Efficient Localization Algorithm Research Articles

Improved Global Localization and Resampling Techniques for Monte Carlo Localization Algorithm

Global indoor localization algorithms enable the robot to estimate its pose in pre-mapped environments using sensor measurements when its initial pose is unknown. The conventional Adaptive Monte Carlo Localization (AMCL) is a highly efficient localization algorithm that can successfully cope with global uncertainty. Since the global localization problem is paramount in mobile robots, we propose a novel approach that can significantly reduce the amount of time it takes for the algorithm to converge to true pose. Given the map and initial scan data, the proposed algorithm detects regions with high likelihood based on the observation model. As a result, the suggested sample distribution will expedite the process of localization. In this study, we also present an effective resampling strategy to deal with the kidnapped robot problem that enables the robot to recover quickly when the sample weights drop-down due to unmapped dynamic obstacles within the sensor’s field of view. The proposed approach distributes the random samples within a circular region centered around the robot’s pose by taking into account the prior knowledge about the most recent successful pose estimation. Since the samples are distributed over the region with high probabilities, it will take less time for the samples to converge to the actual pose. The percentage of improvement for the small sample set (500 samples) exceeded 90% over the large maps and played a big role in reducing computational resources. In general, the results demonstrate the localization efficacy of the proposed scheme, even with small sample sets. Consequently, the proposed scheme significantly increases the real-time performance of the algorithm by 85.12% on average in terms of decreasing the computational cost.

Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure.

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

Design of an Energy Efficient Cluster based Localization Algorithm (EECBLA) for Underwater Wireless Sensor Networks

In underwater Wireless Sensor Networks (UWSN) the node mobility is higher as the nodes drift with the water so identifying the exact location of the nodes is a challenging task. Terrestrial WSN use Global Positioning System (GPS) to locate the nodes, but the same cannot be applied for UWSN as they do not propagate under water. Underwater uses acoustic channel for communication which suffers from low bandwidth, high propagation delay, high bit error rate, high node mobility and variable sound speed which makes localization a challenging task. Hence there is an evolving requirement for energy efficient localization algorithm. To overcome these problems we propose an Energy Efficient Cluster Based Localization Algorithm (EECBLA) to minimize the energy utilization and identify an accurate location of the sensor nodes. EECBLA is a cluster based localization protocol where the GPS enabled high power anchor nodes are utilized to estimate the location of the un localized sensor nodes. To improve the localization accuracy it is necessary to improve the network connectivity. Network connectivity can be improved by increasing the lifetime of the sensor nodes. EECBLA proposes an energy efficient localization scheme where the node operates in active and sleep mode to efficiently utilize the available energy. In this research work we take a sample example and demonstrate how the location is estimated through TOA and Euclidean distance. Accuracy of TOA is measured as 8.36, 4.76, 13.21, 14.85, 16.26 and 3.60. The average estimated localization error of EECBLA is around 4m to 6m. EECBLA localization error is compared with other localization algorithms like 3DUL whose average localization error is 5m to 10m, SDMA whose average localization error is around 24% and localization error of Hybrid RSS ranges from 4.69m to 15.85m. When compared with these methodologies there is a decrease in localization error by 3%, and the increase in accuracy by 5% in EECBLA protocol.

Implementation of an Efficient Artificial Bee Colony Algorithm for Node Localization in Unmanned Aerial Vehicle Assisted Wireless Sensor Networks

Node localization is a fundamental task in wireless sensor networks as it is useful for several localization based protocols and applications. Node localization using Global Poisoning System (GPS) employed fixed terrestrial anchor nodes suffers from high deployment cost and poor localization accuracy in GPS denied locations. These issues can be easily handled by deploying movable Unmanned Aerial Vehicles (UAVs). A movable UAV equipped with a single GPS module virtually increases number of anchor nodes and localizes a node at different locations. Hence, UAVs are cost effective and also provides high localization accuracy. As the flying altitude of UAV greatly influence localization accuracy, the present work firstly optimizes the flying height and then the node localization is defined as least square optimization problem using this optimal height. Since the classical received signal strength indicator based multilateration results high localization error, the least square localization using optimization techniques is found to be better alternative. The recently proposed Artificial Bee Colony (ABC) algorithm is a powerful optimization technique that can be applied for this optimization problem to achieve high accuracy. Thus, this paper aims at designing an ABC localization technique using UAV anchors to achieve minimum localization error. Further, we provide detailed simulation analysis to support the proposed ABC localization scheme.

