Localization In Complex Environment Research Articles

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network (WLAN) is widely used for indoor localization. However, the construction of the fingerprint database is quite time-consuming. Especially when the position of the access point (AP) or wall changes, updating the fingerprint database in real-time is difficult. An appropriate indoor localization approach, which has a low implementation cost, excellent real-time performance, and high localization accuracy and fully considers complex indoor environment factors, is preferred in location-based services (LBSs) applications. In this paper, we proposed a fine-grained grid computing (FGGC) model to achieve decimeter-level localization accuracy. Reference points (RPs) are generated in the grid by the FGGC model. Then, the received signal strength (RSS) values at each RP are calculated with the attenuation factors, such as the frequency band, three-dimensional propagation distance, and walls in complex environments. As a result, the fingerprint database can be established automatically without manual measurement, and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods. The proposed indoor localization approach, which estimates the position step by step from the approximate grid location to the fine-grained location, can achieve higher real-time performance and localization accuracy simultaneously. The mean error of the proposed model is 0.36 ​m, far lower than that of previous approaches. Thus, the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization. It also shows high-accuracy performance with a fast running speed even under a large-size grid. The results indicate that the proposed method can also be suitable for precise marketing, indoor navigation, and emergency rescue.

The Combination of Spectrum Subtraction and Cross-power Spectrum Phase Method for Time Delay Estimation

In order to solve the problem of large error of delay estimation in low SNR environment, a new delay estimation method based on cross power spectral frequency domain weighting and spectrum subtraction is proposed. Through theoretical analysis and MATLAB simulation, among the four common weighting functions, it is proved that the cross-power spectral phase weighting method has a good sharpening effect on the peak value of the cross-correlation function, and it is verified that the improved spectral subtraction method generally has a good noise reduction effect under different SNR environments. Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. The results of this paper can provide useful help for sound source localization in complex environments.

Lightweight Semantic-Aided Localization With Spinning LiDAR Sensor

Autonomous driving demands robust and precise vehicle localization in complex environments with limited on-board computational resources. Incorporating reliable semantic information with localization algorithms can increase accuracy remarkably, however, the process of extracting semantic information from LiDAR point clouds and matching it to semantic maps is computationally intensive. Moreover, pure semantic localization cannot achieve the robustness requirements for safe self-driving as the necessary quantity of semantic landmarks cannot be guaranteed under extreme conditions. In this paper, we present a lightweight semantic-aided localization method that improves upon traditional techniques in two ways. First, we propose a highly efficient pipeline to extract three semantic classes from a LiDAR scan. Second, instead of semantic 3D point cloud registration, map matching is performed through 2D key point matching. We then integrate these two functions into a dynamic semantic aided localization framework. Our on-road experiments demonstrate that the proposed method achieves both the high accuracy of semantic localization and the robustness of non-semantic localization. With our algorithm consuming under 10% of CPU resources, we observe reduced positioning error, especially peak error, when comparing to non-semantic counterparts.

Mobile Agents-Based Collaborative Perception for Pollution Source Localization in Complex Environments

In this letter, we present a multiagent collaborative perception scheme for pollution source localization in complex environments. To ensure reliable connectivity, a crowd-sensing model is designed to adaptively adjust the sensing range of the swarm of mobile agents. While by introducing the residual energy of agents, a modified mayfly algorithm (MA) is adopted to guide mobile agents to move toward the pollutant source, which can give attention to both energy efficiency and localization accuracy. Besides, artificial potential field is utilized to construct the repulsive force field of each mobile agent to avoid collisions in the process of moving toward the pollution source. Experimental simulations validate that the proposed method has better performance in localization accuracy, collision avoidance, and energy efficiency.

License Plate Localization in Complex Environments Based on Improved GrabCut Algorithm

Aiming at the problem that the existed license plate detection method lacking of accuracy and speed, an improved lightweight detection algorithm for license plate detection in natural scenarios was proposed. First, the traditional GrabCut algorithm needs to interactively provide a candidate frame in order to perform the target detection work. We replace the candidate frame by introducing the Aspect ratio of the license plate as the foreground extraction feature to automate the detection of the license plate by GrabCut algorithm. Then, in order to improve the detection precision of traditional target detection algorithms, we introduced the Wiener filter, which is widely used in the field of digital signal processing, and Combine with Bernsen algorithm to complete image noise reduction. Finally, the algorithm was tested with the CCPD dataset, which contains many vehicle images from different complex natural scenes, especially low-resolution images. The experimental results shows that improved GrabCut algorithm achieves an average accuracy of 99.34% for license plate localization and a detection speed of 0.29s/frame, which has better accuracy and real-time performance compared with traditional GrabCut and other license plate localization algorithms.

Pollution Source Localization in Complex Environments Using A Swarm of Mobile Agents

In this letter, we present a pollution source localization method combining particle swarm optimization (PSO) and artificial potential field (APF). PSO is used to control mobile agents to move toward the pollutant source according to concentration and position information. Meanwhile, to avoid collision during the movement, it is assumed that there exist repulsions around mobile agents and obstacles in monitoring area. APF is used to construct the corresponding repulsive force fields for each mobile agent. Besides, the residual energy is introduced to balance the energy consumption among mobile agents. Once the agent swarm locates the pollutant source, support vector regression is adopted to model the pollution diffusion based on concentration measurements and distribution of mobile agents. Experimental evaluations demonstrate the effectiveness and feasibility of our proposed method.

DeepTAL: Deep Learning for TDOA-Based Asynchronous Localization Security With Measurement Error and Missing Data

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.

Soft Range Information for Network Localization

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramer-Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.

Editorial: Recent advances in intelligent robotic systems

This Editorial introduces the motivation and organization process of the special issue on intelligent robotic systems. The major contributions of the six papers published in the special issue are discussed. In these papers, three papers present recent progresses in robot navigation and localization in complex environments. One paper reports a new robot sensing approach. The other two papers address the problems in robot dynamics modeling and service modeling in human-robot interaction.

Parameter-based binaural hearing aid algorithms to improve speech intelligibility and localization in complex environments.

This paper presents new binaural enhancement and noise suppression algorithms for binaural hearing aids. To enhance interaural level difference (ILD) cues at low frequencies, which are usually small, interaural time difference (ITD) cues are estimated and transformed to ILDs. The binaural noise suppression algorithm consists of adaptive beamforming and a coherence-based suppression filter. The estimated phase and signal-to-noise ratio (SNR) at each hearing aid are used to perform the processing. The performance of the proposed methods was assessed using perceptual evaluation with hearing-impaired listeners and objective evaluation.

User localization in complex environments by multimodal combination of GPS, WiFi, RFID, and pedometer technologies.

Many user localization technologies and methods have been proposed for either indoor or outdoor environments. However, each technology has its own drawbacks. Recently, many researches and designs have been proposed to build a combination of multiple localization technologies system which can provide higher precision results and solve the limitation in each localization technology alone. In this paper, a conceptual design of a general localization platform using combination of multiple localization technologies is introduced. The combination is realized by dividing spaces into grid points. To demonstrate this platform, a system with GPS, RFID, WiFi, and pedometer technologies is established. Experiment results show that the accuracy and availability are improved in comparison with each technology individually.