Traditional Localization Methods Research Articles

Simultaneous Localization of Scatterers and Target User Based on Indoor Prior Information in NLOS Environments

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well.

Source Direction Finding and Direct Localization Exploiting UAV Array With Unknown Gain-Phase Errors

An unmanned aerial vehicle (UAV) array is composed of multiple UAVs carrying array elements, which can sense signals synchronously through a global positioning system (GPS) trigger. However, gain-phase errors caused by synchronization errors and the inconsistent complex gains of receiving array channels result in array manifold perturbation, which makes the performance of traditional localization methods degrade or even fail. In this article, two efficient algorithms for source direction finding and direct localization using a UAV array are, respectively, presented. First, the array manifold changes with the movement of UAVs, which contributes to multiposition fusion, thus avoiding the infinite solutions of underdetermined equations. Then, the quadratic optimization problem can be constructed by using the orthogonal relation between noise subspaces and contaminated steering vectors obtained from multiple observations. Thereafter, we can construct the cost function and obtain the spectral function, from which the source directions and positions can be all estimated by grid search. Meanwhile, the gain-phase error values can be solved successively. Moreover, in order to avoid the influence of heteroscedasticity of different observation positions in direct localization, we carry out a blind weighting operation. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Robust Indoor Localization Using Histogram of Oriented Depth Model Feature Map for Intelligent Service Robotics

Indoor service robots accumulate errors when its own reference map differs from the true environment such as when furniture has been rearranged in an area. This results in poor localization accuracy when pose estimation relies on outdated maps. Traditional methods address this issue by rebuilding the reference map from scratch. In this article, we propose a histogram of oriented depth model (HODM) and its extraction approach using laser rangefinders and RGBD cameras. HODM aims to provide a light and robust localization module so that mobile robots work in environments required by rearrangement from time to time. The key concept of HODM is based on using histogram-based model matching for estimating indoor primary structures and floor layouts. HODM localization will use an HODM map as a reference in scan matching, and the experimental results show that the localization error is lower than traditional non-HODM-based localization methods. In the same indoor location, the mapping process is only required once. The HODM approach presented in this article shows two major contributions. First, the localization error is lower even with an outdated reference map when compared with traditional localization methods, and, second, the computational time of HODM is fast. We validate this proposed method through numerical simulations and actual experiments in our laboratory using our experimental mobile robot developed in our NTU-iCeiA robotics laboratory.

Wi-Fi indoor 3D localization algorithm based on multi-classifier fusion

Indoor localization technology based on Received Signal Strength (RSS) fingerprint is widely used in life and industry. Compared with the traditional localization methods, the localization technology integrating multiple machine learning methods has better localization accuracy. However, in multi-floor localization, the existing fusion localization technology ignores the interaction between different floors in the localization process, resulting in low indoor localization accuracy. This paper proposes a Wi-Fi indoor 3D localization method based on multi-classifier fusion named FLMCF. Firstly, floor classification training is carried out to reduce the location deviation in the vertical direction. Secondly, for each floor, multiple classifiers are used for model training and training the optimal weight set by minimizing the average localization error. In this case, the advantages of each classifier can be fully integrated to improve the localization accuracy. Finally, the Reliability Fusion Weight Selection (RFWS) algorithm determines the weight and calculates the final location estimation. The experimental results indicate that FLMCF is nearly 14.1% better than DIFMIC in the 90th percentile of CDF.

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

There is a growing demand for localization of illegal signal sources, aiming to guarantee the security of urban electromagnetic environment. The performance of traditional localization methods is limited due to the non-line-of-sight (NLOS) propagation and sparse layouts of sensors. In this paper, a deep learning-based localization method is proposed to overcome these issues in urban scenarios. Firstly, a model of electromagnetic wave propagation considered with geographic information is proposed to prepare reliable datasets for intelligent cognition of urban electromagnetic environment. Then, this paper improves an hourglass neural network which consists of downsampling and upsampling layers to learn the propagation features from sensing data. The core modules of VGG and ResNet are, respectively, utilized as feature extractors in downsampling. Moreover, this paper proposes a weighted loss function to expand the attention on position features, in order to improve the performance of localization with sparse layouts of sensors. Representative numerical results are discussed to assess the proposed method. ResNet-based extractor performs more efficiently than VGG-based extractor, and the proposed weighted loss function increases the localization accuracy by more than 50%. Additionally, the established geographic model supports qualitative and quantitative evaluation of the robustness with varied degree of NLOS propagation. Compared with other deep learning-based algorithms, the proposed method presents the more robust and superior performance under severe NLOS propagation and sparse sensing conditions.

Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested.

LLNet: A Fusion Classification Network for Land Localization in Real-World Scenarios

Lane localization is one of the core tasks in an autonomous driving system. It receives the visual information collected by the camera and the lane marks and road edges information outputted from the perception module and gives lane index for the subsequent decision module. Traditional rule-based lane localization methods using navigation maps can only be effective in regular road scenarios and have poor generalization ability. High-Definition Map (HD map) was originally thought to solve the lane localization problem, but due to the regulations of the relevant departments, HD map is currently not allowed to be used in autonomous driving systems. In addition, many multi-sensor fusion methods have been proposed to solve the lane localization problem. However, due to the extremely strict safety requirements of autonomous driving systems, these well-designed solutions make it difficult to meet the requirements in terms of robustness, efficiency, and stability. To solve these problems, we innovatively define the lane localization task as a classification problem. First, to better utilize the perceptual information outputted from the perceptual model, we design an image-generating method that projects the perceptual information onto a new image and ensures that our model can learn the perceptual features wisely. Second, to better fuse the perceptual and visual information, we propose a fusion structure deep learning neural network named LLNet to address the lane localization problem in an end-to-end manner. Finally, to ensure the generalization ability, robustness, and stability of LLNet, we conduct extensive comparison experiments on a large-scale real-world dataset, with a total mileage of over 400 km. The experiments show that our approach remarkably outperforms the deep learning classification baselines. In the discussion part of this paper, we give a comprehensive and detailed elaboration for the effectiveness of various designs in our LLNet. To our knowledge, LLNet is the first lane localization method based entirely on deep learning. LLNet is added to the self-driving suite for a kind of mass production vehicle that will be available in the summer of 2022, with an expected sales volume more than 300,000.

Vehicle Localization Using Doppler Shift and Time of Arrival Measurements in a Tunnel Environment.

Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliable, continuous, and accurate positioning in all scenarios, such as tunnels. Therefore, in this work, we present an algorithm that exploits the existing Vehicle-to-Infrastructure (V2I) communication channel that operates within the LTE-V frequency band to acquire in-tunnel vehicle location information. We propose a novel solution for vehicle localization based on Doppler shift and Time of Arrival measurements. Measurements performed in the Beveren tunnel in Antwerp, Belgium, are used to obtain results. A comparison between estimated positions using Extended Kalman Filter (EKF) on Doppler shift measurements and individual Kalman Filter (KF) on Doppler shift and Time of Arrival measurements is carried out to analyze the filtering methods performance. Findings show that the EKF performs better than KF, reducing the average estimation error by 10 m, while the algorithm accuracy depends on the relevant RF channel propagation conditions and other in-tunnel-related environment knowledge included in the estimation. The proposed solution can be used for monitoring the position and speed of vehicles driving in tunnel environments.

Dual-station direction finding location based on disturbance detection

The application of direction finding location technology in electronic warfare is becoming more and more extensive. Aiming at the problem that the traditional location method will greatly affect the location accuracy when the measurement information of each observation station is disturbed, a dual-station direction finding location algorithm based on disturbance detection is proposed. The algorithm uses the least squares method to obtain the target’s position information at each moment, then designs an improved Kalman filter algorithm with disturbance detection to improve the location accuracy. The simulation results show that the algorithm can still accurately track the target when the measurement information is disturbed, which has practical application value.

Negative Pressure Wave-Based Method for Abnormal Signal Location in Energy Transportation System

Abnormal signal location is an indispensable part to handle unexpected events such as pipeline leak in energy transportation system. However, unclear signal characteristics would make traditional location methods such as wavelet analysis failure in location process. To address it, a data-driven method based on negative pressure wave (NPW) is proposed in this article. First, a signal enhancement model based on Gaussian process regression (GPR) is proposed to highlight the changed signal characteristics. Then, a multi-objective evolutionary computation method is proposed to obtain the location result, which overcomes the shortcoming of unclear signal characteristics. Moreover, the signal transmission attenuation rules are added into the location process, which ensures the rationality and the reliability of the location result. Without introducing extra signal numbers and types, the proposed method could achieve higher accuracy and more robustness than traditional location methods such as wavelet analysis, which has the less 1.2% location deviation of the pipeline. Finally, different experiment results show that the proposed method is a realistic way to provide the position of abnormal signal source.