A locally refined adaptive isogeometric analysis for steady-state heat conduction problems

This paper presents an isogeometric analysis with adaptivity using locally refined B-splines (LR B-splines) for steady-state heat conduction simulations in solids. Within this framework, the LR B-splines, which have an efficient and simple local refinement algorithm, are used to represent the geometry, and are also employed for spatial discretization, thus providing a seamless interaction between the CAD models and the numerical analysis. A Zienkiewicz-Zhu a posteriori error estimator in terms of the temperature gradient recovery is used to identify the regions for local mesh refinement. The accuracy and convergence properties of the proposed framework are demonstrated through several two-dimensional isotropic examples. Numerical results indicate good performance of the present method as the adaptive refinement technique yields more accurate solution compared to the uniform global refinement with an improved convergence rate.

Reduction of secondary lobes in joint angle and delay estimation in angle of arrival localization to detect MAC address spoofing in wireless networks

in this paper, we solve the problem of secondary lobes that are due to noise that comes from constructive and destructive multipath interference that are resulted in received signal strength (RSS) variation over time. This is to develop a very efficient localization algorithm that uses a unique fingerprint angle of arrivals (AOAs), in a specified range, with associated time delays (TDs), in the surrounded sparsity design promoting multipath parameter (i.e:RSS). We solve this problem to detect physical identity spoofing of nodes in radio wireless networks, and localize adversaries and jammers of wireless networks. All radio waves are vulnerable to many types of attacks due to the ability to capture them and sniff or eavesdropping on them in the open space. Physical identity spoofing is used to launch many types of attacks against wireless networks like Denial of Service (DOS), Man-In-The-Middle and Session Hijacking and eavesdropping. Eavesdropping is a human-based social engineering attack. Active adversaries are able to jam and eavesdrop simultaneously, while passive adversaries can only eavesdrop on passed signals. In TCP/IP protocol for example, Media Access Card (MAC) Address is transferred in 802.11 frames. Detection process was carried out by analyzing electromagnetic radio waves that are used to transfer data, in the form of radio wave signals that are formed by the modulation process which mixes the electromagnetic wave, with another one of different frequency or amplitude to produce the signal with a specified pattern of frequency and amplitude. We depended on the angle of arrival of vectors and time delay across scattered areas in the surrounded space to solve the problem of co-location in detection and localization of jammers. We used Maximum Likelihood (ML) angle of arrival determination because ML approaches, known to their higher accuracy and enhanced resolution capabilities. And we assessed their computational complexity that was considered as the major drawback for designers to their implementation in practice. Our solution was tested on a jammer that changed the signal strength of received signal at the receiver at an angle of arrival 30 degree. And we used scatterers density to determine the angle of arrival of the sender. The simulation has observed that the power of the received signal has changed from the range of angles 20 to 40 degrees. We used scatterers because they describe the density of the signal power, and also enhance the signal to noise ratio, that resulted from the multipath fading of the signal strength. And also overcoming the problem of secondary lobes that are due to signal propagation, while determining the angle of arrival of a signal sender. So, we developed a new passive technique to detect MAC address spoofing based on angle of arrival localization. And assessed the computation complexity of the localization technique through depending on a range angle to estimate the angle of arrival of the adversary within it. And we reduced number of secondary lobes, and their peaks, in the importance function, while determining the angle of arrival, and so increasing the accuracy of angle of arrival measurement. We compared our work to other techniques and find that our technique is better than these techniques.

