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In a localization environment based on wireless sensor networks, the non-line-of-sight (NLOS) propagation of wireless signal can greatly degrade the performance of the localization system. In this paper, a novel semidefinite-relaxation-based target localization method is presented by utilizing an adaptive threshold correction scheme. First, whether the measurement is in NLOS propagation is identified according to the prediction stage of extended Kalman filtering (EKF) algorithm. Then, the objective function and constraints are, respectively, constructed according to the measurement model of time of arrival (TOA) and received signal strength indicator (RSSI) ranging method. By employing the semidefinite relaxation method, the original problem is converted into the SDP problem, in which the corrected measurement information is obtained and thus the accuracy is improved. In addition, a correction method for NLOS propagation threshold is proposed by which an optimal threshold is obtained quickly. Different from the traditional simulation environment without the consideration of obstacles, in this paper, obstacles are set so as to make it closer to the engineering practice. Finally, the simulation results are given to show the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a hybrid robust sequential fusion estimation (HRSFE) method for moving-target localization in mobile asynchronous wireless sensor networks (WSNs). The estimation error or inconsistency, caused by the uncertainties of sampling, communication, and sensor-node position, is considered fully and compensated by incorporating the sensor-node position with uncertainty into the state vector as well as introducing an adaptive fading factor into the cubature Kalman filter (CKF) to maintain the estimation consistency. In addition, a QR-decomposition-based square-root form of the cubature strong localization filter (SR-CSLF) is proposed to improve the accuracy and stability of the CSLF. Moreover, an HRSFE method is designed to estimate the augment state of the WSN-assisted moving-target-localization system, and the proposed estimation method combines the merits of both the SR-CSLF and SR-CKF effectively and is able to deal with the uncertainties of sampling, communication, and sensor-node position in a unified framework. Simulations and experiments of a WSN-assisted moving-target-localization system under various uncertainty settings are employed to demonstrate the effectiveness and superiorities of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, the problem of 3-D target localization from bistatic range measurements in multiple-input–multiple-output (MIMO) radars with distributed antennas is considered. A closed-form two-stage weighted least squares solution is developed, which is able to uniquely determine the target position with a smaller number of sensors (transmitters or receivers) compared to the other existing closed-form methods. The proposed method employs several weighted least-squares minimizations and does not require any initial solution guesses to give the target position estimate. A detailed theoretical performance analysis associated with the method as well as the Cramer–Rao lower bound (CRLB) are provided. The proposed estimator is shown theoretically to achieve the CRLB accuracy under small Gaussian noise. Computer simulations are included to corroborate the theoretical development and to compare the estimator performance with several previous approaches. The proposed algorithm in the considered example is shown to outperform the existing closed-form estimators. In the 3-D and in the case of a minimum number of sensors (i.e., two transmitters and two receivers) in a MIMO radar, the proposed method is also shown to be able to localize a target with high accuracy while the previous closed-form methods cannot uniquely determine the target position. [ABSTRACT FROM AUTHOR]

Summary: In order to enable the multi‐agents to elliptically circumnavigate the multi‐targets in more complex environment, we propose a geometric center estimator and an elliptical circumnavigation controller in two‐dimensional space by only employing bearing measurements without knowing the target's position and velocity. The stability of the algorithm is proved for both stationary targets and dynamic targets. Finally, a series of numerical simulations is presented to verify the correctness of the algorithm both in ideal networks and in networks with communication delays. [ABSTRACT FROM AUTHOR]

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system. [ABSTRACT FROM AUTHOR]

A weighted total least squares method based on double error model is proposed for the target localization in the simultaneous presence of measurement error and sensor node position error in underwater acoustic sensor networks. A TDOA target localization model with double errors is established. The weighted total least squares method is used to solve the proposed model, and the expression of the weights is derived. The proposed algorithm model is to use the prior information of measurement error variance and sensor node position error variance to obtain the final target position estimate by iteration method. The simulation and water tank experimental verification of the proposed method were made by simulating the simultaneous existence of the measurement error and the sensor node positions error. The results show that the proposed target localization method based on weighted total least squares has higher localization performance. [ABSTRACT FROM AUTHOR]