3-D Source Location by Neural Network for FBG Acoustic Emission Sensors

Fiber Bragg grating acoustic emission sensors have been used in many applications. In this paper, based on four fiber Bragg grating acoustic emission sensors, an acoustic emission location experiment is carried out on the surface of a cylindrical polymer-bonded explosive specimen. Due to the difference in the strain sensitivity of fiber Bragg grating in different directions, the traditional time-difference location method is not fit for fiber Bragg grating acoustic emission sensors. A 4-layer dense neural network for fiber Bragg grating acoustic emission sensors, together with a Bayesian regularization method is used to calculate the coordinates of the acoustic emission wave source. The experimental results show that the neural network location method is suitable for fiber Bragg grating acoustic emission sensors.

Positioning Combination Method of USBL Using Four-Element Stereo Array

In the present article, an ultra-short baseline (USBL) combined location method based on three four-element stereo arrays is proposed. In order to solve the problem of the poor positioning effect of acoustic positioning under a high incident angle of signal, two kinds of four-element stereo arrays are designed, and the localization approach of the new array is given. At the same time, for the regular triangular pyramid array, a virtual array element is proposed to construct a planar cross array to improve the poor positioning effect of the regular triangular pyramid array at a low incident angle. This paper analyzes the positioning performance of three arrays. Combined with the traditional cross-planar array localization method, a set of positioning strategies to switch the two localization methods under different incident angles were designed. The switching thresholds of the two methods were analyzed by simulation. Simulation results show that the new arrays can locate stably at different incident angles and improve the overall positioning performance of the array.

Comparison of Four-hook Needle and Memory Alloy Coil in Localization of Pulmonary Nodules

背景与目的随着肺结节微创手术的广泛开展，术前定位变得愈发关键。传统定位方式存在一定的缺陷，定位方式的改进很有必要。本研究旨在对比分析两种新型定位方式即四钩定位针和记忆合金弹簧圈在肺结节定位中应用的安全性和有效性。方法回顾性分析152例患者的资料，76例为四钩定位针组，76例为记忆合金弹簧圈组，术前均行肺结节定位，后行电视胸腔镜辅助肺结节楔形切除术，统计平均定位时间、定位并发症、结节切除时间等。结果两组患者均成功切除目标肺结节，四钩定位针组76例患者术中均找到定位装置，肺结节全部成功切除，1例因严重胸腔粘连而中转开胸行肺结节楔形切除术。记忆合金弹簧圈组76例患者肺结节均成功切除，1例因术中切除标本后未找到病灶而行妥协性扩大切除术。两组气胸、肺内出血发生率、定位成功率、结节楔形切除时间无统计学差异。四钩定位针组平均定位时间为（13.66±3.11）min，低于记忆合金弹簧圈组的（15.51±3.65）min，二者有统计学差异（P=0.001）。记忆合金弹簧圈组当结节至胸膜距离≥1.5 cm和 < 1.5 cm时，平均定位时间分别为（17.20±4.46）min、（14.91±3.15）min，二者有统计学差异（P=0.044）。结论四钩定位针和记忆合金弹簧圈两种新型肺结节定位方式均有较好的安全性及有效性，四钩定位针定位操作用时更短。在使用记忆合金弹簧圈定位时，对于到胸膜距离 < 1.5 cm的肺结节效果可能更佳。

Accelerating Bayesian microseismic event location with deep learning

Abstract. We present a series of new open-source deep-learning algorithms to accelerate Bayesian full-waveform point source inversion of microseismic events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep-learning models to learn the mapping between source location and seismic traces for a given 3D heterogeneous velocity model and a fixed isotropic moment tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process. We compare our results with a previous study that used emulators based on Gaussian processes to invert microseismic events. For fairness of comparison, we train our emulators on the same microseismic traces and using the same geophysical setting. We show that all of our models provide more accurate predictions, ∼ 100 times faster predictions than the method based on Gaussian processes, and a 𝒪(105) speed-up factor over a pseudo-spectral method for waveform generation. For example, a 2 s long synthetic trace can be generated in ∼ 10 ms on a common laptop processor, instead of ∼ 1 h using a pseudo-spectral method on a high-profile graphics processing unit card. We also show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The speed, accuracy, and scalability of our open-source deep-learning models pave the way for extensions of these emulators to generic source mechanisms and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.