Using Feature Selection for Local Causal Structure Learning

Local causal structure learning aims to discover and distinguish the direct causes and direct effects of a target variable. However, the state-of-the-art local causal structure learning algorithms need to perform an exhaustive subset search within the currently selected variables for PC (i.e., parents and children) discovery. In this article, we propose an efficient local causal structure learning algorithm around a target variable, called LCS-FS (Local Causal Structure learning by Feature Selection). First, to construct the local causal skeleton of the target, we employ feature selection for finding PC without searching for conditioning sets to speed up PC discovery, leading to improve the skeleton construction efficiency. Second, to orient edges in this local causal skeleton, we propose an efficient method to find separating sets from the subsets of PC for identifying V-structures. With the integration of feature selection and the new way of finding separating sets, LCS-FS recursively finds the spouses of Markov blankets in local causal skeleton for edge orientations, until the direct causes and direct effects of the target are distinguished. The experiments on five benchmark Bayesian networks with the number of variables from 35 to 801 validate that our algorithm achieves higher efficiency and better accuracy than the state-of-the-art local causal structure learning algorithms.

A multiperiod workforce scheduling and routing problem with dependent tasks

In this paper, we study a new Workforce Scheduling and Routing Problem, denoted Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks. In this problem, customers request services from a company. Each service is composed of dependent tasks, which are executed by teams of varying skills along one or more days. Tasks belonging to a service may be executed by different teams, and customers may be visited more than once a day, as long as precedences are not violated. The objective is to schedule and route teams so that the makespan is minimized, i.e., all services are completed in the minimum number of days. In order to solve this problem, we propose a Mixed-Integer Programming model, a constructive algorithm and heuristic algorithms based on the Ant Colony Optimization (ACO) metaheuristic. The presence of precedence constraints makes it difficult to develop efficient local search algorithms. This motivates the choice of the ACO metaheuristic, which is effective in guiding the construction process towards good solutions. Computational results show that the model is capable of consistently solving problems with up to about 20 customers and 60 tasks. In most cases, the best performing ACO algorithm was able to match the best solution provided by the model in a fraction of its computational time.

An efficient local search algorithm for solving maximum edge weight clique problem in large graphs

Maximum vertex weight clique problem (MVWCP) and maximum edge weight clique problem (MEWCP) are two significant generalizations of maximum clique problem (MCP), and can be widely used in many real-world applications including molecular biology, broadband network design and pattern recognition. Recently, breakthroughs have been made for solving MVWCP in large graphs, resulting in several state-of-the-art algorithms, such as WLMC, FastWClq and LSCC + BMS. However, less attention has been paid to solving MEWCP in large graphs. In this paper, we present an efficient Stochastic Local Search (SLS) algorithm for MEWCP by combining clique construction, local search and graph reduction, resulting in a new algorithm named ReConSLS. We also propose a new upper bound function for edge weighted graphs which is essential for graph reduction. Extensive experiments on a wide range of large graphs demonstrate that ReConSLS surpasses state-of-the-art SLS competitors on the majority of testing graphs.

SCCWalk: An efficient local search algorithm and its improvements for maximum weight clique problem

The maximum weight clique problem (MWCP) is an important generalization of the maximum clique problem with wide applications. In this study, we develop two efficient local search algorithms for MWCP, namely SCCWalk and SCCWalk4L, where SCCWalk4L is improved from SCCWalk for large graphs. There are two main ideas in SCCWalk, including strong configuration checking (SCC) and walk perturbation. SCC is a new variant of a powerful strategy called configuration checking for local search. The walk perturbation procedure is used to lead the algorithm to leave the current area and come into a new area of feasible solution space. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called best from multiple selection to select the swapping vertex pair quickly and effectively, resulting in the SCCWalk4L algorithm. In addition, SCCWalk4L uses two recent reduction rules to decrease the scale of massive graphs.We carry out experiments to evaluate our algorithms on several popular benchmarks, which are divided into two groups, including classical benchmarks of small graphs namely DIMACS, BHOSLIB, winner determination problem, and graphs derived from clustering aggregation, as well as massive graphs, including a suite of massive real-world graphs and large-scale FRB graphs. Experiments show that, compared to state-of-the-art heuristic algorithms and exact algorithm, the proposed algorithms perform better on classical benchmarks, and obtain the best solutions for most massive graphs.