In this letter, received signal strength (RSS) based target localization is studied. First we start with the case of known emitted power of target. The least squares (LS) approach is adopted, and the object function is shown to be decomposed as a convex quadratic plus a concave term. Consequently, a Majorization-Minimization (MM) based approach from range measurements termed R-MM is proposed, which iteratively finds the local optimum through simple operations. To further improve the performance, unconstrained squared-range (USR)-based LS estimation is used as an initial point for R-MM, named R-MM-USR. Then R-MM-USR is extended to solve the target localization under unknown emitted power through alternating minimization. For both cases Cramèr Rao bound (CRB) is derived. In addition, the computation complexity of R-MM-USR is lowest among the high accuracy approaches. Besides, numerical simulations are conducted to demonstrate the near optimal performance, compared with CRB and other state-of-art methods. [ABSTRACT FROM AUTHOR]

This paper is focused on noncooperative target position estimation via the joint use of two-dimensional (2-D) hyperbolic and elliptic passive location techniques based on time difference of arrival (TDOA) and passive coherent locator (PCL) measurements, respectively. A fusion strategy is laid down at the signal processing level to obtain a reliable estimate of the current target position. With reference to the scenario with a single transmitter of opportunity, the mathematical model for joint exploitation of TDOA and PCL strategies is formulated. Then, the Cramer–Rao lower bound (CRLB) for the Cartesian coordinates of the target is established and the theoretical performance gains achievable over the localization technique using only TDOA or PCL observations are assessed. Finally, TDOA-PCL hybrid 2-D localization algorithms are provided and their performance in terms of root-mean-square error is compared with the square root of the CRLB. [ABSTRACT FROM AUTHOR]

Utilizing the bistatic range (BR) and bistatic range rate (BRR) obtained from time delay difference and Doppler shift difference, a moving target can be localized with the passive multistatic radar (PMR) network. This paper presents a two-stage closed-form method for target localization in PMR. Different from the conventional two-stage weighted least squares (TSWLS) methods, the proposed method employs the weighted spherical-interpolation method to obtain an initial estimate in the first stage and reduces the error in the initial estimate with the deviation refinement in the second stage. Theoretical analysis through mean-square error (MSE) shows the proposed method approaches the Cramér-Rao lower bound (CRLB) accuracy under the assumption of mild measurement noises. Simulation results validate the analytical results. The proposed method is also shown to achieve the accuracy improvement in target velocity estimate at relatively higher noise levels. [ABSTRACT FROM AUTHOR]

In this paper, a novel algebraic closed-form method is proposed to estimate the position and velocity of a moving target in distributed multiple-input multiple-output radar systems with erroneous sensor locations by utilizing time delay and Doppler shift measurements. Unlike the existing methods that introduce nuisance parameters to build the pseudo-linear equations, the proposed method uses the singular value decomposition approach to establish new linear equations with regard to the target location with no nuisance parameters, and then derives a weighted least-squares (WLS) solution. To further improve the localization accuracy, the solution is refined through estimating the error by another WLS estimator. Based on the theoretical derivation and numerical simulations, the proposed estimator is demonstrated to be approximately unbiased and can attain the CRLB under small noise conditions. Moreover, the simulation results show that the proposed method achieves better target location accuracy than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this letter, an efficient estimator for 3-D target localization in distributed multiple-input multiple-output (MIMO) radars using time delay (TD) and angle of arrival (AOA) measurements is proposed. First, an approximately equivalent maximum likelihood (ML) estimation problem is formulated. Then, the aforementioned ML problem is recast into a convex optimization problem for which we derive a semi closed-form solution that eventually boils down to finding the roots of certain polynomials. Using numerical simulations, we demonstrate that the proposed estimator reaches the Cramer-Rao lower bound (CRLB) up to relatively high Gaussian measurement noise levels. Furthermore, the proposed method is shown to outperform the state-of-the-art algorithms under the above-mentioned noise model. [ABSTRACT FROM AUTHOR]