Data driven interference source localization based on train real-time onboard interference monitoring

Interference is an important factor affecting railway mobile communications, which leads to deterioration of communication quality. In serious cases, the link connection interruption will lead to degraded operation of train control system, then affect the operation efficiency of the railway system. In order to reduce the impact of interference on railway communications, the interference source should be localized and eliminated timely. However, the traditional interference localization methods require auxiliary equipment to be arranged along the track. In this paper, a data driven interference source localization method based on train real-time onboard interference monitoring is proposed, which can reduce the interference localization cost. Based on the collected Received Signal Strength (RSS) of interference signal, the particle filter principle is used to locate the target interference source combined with the data driven path-loss models. Besides, the modified whale optimization algorithm (MWOA) is introduced to further improve the positioning accuracy. The simulation and experiment results show that the proposed data driven interference source localization method outperforms the traditional localization method, and the positioning accuracy is improved from tens of meters degree to meters degree, which can meet the requirements of engineering practice to find the interference source.

Precision location approach in deep-space optical communications.

A traditional beacon location method is difficult to apply to a deep space optical communications link due to the high laser power required for long distances. The use of natural celestial bodies as beacon images can solve this problem. The correct location of the beacon is critical to establish and maintain an optical communications link. Therefore, in this paper we propose an approach to determine the location of a natural celestial beacon. To identify a beacon in an uncertain region, the phase correlation between the detected and reference images is applied. The influence of an image translation is eliminated through a Fourier transform, and the scaling and rotation are converted into the translation and solved using a log-polar transformation and phase correlation, respectively. The availability of a new approach is verified by the experiment. A field-programmable gate array embedded processing system is designed to realize the proposed algorithm. When the image noise is considered, the success probability of the algorithm can reach more than 96%. We believe this work is beneficial for deep space optical communications system design.

A Multiple-Fault Localization Method for Embedded Software with Applications in Engineering

Embedded software is increasingly being used with high reliability. However, the fault localization of embedded software is still largely dependent on the experience of engineers. Besides, faults in embedded software programs are not independent individuals; they are related to each other and affect each other, which may lead to more complex interaction behavior. These uncertainties render the traditional methods for single-fault localization with limited practical value. This paper has proposed a multiple-fault localization method to be applied to the embedded software, with emphasis on the cache-based program spectra-acquiring method and the hybrid clustering-based fault partition method. Through case studies on 108 groups of the subject program, it has been proved that the hybrid clustering-based fault partition method has significantly improved the effectiveness of multiple-fault localization in comparison with the traditional fault localization methods. Experiments on three embedded software programs in engineering have revealed that the cache-based program spectra-acquiring method saves nearly half of the running-time cost compared with the traditional spectrum-acquiring method based on real-time transmission. Therefore, the multiple-fault localization method proposed in this paper can be applied in embedded software debugging and testing in engineering.

Indoor Trajectory Prediction Algorithm Based on Communication Analysis of Built-In Sensors in Mobile Terminals

In recent years, application services based on indoor locations have become increasingly popular, attracting extensive research attention. Among these application services, indoor location technology based on Wi-Fi signal fingerprints has developed rapidly. However, wireless signal transmission is easily affected by the environment, which will lead to deviations in the fingerprint location of the Wi-Fi signal. To improve the location accuracy and reduce the adverse effects of environmental factors, a method of fingerprint location for Wi-Fi signals assisted by smart phone built-in sensors is proposed, that is, to use the built-in sensors such as the accelerometer and gyroscope to collect data, calculate the user’s track information and track credibility, and combine the track information and signal fingerprint information to establish a comprehensive probability model, perform position matching, and determine the nearest reference point. The experimental results show that compared with the traditional fingerprint location method of Wi-Fi signals, the user trajectory information estimated from the sensor data can be used to effectively overcome the impact of environmental changes and improve the average location accuracy.

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location‐sensitive hash (LSH) based on P stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension k of the hash function family is 14, and the interval size W is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension k of the hash function family is 6, and the interval size W is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.

Application of fracture body surface localization film combined with CT volume rendering in the minimally invasive rib fractures internal fixation.

To investigate the application value of the technique of fracture body surface localization film combined with CT volume rendering in the selection of minimally invasive incision for internal fixation of rib fractures. Clinical data of 55 cases of patients who underwent internal fixation for rib fracture in our hospital from June 2019 to April 2020 were selected. The differences in the accuracy of preset incision, incision length, operation time, intraoperative blood loss, postoperative wound drainage, and postoperative pain score between the group with fracture body surface localization film combined with CT volume rendering (n=32) and the group with traditional localization method (n=23). Compared with traditional localization method, fracture body surface localization film combined with CT volume rendering could improve the accuracy of surgical incision, reduce the operation time, incision length, intraoperative blood loss, postoperative wound drainage, and postoperative pain score (p<0.05). The application of fracture body surface localization film combined with CT volume rendering has obvious effects on the accurate selection of incision of rib fracture internal fixation, and it is an effective method that is worthy of promotion.