Passive Target Localization Research Articles

Platform Design of Passive Target Perception and Localization Based on Sensor Networks

With the rapid development, extensive knowledge, and diverse application scenarios of target perception and positioning technology in sensor networks, a passive target perception and localization platform based on Wireless Sensor Networks (WSN) has been designed. The platform is mainly applied for the teaching of electronic information, communication, and Internet of Things (IoT) engineering. The platform follows the teaching concept of “developing students’ ability to solve complex engineering problems” in the emerging engineering discipline and combines experimental simulation with real-world testing, as well as integrating scientific research and teaching. It encompasses technical elements such as perception and localization theory, sparse representation classification modeling, solving underdetermined equations, data analysis, and sparse coding, as well as nontechnical aspects such as team collaboration and cost budgeting. The platform boasts high fidelity and scalability, providing students with the opportunity to develop comprehensive practical and innovative skills in solving complex engineering problems.

UAVs-assisted passive source localization using robust TDOA ranging for search and rescue

Estimating the location of a target in search-and-rescue operations is quite challenging when the target is not responding. Therefore, in this paper, we investigate the passive target localization problem using mobile unmanned aerial vehicles (UAVs), where multiple mobile UAVs receive the time-difference-of-arrival (TDOA) measurements from the source UAV. Unlike traditional TDOA for static UAVs, the problem becomes more challenging when considering the mobility of UAVs. Therefore, we propose a novel TDOA model for target localization with mobile UAVs. We also measure the performance limit inequality between its Cramer-Rao lower bound (CRLB) and the mean-squared error (MSE).

An Improved Chaos Driven Hybrid Differential Evolutionand Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements

This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.

Outlier-Robust Passive Elliptic Target Localization

Outlier-Robust Passive Elliptic Target Localization

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization.

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).

Target Localization and Power Allocation Using Wireless Energy Harvesting Sensors

As radio-frequency (RF) based wireless energy harvesting technology can provide remote and continuous power to low-power devices, e.g., wireless sensors, it may be a substitute for batteries and extend the lifetime of the wireless sensor networks. In this paper, we propose a wireless energy harvesting localization system (WEHLoc), which contains batteryless wireless sensors as anchors and an energy access point (E-AP) to transfer power to the anchors. We consider a passive target localization scenario, in which the anchors monitor the target and send the sensed ranging data back to the E-AP. Additionally, we formulate the optimal estimation accuracy problem which is a 0–1 mixed-integer programming problem and relates to the energy beam, target transmitted power, and deployed anchor density. Then, we develop the power allocation scheme of the E-AP to solve the objective. In order to reduce the complexity, we propose a heuristic method that converts the maximum estimation accuracy problem into the energy efficiency problem and use linear programming to solve them. The simulations demonstrate that WEHLoc can be massively deployed in a wide area, and the estimation error and the power consumption are relatively low.

Observability Study on Passive Target Localization by Conic-Angle Measurements.

Information from a passive linear array sensor is related to the conic angle formed by a target and the sensor in three-dimensional (3D) space so that the target localization system using the sensor should be also designed in 3D space. This paper presents an observability study of a passive target localization system created using conic angle information. The study includes the analysis of the sensor maneuver requirement needed to achieve system observability and simulations to demonstrate the results of the analytic scheme. The proposed sensor maneuver requirements satisfy the system observability conditions by using the local linearization approach of the Fisher information matrix. It is also shown that this requirement can be mitigated for special cases in which the depth difference between the sensor and the target is given. Using the simulation, it is shown that sensors following the proposed scheme are able to obtain meaningful information that can be used to estimate 3D target states.

An improved adaptive hybrid firefly differential evolution algorithm for passive target localization

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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.