In this letter, the moving target localization problem is studied under distributed multiple-input multiple-output radar systems. Based on the two-stage weighted least squares framework, a closed-form solution of target position and velocity is derived. In the first stage, linear equations about target position and velocity are established with no nuisance parameters. Then the estimate is achieved by a weighted least squares (WLS) estimator. In the second stage, the estimation error of the first stage is estimated by another WLS method to refine the localization performance. The final estimate, combining solutions of the first stage and the second stage, is approximately unbiased and its covariance is equal to the Cramer-Rao lower bound under small noise conditions through the theoretical derivation and numerical simulations. Simulation results demonstrate the proposed method achieves better location accuracy than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The variance of distance-dependent noise exhibits different correlations with distance when the measurement method or device cost varies due to environmental interference. To efficiently model this correlation with less time cost, Gaussian Process Regression (GPR), which is characterized by its ability to learn nonlinear relationships with a small number of training samples, is utilized. Then, using the chain rule, the general CRLB can be applied under different measurement methods. It is represented by a function containing the GPR-based model and the relative position of the target and anchor. This means that the corresponding CRLB can be obtained quickly only by modeling the distance-dependent noise and determining measurement methods. Finally, to demonstrate the effectiveness of the GPR-based model and the validity of the derivation in this paper, two examples of time-of-arrival-based (TOA-based) and received signal strength-based (RSS-based) experiments are presented. • The distance-dependent noise under different measurement methods or device costs is modeled by drawing on the characteristics of GPR, namely the limited number of training samples and the strong ability to learn nonlinear relationships. • We derived the general CRLB expressions considering distance-dependent noise, whatever the differences of measurement method or device cost. • By decoupling the GPR-based model of distance-dependent noise and relative positions of the sensors, we can rapidly derive the CRLB expressions for different device cost and measurement methods. [ABSTRACT FROM AUTHOR]

Circular targets, as one of typical image features, are widely used in image-based measurement, such as photogrammetry, displacement sensing, and vibration monitoring. However, its positioning accuracy is usually sensitive to intensity changes in ambient lighting, particularly in outdoor applications and long-term monitoring scenarios. In this paper, a speckle-based circular target (SbCT) is introduced as a solution to the sensitivity of localization accuracy of traditional circular targets in illumination variation environment. By incorporating a speckle pattern on the circular target and utilizing digital image correlation method, the SbCT achieves insensitivity to illumination variations when an appropriate correlation function is chosen. This enables accurate and reliable measurements even in lighting varying environment. This paper initially outlines the design strategy of the SbCT and demonstrates its extraction procedure. Subsequently, simulations are conducted to analyze the positioning accuracy of SbCT under linearly varying illumination situations. Additionally, experiments are performed to study the accuracy and robustness of SbCT with the comparison to concentric circular targets under different lighting variation cases. The results from both simulation and experiment demonstrate the illumination robustness of SbCT, of which the stability is better than 0.01 pixel. According to the displacement tracking experiment, the relative error obtained by SbCT is smaller than 0.2%. Therefore, it can be concluded that SbCT is an illumination-robust feature, offering automatic recognition and accurate localization performance even in illumination varying situations. • A new type of target is presented combined with speckle pattern and circular shape. • SbCT exhibits insensitivity to both linear and nonlinear illumination variations. • SbCT can offer more accurate measurement data under lighting changing cases. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid localization method that combines the time difference of arrival (TDOA) and angle of arrival (AOA) measurements to achieve improved accuracy with two base stations. Unlike other subspace-based methods that require calculating the eigenbasis, our proposed method constructs a Krylov subspace basis using the residual vectors of the Conjugate Gradient (CG) algorithm, which reduces computational complexity. In our approach, the measurements obtained from AOA estimation serve as initial estimates in our Adaptive Conjugate Gradient Algorithm (ACGA). ACGA incorporates an innovative adaptation mechanism that utilizes the residual error for updating and refining the estimated target position. This adaptation strategy allows our algorithm to iteratively improve localization accuracy, especially in the presence of NLOS errors. To assess the performance of our method, we conduct exhaustive simulations, considering various NLOS error scenarios and comparing them against conventional methods and extensively developed algorithms. The results demonstrate that our proposed method surpasses existing techniques, achieving a more densely spaced estimated position of the target in noisy environments with only two base stations. Furthermore, our method approaches the Cramer Rao Lower Bound (CRLB), indicating its high accuracy and efficiency in estimating the target position in typical NLOS environments. The effectiveness and potential of the proposed hybrid TDOA-AOA localization method are evident from the superior performance demonstrated in our simulations. These findings highlight the practical significance of our approach for real-world applications where accurate target localization is crucial, such as wireless communication, radar systems, and autonomous navigation. • Hybrid TDOA-AOA Method: Innovative hybrid fusion of TDOA-AOA for target localization with two base stations. • Efficient Basis Construction: Novel Krylov subspace technique that reduces complexity in challenging environment. • Adaptive Refinement: An adaptive mechanism that refines target estimates iteratively. • Superior Performance: Performance achieves densely spaced target positions in noisy environment. • Practical Applications: Significant in wireless communication, radar and navigation. [ABSTRACT FROM AUTHOR]