Efficient Quantizer Design Algorithm for Sequence-Based Localization (SBL) Systems

Efficient Quantizer Design Algorithm for Sequence-Based Localization (SBL) Systems

A Fast Efficient Local Search-Based Algorithm for Multi-Objective Supply Chain Configuration Problem

Supply chain configuration (SCC) plays an important role in supply chain management. This paper focuses on a multi-objective SCC (MOSCC) problem for minimizing both the cost of goods sold and the lead time simultaneously. Some existing population-based methods use the evolution of a population to obtain the optimal Pareto set, but they are time-consuming. In this paper, an $\boldsymbol {E}$ fficient $\boldsymbol {L}$ ocal ${S}$ earch-based algorithm with rank (ELSrank) is designed to solve the MOSCC problem. Firstly, instead of use of population, two solutions ( $\boldsymbol {x} _{{\boldsymbol{A}}}$ and ${x}_{{\boldsymbol{B}}}$ ) are generated by the greedy strategy, which have the minimal cost and the minimal time, respectively. They approximately locate in two sides of the Pareto front ( PF ). Secondly, with the consideration of the problem characteristics, a local search (LS) is proposed to find competitive solutions among the common neighborhood of two given solutions. If $\boldsymbol {x} _{{\boldsymbol{A}}}$ and $\boldsymbol {x} _{{\boldsymbol{B}}}$ are chosen to execute the proposed LS, solutions along the link path (the approximate PF ) of $\boldsymbol {x} _{{\boldsymbol{A}}}$ and $\boldsymbol {x} _{{\boldsymbol{B}}}$ can be found. This way, the solutions along the whole PF can be found. The comparative experiments are conducted on six instances from the real-life MOSCC problems, and the results show that ELSrank performs better than other start-of-the-art algorithms, especially on the large scale problem instances.

Beamspace Direct Localization for Large-Scale Antenna Array Systems

Direct localization systems can outperform two-step localization ones by jointly processing all the measurements observed at base stations. However, such systems encounter large communication and computational load when the signal dimension is high, e.g., with large-scale antenna arrays. In this paper, we establish a general framework for beamspace direct localization, which consists of beamspace design and position determination. By deriving the performance bound of beamspace direct localization, we first prove that the high-dimensional array signal can be represented in a low-dimensional beamspace without information loss, and then cast the beamspace design as an optimization problem under dimension constraints. In the presence of parameter uncertainty, we propose a robust beamspace scheme to guarantee the performance in the worst case. Using the low-dimensional signals, we develop an efficient direct localization algorithm with the computational complexity and communication overhead orders of magnitude smaller than the conventional methods. Simulation results show that the proposed approach attains near-optimal localization performance.

The Horseshoe-Like Regularization for Feature Subset Selection

Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The l0 penalty is ideal for this task, the caveat being it requires the NP-hard combinatorial evaluation of all models. A recent area of considerable interest is to develop efficient algorithms to fit models with a non-convex lγ penalty for γ ∈ (0,1), which results in sparser models than the convex l1 or lasso penalty, but is harder to fit. We propose an alternative, termed the horseshoe regularization penalty for feature subset selection, and demonstrate its theoretical and computational advantages. The distinguishing feature from existing non-convex optimization approaches is a full probabilistic representation of the penalty as the negative of the logarithm of a suitable prior, which in turn enables efficient expectation-maximization and local linear approximation algorithms for optimization and MCMC for uncertainty quantification. In synthetic and real data, the resulting algorithms provide better statistical performance, and the computation requires a fraction of time of state-of-the-art non-convex solvers.

