined based on the calibration measurements as well as the prior knowledge of sensor locations. The second step provides the estimate of multiple-target locations by combining the measurements of target signals as well as the estimated values in the first phase. An efficient ICWLS algorithm is presented at each stage. Both the two algorithms are implemented by using matrix singular value decomposition (SVD), which is able to provide a closed-form solution and update the weighting matrix at every iteration. Finally, the convergence behavior and estimation mean-square-error (MSE) of the new estimator are deduced. Both theoretical analysis and simulation results show that the developed method can improve the TDOA/FDOA localization accuracy obviously with the help of UAV calibration emitters.

Detection and Localization of Multiple Targets in a MIMO System using DFT Wideband Waveforms

Detection and Localization of Multiple Targets in a MIMO System using DFT Wideband Waveforms

EP1.17-28 Electromagnetic Navigation-Guided Preoperative Localization for Small Malignant Pulmonary Tumors

Surgical removal of non-visible, non-palpable small pulmonary nodules is sometimes challenging during video-assisted thoracoscopic surgery (VATS) and preoperative nodule localization is of paramount importance for precise resection. We reported our experience of preoperative localization using the SPiN thoracic electromagnetic navigation system. The inclusion criteria include: 1. Patients with highly suspicious malignant pulmonary nodules according to persistent or progressive nature on follow-up CT scans, or history of extrathoracic malignancy. 2. Target nodule size less than two centimeters. 3. Presumed to be non-visible and non-palpable during VATS according to surgeon`s judgement. 4. Patients who received electromagnetic navigation for preoperative localization. As the SPiN system allows for both transbronchial (endobronchial) and transthoracic (percutaneous) routes, the optimal approach was decided according to the location of target nodule.The preoperative localization and thoracoscopic resection were performed in the same operation room by the same team. The successful localization was defined as successful identification of target tumors during VATS procedure without palpation. From Jun 2018 to March 2019, a total of 30 patients with 35 nodules were included. Thirty-one percutaneous and five endobronchial approaches were performed. One patient received both approaches for the same tumor. Three patients received both percutaneous and endobronchial approaches for multiple targets localization. The mean nodule size was 9.6 ±3.5 mm. The materials used for marking included dye (n = 18), micorcoil (n = 4), indocyanine green (ICG, n = 2), microcoil + dye (n = 8), and microcoil + ICG (n = 3). There was no localization-related adverse event. Successful localization was achieved in 27 of 30 (90.0%) patients and 32 of 33 35 (91.4%) nodules. In three patients, localization failed as dye extravasation in the pleural cavity without any stains in the subpleural parenchyma. The pathological diagnosis included metastatic tumors (n = 6), primary pulmonary squamous cell carcinoma (n = 2), and primary pulmonary adenocarcinoma (invasive adenocarcinoma, n = 9; minimally invasive adenocarcinoma, n = 12; adenocarcinoma in situ, n = 6). We reported our experience of electromagnetic navigation-guided transbronchial and transthoracicpreoperative localization for small malignant pulmonary tumors. Lessons from failed cases were learned and localization techniques were modified to improve successful localization rate.

Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm.

Passive Multiple Target Indoor Localization Based on Joint Interference Cancellation in an RFID System

Radio frequency identification (RFID) provides a simple and effective solution to the passive indoor localization. The conventional wisdom about RFID localization is using reference tags. It performs well in tag or passive single target localization. However, in the passive multiple target scenario, reference tag based localization suffers from some limitations, including the array aperture, mutual coupling of reference tags, and coherent superimposition signals. These problems are harmless and ignored in tag or passive single target localization, but degrade the performance severely in passive multiple target scenario. Therefore, in this paper, the authors propose a joint interference cancellation method to mitigate the effect of these limitations. Uniform circular array (UCA) of reference tags were used to reduce the interference of the array aperture. A carefully designed relative position of adjacent reference tags and a modified channel model were combined to reduce the mutual coupling. A virtual distributed reader antenna array was used to reduce the false positive and false negative estimations. The system was evaluated in real indoor environment using noodles and colas as targets and can work well in a smoky environment that is similar to some real industrial environments. The accuracy of target number estimation is 97.5%. The spatial resolution is about 30 cm, and the median error of 2-D multiple target localization is about 5.5 cm.