We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

In this article, a new technique for determination of 2D signal source (target) position is proposed. This novel approach, called the Inscribed Angle (InA), is based on measuring the time difference of sequential irradiation by the main beam of the target antenna’s radiation pattern, using Electronic Support Measures (ESM) receivers, assuming that the target antenna is rotating and that its angular velocity is constant. In addition, it is also assumed that the localization system operates in a LOS (Line of Sight) situation and that three time-synchronized sensors are placed arbitrarily across the area. The main contribution of the article is a complete description of the proposed localization method. That is, this paper demonstrates a geometric representation and an InA localization technique model. Analysis of the method’s accuracy is also demonstrated. The time of irradiation of the receiving station corresponds to the direction in which the maximum received signal strength (RSS) was measured. In order to achieve a certain degree of accuracy of the proposed positioning technique, a method was derived to increase the accuracy of the irradiation time estimation. Finally, extensive simulation was conducted to demonstrate the performance and accuracy of our positioning method.

The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.

Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.

Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method. [ABSTRACT FROM AUTHOR]

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment. The effectiveness of energy–accuracy tradeoff is also validated. [ABSTRACT FROM AUTHOR]

Adaptation and learning over low-cost wireless networks, meanwhile keeping an acceptable performance, are well motivated. This paper focuses on online parameter estimation over binary networks, which consist of noisy low-resolution sensors, each only giving coarsely one-bit quantized output observations and transmitting them to a fusion center. We develop a class of recursive least-squares (RLS) algorithms based on an expectation–maximization framework, which realizes adaptive parameter estimation from one-bit observations of the noisy output stream. The developed algorithms are, respectively, derived with and without prior knowledge of the noise variances, and their performances are theoretically and experimentally evaluated. Moreover, it is shown that, although the information contained in the one-bit observations is very limited, the proposed algorithms are comparable to the classical RLS algorithm using the original (nonquantized) observations. In addition, as a practical application, the proposed algorithm combined with array signal processing techniques is applied to bearing-only target localization over wireless sensor array networks, and its effectiveness is verified through simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, an algebraic solution for elliptic localization in multiple-input multiple-output (MIMO) radar systems is proposed by taking the uncertainties in antenna positions and their clock parameters into account. Through nonlinear transformation and multi-stage processing, the target position is estimated in closed-form by successively utilizing the weighted least squares estimator. Theoretical analysis demonstrates that the proposed method is able to attain the Cramer-Rao lower bound (CRLB) performance under mild Gaussian error. The conditions for achieving the CRLB are derived as well. The numerical simulations confirm the analytical results and demonstrate significant performance improvement of the proposed estimator in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A new subspace method is proposed to solve the bearing-only target localization (BOTL) problem. Instead of linearizing the nonlinear bearing equations to form least-squares optimization, we construct a scalar product matrix by making full use of all bearing and intersensor bearing geometric information. After exploiting the dimension and eigenstructure of the scalar product matrix, we devise a subspace algorithm for BOTL with its weights computed from a prior location estimate. Simulation results show that the proposed algorithm achieves a mean square error performance close to the Cramér–Rao lower bound, even at high noise levels. Field experiments demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Sensor deployment positions play an important factor in determining target location estimation error performance in sensor networks employing received signal strength indicator measurements. The problem we investigate is as follows: Given a deployment area that has possibly non-uniform estimation error requirements and some initial sensor positions, then how can these sensors be deployed such that requirements are met as best as possible? We propose three variants of a low-complexity distributed strategy that require little information exchange between neighboring sensors. Each sensor locally calculates both the direction and magnitude of movement necessary to reduce the difference between achieved and required estimation errors at points within it sensing radius. The direction of movement is calculated as a weighted combination of these points. The weights can incorporate the error difference, number of sensors covering a point, the distance to a sensor and sensor density. Depending on the parameters chosen, three different weighing methods (namely, the density weighted centroid, error weighted centroid (EWC) and modified EWC) are proposed in this paper. We also provide an analytic derivation of the necessary distance a sensor should move. The proposed strategy is compared against a the RELOCATE algorithm and centralized generic genetic algorithm (GA) relocation method. Simulation results demonstrate that using the distributed strategy can achieve a comparable performance (within 10%) of the GA's performance and significantly outperform that of the RELOCATE. Sensor movement for different k. [ABSTRACT FROM AUTHOR]