An Airborne Target Localization Algorithm Based on UKF and Optimal Maneuver Strategy

The airborne bearing-only passive target localization performance is relevant with the specific filter and maneuver model. This paper presents a combination of Unscented Kalman Filtering (UKF) with control inputs and optimal route planning algorithm to improve the performance. Taking the minimum trace of unbiased UKF estimation covariance matrix as criteria, single-step heading traverse method is applied to reach every optimal solution. A single flight path simulation and Monte Carlo analysis validate that the optimal strategy can improve both the convergence and localization accuracy.

SU‐F‐J‐227: Accuracy of Passive Magnetic Ultrasound Needle Guidance in High‐ Fidelity Conventional and Novel Simulated Block Approach Phantoms

Purpose:The objective of this research was to demonstrate accuracy of a real‐time passive ultrasound magnetic needle guidance technology (NGT) and target localization algorithm using conventional anechogenic contact phantoms and a novel fluid‐filled hyperechogenic phantom simulating nerve block technique.Methods:Accuracy (mm to phantom target) was assessed by observing 35 medical practitioners (16 anesthesiologists and 19 residents) performing 560 needle procedures in high‐fidelity ex vivo porcine tissue phantoms. Each practitioner performed 8 tasks with GE Venue 50 magnetic NGT system with PinPoint GT (Bard Access Sys.) and 8 conventional echogenic needle (control) procedures under various conditions (#18/#22 needle gauge, IP/OOP approach, and hyperechogenic simulated block approach targets or anechogenic solid targets). Passes, user feedback, and ergonomics were also assessed.Results:Overlaid visual aids produced in real time (<300 mS delay) by NGT enabled users to consistently perform with better accuracy than control echogenic needle guidance. Accuracy improved by 51.2% at 95% CI [29.8%, 66.7%] (P = 0.0008) among block approach phantoms and 51.4% at 95% CI [34.9%, 64.0%] (P < 0.0001) among placement phantoms. Further, inadvertent contact with targets was reduced by NGT visualizations versus controls in both anesthesiologists (0 vs. 8 contacts; accuracy 2.0±1.7 vs. 4.13±6.7 mm) and novice users (3 vs. 12 contacts; accuracy 2.2±2.3 vs. 2.9±3.7 mm), respectively. Among novice users, NGT visualizations notably reduced quality‐compromising ergonomic observations compared to controls under equivalent conditions.Conclusion:NGT systems employing magnetic field generation and electromagnetic sensors to track needle position and orientation relative to a sonoanatomical target within a magnetic field have the potential to enable greater accuracy for complex needle placements and reduce unintentional needle contact, particularly in novice users. Further, NGT visualizations of needle tip and projected trajectory contribute to positive ergonomics and reducing quality‐compromising behaviors among operators, potentially reducing fatigue and needle progression error.This work was supported by funding provided by the GE Healthcare Ultrasound Business, Milwaukee, WI.

Robust Least Squares Approach to Passive Target Localization Using Ultrasonic Receiver Array

A precise range difference (RD)-based passive target localization algorithm is proposed for mobile robot applications. To effectively solve the real-time issue, the nonlinear relation between the RD information and the target location is removed by introducing the target range as an auxiliary variable to be estimated. Then, the RD-based localization problem is formulated in the setting of linear estimation. The resultant linear measurement model contains the stochastic parametric uncertainty which causes the severe performance degradation of the conventional linear least squares (LS) method when the RD measurement noise is not negligible. To cope with this problem, the recently developed linear robust LS (RoLS) estimation theory is applied for the passive target localization problem. Using the geometric relation among the ultrasonic receivers, a systematic way to determine the design parameters of the RoLS estimator is suggested. It is shown that the proposed method can provide the nearly unbiased target location estimates for the whole location area. The proposed solution is very practical because it is preferable for real-time robot applications owing to its linear recursive structure. Through the computer simulations and actual experiments, it is shown that the proposed algorithm guarantees the superior localization performance and the fast convergence compared to the existing one.

