Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

In recent years, Indoor Positioning Systems (IPS) have gained significance across various applications, including asset tracking, monitoring, interior navigation, and location-based services. The use of Wi-Fi-based technology is a popular choice for IPS due to its cost-effectiveness and widespread accessibility. The Wi-Fi signal’s Round Trip Time (RTT) measurement, using the Fine Time Measurement (FTM) protocol, offers fewer ranging errors in Line of Sight (LOS) conditions. However, Wi-Fi RTT ranging measurements encounter higher-ranging errors in Non-Line of Sight (NLOS), multipath, and interference scenarios. This study examines the error in ranging measurements for different scenarios such as LOS, Glass, Metal, and Wall blocking scenarios. To address these challenges, we propose a method that combines calibrated RTT range and processed Received Signal Strength (RSS) feature values in constructing a fingerprinting map to enhance positioning accuracy. The methodology includes the development of a range compensation model for RTT range calibration, utilizing a Gaussian filter for RSS measurement values processing, and creating a classifier model to distinguish between LOS and Wall scenarios. This integrated approach reduces noise in measurement values, and the Gaussian Process Regression (GPR) algorithm is utilized to predict the final location of the user. Our proposed method achieved a positioning error of 0.79 m, surpassing the performance of RTT fingerprinting by 17.71 %, RSS fingerprinting by 49.68 % and trilateration methods by 29.46 %.

With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

This paper deals with the numerical implementation of the material sensitivity analysis, which is used to efficiently determine an optimal magnetization distribution on a ship hull. A material sensitivity formula for the forward problem formulated in terms of the equivalent magnetic charge method is analytically derived. Then, the components of the adjoint system including the magnetic pseudo-source are thoroughly investigated in order to obtain the first-order gradient information of an objective function with respect to the magnetic charge. The formula facilitates applying the deterministic approach to searching for an optimal charge distribution and also it yields stable and reliable solutions to magnetostatic inverse problems in three dimensions. Finally, the proposed method has been successfully applied to a scale model ship and the predicted results on the underwater field disturbance due to the remanent magnetization of the hull have been compared to real measurements.