The overall localization error performance for a sensor network employing quantized received signal strength (RSS) measurements largely depends on the number of bits used and their distribution. In this paper, we consider a rate-constrained bit allocation problem in which multiple points of interest (PoIs) are assigned possibly non-uniform error requirements. The main objective is to allocate a number of bits in order to satisfy error requirements as best as possible in a squared error sense. Due to the computational complexity of optimally solving the allocation problem, two novel low complexity allocation algorithms are proposed. The main idea behind these algorithms is to relate localization errors to corresponding bit density requirements. In the first algorithm, bit allocation is formulated as a sequential constrained convex optimization problem which can be solved with efficiency using available convex solvers. The second algorithm is a greedy version of the first algorithm in which sensors are selected for allocation based on the bit density differences at their neighboring PoIs. Numerical experiments show the efficacy of the proposed algorithms in comparison to other available algorithms in terms of overall error, coverage, and computational complexity. [ABSTRACT FROM AUTHOR]

• Variations in patients' breathing characteristics during treatment were presented. • Limitation in the use of the ABC related to airflow rate was addressed. • Interfraction variation in breath-hold volume was investigated for the whole course. • Intrafraction variation in breath-hold volume was investigated for the whole course. • Target locations correlated to breath-hold volume were statistically analyzed. Conventionally fractionated and stereotactic body radiation therapy (SBRT) for thoracoabdominal tumors may utilize breath-hold techniques. However, there are concerns that differential amounts of inspired airflow may result in unplanned tumor dislocation and underdosing. Thus, we investigated tumor localization accuracy associated with lung volume variations during breath-hold treatment via an automated-gating interface. Twelve patients received breath-hold treatment with the active breathing coordinator (ABC) through an automated-gating interface. All breath-hold volumes were recorded at CT simulation, setup imaging, and during treatment, and analyzed as a function of airflow rate into the ABC. The variation of breath-hold volumes was calculated for each fraction over entire course. Intrafraction target motion related to the breathing variation was investigated based on daily imaging acquired before the breath-hold treatment. Correlation between target location and breath-hold variation was statistically analyzed. The air volume held by the ABC increased as the airflow rate increased on inhalation and decreased on exhalation. The mean range of airflow rate was 0.77 L/s and 0.29 L/s in the conventionally fractionated and SBRT patients, respectively. The maximum air volume difference with respect to the reference volume at the CT simulation was 1.0 L for conventional fractionation and 0.16 L for SBRT. The target dislocation caused by 0.25 L of air volume difference was 6 mm for SBRT. Three patients showed significant correlation between the target location and breath-hold variations. This investigation shows that because variations in the breath-hold volume may cause target dislocation, patient-specific breath-hold setting is required to improve tumor localization accuracy. [ABSTRACT FROM AUTHOR]