Passive sonar target localization and tracking using sequential bayesian filter in uncertain sea environment

A problem of localizing and tracking an acoustic source is researched when ocean environment including water column sound speed profile, ocean depth and seabed property is uncertain. In a Bayesian framework, the source and environmental parameters are regard as random variables with known prior knowledge, then the prior knowledge of parameters and acoustic model are combined with a likelihood function of data to provide posterior probability density functions (PDF) of both south and environmental parameters, target location parameters are estimated by marginalization integrates over the environmental parameters finally. In other hand, the environmental parameters and target location parameters evolve in time or space, which can be described by state-space model. Information on these parameters evolution and uncertainty at preceding steps can be incorporated to determine future probability of parameters with acoustic data being available at current step. A framework of a sequential Bayesian filter is derived naturally based on the model. A Kalman filter or particle filter could be used to implement the sequential Bayesian filter depend on the linear or nonlinear of the measurement equation or/and the state equation. The sequential Bayesian filter is demonstrated to be able to localize and track a source broadcasting a broadband signal in shallow water using both simulated and real data acquired by a towed array.

Multidimensional Scaling Analysis for Passive Moving Target Localization With TDOA and FDOA Measurements

A new framework for positioning a moving target is introduced by utilizing time differences of arrival (TDOA) and frequency differences of arrival (FDOA) measurements collected using an array of passive sensors. It exploits the multidimensional scaling (MDS) analysis, which has been developed for data analysis in the field such as physics, geography and biology. Particularly, we present an accurate and closed-form solution for the position and velocity of a moving target. Unlike most passive target localization methods focusing on minimizing a loss function with respect to the measurement vector, the proposed method is based on the optimization of a cost function related to the scalar product matrix in the classical MDS framework. It is robust to the large measurement noise. The bias and variance of the proposed estimator is also derived. Simulation results show that the proposed estimator achieves better performance than the spherical-interpolation (SI) method and the two-step weighted least squares (WLS) approach, and it attains the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, for the proposed estimator the threshold effect, which is a result of the nonlinear nature of the localization problem, occurs apparently later as the measurement noise increases for a near-field target.

On exploiting propagation delays for passive target localization using bearings-only measurements

For many cases, target localization accuracy can significantly improve by accounting for the propagation delay between the target source and the sensors. This paper considers when it is advantageous to compensate for the propagation delay for the case of a network of nodes where each node consists of an array of sensors. Each node calculates a direction of arrival (DOA) from the raw data, and these DOAs are combined to localize the target. First, we consider the case when no prior data are given and the localization occurs using DOA measurements from a single snapshot. Such localization methods are necessary to initiate a target track. Finally, we investigate the case where an extended Kalman filter is used to aggregate measurements over multiple snapshots.

On the bias of linear least squares algorithms for passive target localization

The paper derives analytical bias expressions for the least squares bearings-only target localization algorithms. A detailed analysis of the interplay between the target localization geometry and the estimation bias is provided for the cases of elliptical and linear observer trajectories. It is observed that the estimation bias is proportional to the distance between the target and the centre of mass of the observer positions. The effect of the localization geometry parameters on the estimation bias is illustrated analytically and by way of numerical examples. The theoretical findings of the paper are backed up with simulation studies.

Time delay estimates in passive sonar target localization

In passive sonar systems, a target is localized using time delays obtained from signals received at spatially separated points. The variance of the time delay estimates about the true time delay at low signal‐to‐noise ratio (SNR) depends on SNR, bandwidth, center frequency, and spectral slopes of signal and noise. The accuracy of time delay estimation can be enhanced using a pre‐shaping filter. We shall show in this paper that if signal and noise spectral fall off rates are close to each other (within 3 dB/oct), then the preshaping filter does not appreciably enhance the accuracy of time delay estimation. However, if the signal and noise spectral slopes are significantly different (about 6 dB/oct), then the preshaping filter increases the accuracy.

On the Continuity of the Generalized Inverse

On the Continuity of the Generalized Inverse