A nested-PARAFAC based approach for target localization in bistatic MIMO radar systems

Bistatic MIMO radar systems gather several advantages such as increased resilience to electronic countermeasures, unknown receiver location, higher identifiability of targets and direct application of several high resolution adaptive techniques. In this paper, we propose a tensor-based method for joint direction of departure (DoD) and direction of arrival (DoA) estimation in bistatic MIMO radar systems. By assuming that the transmit array is divided into two maximally overlapping subarrays, we initially model the cross-covariance matrix of the matched filters outputs as a Nested-PARAFAC decomposition of a fourth-order covariance tensor. Then, exploiting the structure of this decomposition, we first propose a two stage algorithm for joint DoD and DoA estimation of multiple targets based on double alternating least squares (DALS). In addition, for scenarios in which the number of receive antennas exceeds the number of targets, we propose a closed-form solution to the second stage of the proposed method based on the least squares Khatri-Rao factorization (LS-KRF) concept. Simulation results show that the proposed method offers a highly-accurate localization of multiple targets in real-world scenarios where the antenna elements at the transmit and receive arrays have positioning errors as well as less complexity compared to competing state-of-the-art tensor-based solutions.

A Linear Estimator for Network Localization Using Integrated RSS and AOA Measurements

This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.

Multitarget Localization With Inaccurate Sensor Locations via Variational EM Algorithm

In wireless sensor networks, target localization has been the focus of considerable research effort, and the compressive sensing (CS) based localization method is of particular interest. However, most existing works usually assume that the positions of sensors are known exactly, while in practice, they may not be accurate. When the assumption is violated, the localization performance will deteriorate dramatically. In this article, we propose a novel CS-based multiple target localization method using the superimposed received signal strength, measured by sensors with inaccurate positions. In order to address such issues, we regard the known but inaccurate sensor locations as adjustable parameters. Accordingly, the sensor positions can be refined through the adjustment of parameters. As a result, the problem is reformulated as a joint sparse signal estimation and parameter optimization task. Then, the variational expectation-maximization (EM) algorithm and the subspace trust-region method are applied to iteratively estimate the unknown target locations and refine inaccurate sensor locations. Simulation results are included to confirm the superior localization performance of the proposed algorithm by comparing with the state-of-the-art methods.

Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras.

This paper proposes a novel unified framework to solve the 3-D localization and tracking problem that occurs multiple camera settings with overlapping views. The main challenge is to overcome the uncertainty of the back projection arising from the challenges of ground point detection in an environment that includes severe occlusions and the unknown heights of people. To tackle this challenge, we establish a Bayesian learning framework that maximizes a posterior over the trajectory assignments and 3-D positions for given detections from multiple cameras. To solve the Bayesian learning problem in a tractable form, we develop an expectation-maximization scheme based on the variation inference approximation, where the probability distributions are designed to follow Boltzmann distributions of seven terms that are induced from multicamera tracking settings. The experimental results show that the proposed method outperforms the state-of-the-art methods on the challenging multicamera data sets.

Indoor multiple human targets localization and tracking using thermopile sensor

Thermal sensors have been widely applied for human targets in battlefield surveillance and smart housing. A low-resolution thermopile sensor—GridEye is used to localize and track indoor multiple human targets. Interpolation is introduced to increase the resolution of sensor data and Gaussian filter is designed to smooth the data. An adaptive threshold method is designed to remove non-human thermal targets and background image. The human targets are located by considering the space deployment and the viewing angle effect of GridEye. Depending on target positions and the heat signatures of human targets, the targets are associated with their respective trajectories. The experimental results have shown that multiple humans can be tracked well in the indoor environment.

Block Variational Bayesian Algorithm for Multiple Target Localization With Unknown and Time-Varying Transmit Powers in WSNs

Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.

Cooperative guidance of a fleet of UAVs for multi-target discovery and tracking in presence of obstacles using a set membership approach

Abstract This paper presents a set-membership approach for the coordinated control of a fleet of UAVs aiming to search and track an a priori unknown number of targets spread over some area of interest. The originality of the approach lies in the description of the perturbations and measurement uncertainties via bounded sets. Sets guaranteed to contain the actual state of already detected targets are provided using the set-membership approach. A specific criterion evaluating the uncertainty of the detected and still to be detected target has been defined, which is used to determine the trajectories of the UAVs so as to decrease the global estimation uncertainty. The criterion accounts for potential occlusions in the field of view of UAVs due to obstacles located on the zone. Simulations show that the proposed control input optimization is able to provide good localization and tracking performance for multiple targets.

Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging.

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

Grid Evolution: Joint Dictionary Learning and Sparse Bayesian Recovery for Multiple Off-Grid Targets Localization

In this letter, we propose an efficient grid evolution multiple targets localization framework for off-grid targets. First, we propose a more accurate localization model, enabling grid evolution by considering all the grids as random variables to be inferred. Then, the localization problem is formulated as a joint sparsifying dictionary learning and sparse signal recovery problem. Finally, the joint optimization problem is solved under the general framework of sparse Bayesian learning (SBL). Different to previous SBL based localization algorithms, we adopt the hierarchical Laplace distribution for sparse prior, rather than the Sudent’s t distribution. We compare the proposed framework with state-of-the-art off-grid targets localization algorithms as well as Cramer–Rao lower bound. Numerical simulations highlight the improved performance of the proposed framework in terms of localization error, noise robustness, and required number of measurements.