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High-resolution video images contain huge amount of data so that the real-time capability of image registration and target localization algorithm is difficult to be achieved when operated on central processing units (CPU). In this paper, improved ORB (Oriented FAST and Rotated BRIEF, FAST, which means "Features from Accelerated Segment Test", is a corner detection method used for feature points extraction. BRIEF means "Binary Robust Independent Elementary Features", and it's a binary bit string used to describe features) based real-time image registration and target localization algorithm for high-resolution video images is proposed. We focus on the parallelization of three of the most time-consuming parts: improved ORB feature extraction, feature matching based on Hamming distance for matching rough points, and Random Sample Consensus algorithm for precise matching and achieving transformation model parameters. Realizing Compute Unified Device Architecture (CUDA)-based real-time image registration and target localization parallel algorithm for high-resolution video images is also emphasized on. The experimental results show that when the registration and localization effect is similar, image registration and target localization algorithm for high-resolution video images achieved by CUDA is roughly 20 times faster than by CPU implementation, meeting the requirement of real-time processing. [ABSTRACT FROM AUTHOR]

This paper considers the problem of two-dimensional constrained trajectory optimization of a point-mass aerial robot for constant-maneuvering target localization using bearing-only measurement. A performance metric that can be utilized in trajectory optimization to maximize target observability is proposed first based on geometric conditions. One-step optimal maneuver that maximizes the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal maneuver derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings. [ABSTRACT FROM AUTHOR]

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a novel scheme to solve the target localization problem by using combined measurements of received signal strength and angle-of-arrival (AOA) hybrid measurements in three-dimensional (3D) wireless sensor networks (WSNs). The proposed scheme can work effectively in both cases of known and unknown target transmit power. In the known transmit power case, by using polarized identity, we transform the available AOA measurements into a norm form and establish new relationships between the AOA measurements and the unknown target location, a novel target localization method is then proposed by using the second-order cone programming relaxation technique. Furthermore, we demonstrate that by using mean inequality, the proposed method can be effectively extended to the unknown transmit power case. In this case, we propose a practical alternating estimation procedure which alternatively estimates the target location and transmit power. Computer simulations are carried out to demonstrate that comparing with other methods, the proposed method can achieve significant performance gains for different settings in 3D WSNs. [ABSTRACT FROM AUTHOR]

Background: Sedatives and opioids used during deep brain stimulation (DBS) surgery interfere with optimal target localization and add to side effects and risks, and thus should be minimized. Objective: To retrospectively test the actual need for sedatives and opioids when cranial nerve blocks and specific therapeutic communication are applied. Methods: In a case series, 64 consecutive patients treated with a strong rapport, constant contact, non-verbal communication and hypnotic suggestions, such as dissociation to a "safe place," reframing of disturbing noises and self-confirmation, were compared to 22 preceding patients under standard general anaesthesia or conscious sedation. Results: With introduction of the protocol the need for sedation dropped from 100% in the control group to 5%, and from a mean dose of 444 mg to 40 mg in 3 patients. Remifentanil originally used in 100% of the patients in an average dose of 813 µg was reduced in the study group to 104 µg in 31% of patients. There were no haemodynamic reactions indicative of stress during incision, trepanation, electrode insertion and closure. Conclusion: With adequate therapeutic communication, patients do not require sedation and no or only low-dose opioid treatment during DBS surgery, leaving patients fully awake and competent during surgery and testing. [ABSTRACT FROM AUTHOR]

Localization of multitarget by means of a network of dispersed radar sensors using Doppler-only measurement faces the problem of data association, i.e., associating the measured Doppler-shifts with their originated targets. Conventionally, this association is realized based on correlations between successive estimations of targets' state. However, up to now, no data association method is available for localization using only one snapshot measurement. This paper tries to utilize the relevance of measurements in spatial domain instead of in time domain. An approach based on a clustering method, i.e., generalized fuzzy algorithmic scheme (GFAS), is introduced to solve the data association problem with a single snapshot. First, the measured data are formed as a matrix. Then, a clustering method is used for data association. This clustering is a procedure of collecting information of each target from the measurements of multiple sensors. Thus, the results of clustering will be used to associate the measured data with their originated targets. In order to overcome the problem that GFAS may associate more than one piece of data measured by the same sensor with a same target, an improved GFAS (IGFAS) is proposed. Different from other methods for data association, only one snapshot is required in the proposed method. Furthermore, the computational complexity will not increase exponentially as the number of targets or sensors increase. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Frequency diverse array (FDA) has received much attention, but little work about FDA receiver architectures and receiver processing algorithms have been reported. In this paper, we analyze the localization performance of FDA radar with a two-stage estimator. Three FDA receive architectures are discussed and compared with the phased-array (PA). In order to overcome disadvantages of the monochromatic FDA radar signal model adopted in the literature that ignores the coherent transmit gain in FDA radar, we formulate a bandwidth model for a general FDA radar received signals including both nonoverlapping and overlapping spectra. The localization performance are assessed by the Cramér–Rao bound (CRB) and receive signal noise ratio. The proposed model provides more accurate representation of FDA radar received signals and more reasonable comparisons between FDA and PA radars. Additionally, a fast two-stage estimator is proposed to reduce the two-dimensional multiple signal classification computation complexity. All proposed methods are verified by both theoretical analysis and numerical results. [ABSTRACT FROM AUTHOR]