Local feature selection based on artificial immune system for classification

Conventional feature selection algorithms select a global feature subset for the entire sample space. In contrast, in this paper we propose an efficient filter local feature selection algorithm based on artificial immune system, which assigns a locally relevant feature subset for each neighboring region of the sample space. This algorithm introduces a clonal selection algorithm to explore the search space for the optimal feature subsets, and adopts local clustering idea as an evaluation criterion that maximizes the inter-class distance and minimizes the intra-class distance in the small region of each sample. Experimental results on a wide variety of synthetic and UCI datasets demonstrates that our proposed method achieves better performance than both state-of-the-art global feature selection algorithms and local feature selection algorithms. In addition, a main parameter analysis of the proposed method is carried out.

Efficient Local Map Search Algorithms for the Placement of Flying Relays

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadows and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.

Computationally efficient TDOA, FDOA and differential Doppler rate estimation algorithm for passive emitter localization

This paper addresses the joint time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate estimation problem in passive emitter localization, considering the range and Doppler frequency migrations during the observation time. A computationally efficient estimation algorithm via second-order keystone transform and Lv's distribution is proposed. This method can effectively remove two types of migrations and accomplish the parameter estimation. Cross-terms suppression ability of the proposed method is also analyzed and its characteristic indicates the applicability in the scenario of multi-targets. The whole estimation step can be fast implemented by complex multiplications, fast Fourier transform (FFT) and inverse FFT operations without any searching procedure. Extensive numerical experiments validate that with a much lower computational cost, the proposed method could achieve comparable estimation performance with the maximum likelihood estimator and outperforms the other representative algorithms.

RFID Reader Localization Using Hard Decisions With Error Concealment

This paper presents an efficient reader localization algorithm in radio frequency identification (RFID) networks. In the proposed algorithm, it is assumed that no channel state information is available at the reader and the backscatters of all RFID tags are converted to hard decisions using an energy detector. The accuracy of the estimated location is improved using an error concealment algorithm that utilizes the fact that adjacent tags are expected to produce similar decisions. The final location estimates are obtained using particle swarm optimization of a maximum likelihood estimator that takes into account imperfections in the sensing and transmission processes. The performance of the proposed algorithm is evaluated in terms of the root mean square error using Monte Carlo simulation, and compared to other well-established localization algorithms. Moreover, Cramer–Rao lower bound is derived to assess the efficiency of the new estimator. The obtained results show that the proposed algorithm estimation accuracy is up to 26.5% more than the other benchmark estimators.

Least-square Matching for Mobile Robot SLAM Based on Line-segment Model

This study proposes an efficient simultaneous localization and mapping (SLAM) algorithm for a mobile robot. The proposed algorithm consists of line-segment feature extraction from a set of points measured by a LIDAR, association and matching between the line-segments and a map database for position estimation, and the registration of the line-segments into the map database for the incremental construction of the map database. The line-segment features help reduce the amount of data required for map representation. The matching algorithm for position estimation is efficient in computation owing to the use of a number of inliers as the weights in the least-squares method. Experiments are conducted to demonstrate the performance of the proposed SLAM algorithm, and the results show that the proposed algorithm is effective in the map representation and the localization of a mobile robot.

Stochastic numerical approach for solving second order nonlinear singular functional differential equation

A new computational intelligence numerical scheme is presented for the solution of second order nonlinear singular functional differential equations (FDEs) using artificial neural networks (ANNs), global operator genetic algorithms (GAs), efficient local operator interior-point algorithm (IPA), and the hybrid combination of GA-IPA. An unsupervised error function is assembled for the DDE optimized by ANNs using the hybrid combination of GA-IPA. Three kinds of the second order nonlinear singular DDEs have been solved numerically and compared their results with the exact solutions to authenticate the performance and exactness of the present designed scheme. Moreover, statistical analysis based on Mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is also performed to validate the convergence and accuracy of the present scheme.