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

Unmanned aerial vehicles (UAVs) play a key role in modern surveillance-related missions. A major task for performing these missions is to find the precise location of a moving target in real-time, for which the main challenge is to estimate the target position to high precision using the noisy measurements from the airborne sensors. In this paper, we present a closed-form on-line simultaneous target localization and UAV trajectory optimization method based on the visual platform, which can effectively minimize the localization uncertainty to the target. The proposed method can be elucidated explicitly using two phases, of which, in the target localization phase, the expended information filtering (EIF) is exploited, which can express the predicted Fisher information matrix (FIM) of the target explicitly and iteratively, and in the UAV trajectory optimization phase, the property of the predicted FIM is exploited to establish the UAV waypoint optimization objective by taking account of the UAV motion limit. Compared with existing methods of the same class, the proposed method not only estimates the next target position more correctly, but also takes the error of the target motion into consideration, thus improving the effectiveness of the optimized UAV trajectory. Both simulations and field experiments were conducted, which show that the proposed method outperformed the existing methods.

• We propose Electroanatomic map based target localization for STAR. • EAM registration is sufficient to localize treatment region. • Five of 7 patients demonstrated a reduction in ventricular tachycardia events. Identifying the target region is critical for successfully treating ventricular tachycardia (VT) with single fraction stereotactic arrhythmia radioablation (STAR). We report the feasibility of target definition based on direct co-registration of electroanatomic maps (EAM) and radioablation planning images. The EAM consists of 3D cardiac anatomy representation with electrical activity at endocardium and is acquired by a cardiac electrophysiologist (CEP) during electrophysiology study. The CEP generates an EAM using a 3D cardiac mapping system anticipating radioablation planning. Our in-house software read these non-DICOM EAMs, registered them to a planning image set, and converted them to DICOM structure files. The EAM based target volume was finalized based on a consensus of CEPs, radiation oncologists and medical physicists, then expanded to ITV and PTV. The simulation, planning, and treatment is performed with a standard STAR technique: a single fraction of 25 Gy using volumetric-modulated arc therapy or dynamic conformal arc therapy depending on the target shape. Seven patients with refractory VT were treated by defining the target based on registering EAMs on the planning images. Dice similarity indices between reference map and reference contours after registration were 0.814 ± 0.053 and 0.575 ± 0.199 for LV and LA/RV, respectively. The quality of the transferred EAMs on the MR/CT images was sufficient to localize the treatment region. Five of 7 patients demonstrated a dramatic reduction in VT events after 6 weeks. Longer follow-up is required to determine the true safety and efficacy of this therapy using EAM-based direct registration method. [ABSTRACT FROM AUTHOR]

Object detection and target localization are important technologies in image processing and computer vision, which have a wide range of applications in agricultural systems. By fusing the YOLOv5 algorithm and monocular vision-based method, a target localization algorithm is proposed to accurately identify and locate the various objects in agricultural scenes. The GPS information of the target object can be obtained by means of processing the attitude angle information of the unmanned aerial vehicle (UAV) at the time when it takes pictures. The superiority of the proposed method is demonstrated through the test on the agricultural scenario dataset taken by UAV, with the experimental results showing the algorithm's satisfactory speed and accuracy. [ABSTRACT FROM AUTHOR]