An accurate and robust environment sensing algorithm for enhancing indoor localization

In this article, we use the principle of configurable multipath in the field of indoor localization. To validate it, we give a case study of a single AP-based indoor localization system. In the proposed system, we first model the wireless channel in the presence of the designed active relays. Then, we resolve multipaths using sparse recovery and then collaborate it with the maximum likelihood (ML). After that, a likelihood-based method is proposed to identify the paths that are used for assisting localization. We also propose a localization model with the hybrid measurements of the angle of arrival (AoA) and time of flight (ToF). Then, we give the design of the active relay, which has a simple all-analog circuit and is free of self-interference via creating isolation between transmitting and receiving channels. Finally, we implement the proposed system in various indoor environments with the designed AP and active relays. The experimental results show that the median errors of relative ranging and AoA are 0.5 m and 3.3°, respectively, and the median location error is 0.49 m with only one AP, which is comparable to the accuracy achieved by the systems using multiple APs.

The Internet of Thing (IoT) based applications can provide various services and be widely applied in intelligent home. With the tendency of house safety protection, the detecting accuracy and privacy of intrusion detection system, which detects the human motion in indoor environment, has become a continuing concern. Up to now, there are emerging many intrusion detection systems which employ different devices such as camera and infrared. However, the poor privacy and deployment of specialized devices are mainly disadvantages of the afore-mentioned systems for deploying in home environment. In this paper, we propose WLID, a whole-home level intrusion detection system based on RSSI (Received Signal Strength Indicator) measurements of WiFi in indoor complex environment. In order to expand the area of human presence detection, WLID cooperates with WiFi-enabled IoT devices such as smart TV, air conditioner and other smart devices. The detection system constructs a detection algorithm with the non-parametric statistical method by only using RSSI and realize whole-home level real-time detection by using software implementation. The experimental results show that WLID can achieve the consistent detection rate close to 100% in a practical home environment.

Indoor radio frequency positioning systems enable a broad range of location aware applications. However, the localization accuracy is often impaired by Non-Line-Of-Sight (NLOS) connections and indoor multipath effects. An interesting evolution in widely deployed communication systems is the transition to multi-antenna devices with beamforming capabilities. These properties form an opportunity for localization methods based on Angle of Arrival (AoA) estimation. This work investigates how multipath propagation can be exploited to enhance the accuracy of AoA localization systems. The presented multipath assisted method resembles a fingerprinting approach, matching an AoA measurement vector to a set of reference vectors. However, reference data is not generated by labor intensive site surveying. Instead, a ray tracer is used, relying on a-priori known floor plan information. The resulting algorithm requires only one fixed receiving antenna array to determine the position of a mobile transmitter in a room. The approach is experimentally evaluated in LOS and NLOS conditions, providing insights in the accuracy and robustness. The measurements are performed in various indoor environments with different hardware configurations. This leads to the conclusion that the proposed system yields a considerable accuracy improvement over common narrowband AoA positioning methods, as well as a reduction of setup efforts in comparison to conventional fingerprinting systems.

At present, most mature localization technologies are based on the line-of-sight (LoS) environment. However, many human activities occur in the indoor non-line-of-sight (NLoS) environment, and thus localization in this challenging environment is also indispensable. In addition, the localization using a single access point (AP) is more suitable for limited space, such as shopping malls and teaching buildings. Therefore, it is of great significance to study the localization technology of single AP in indoor NLoS environments. This paper proposes a localization algorithm to solve this difficult problem based on the natural reflection path, which can associate the locations of the target, scatterer, and AP. Firstly, the localization model is constructed based on the angle-of-departure ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> oD), angle-of-arrival (AoA), and relative-time-of-flight (rToF) of the natural reflection path. Unlike absolute time-of-flight (ToF), rToF can eliminate synchronization errors. Then, we derive the closed form solution of the target location, and the least square (LS) algorithm is used to solve the target location. Finally, we use the commercial-grade ray-tracing simulation software to conduct the experiments. Simulation results show that the algorithm can achieve high localization accuracy with only a few natural reflection paths.

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

Traditional Angle of Arrival (AoA)-based WiFi array indoor localization algorithms do not fuse Channel State Information (CSI) inter-packet data for estimation, which makes WiFi arrays less effective for localization in complex indoor environments. Most algorithms are overburdened leading to inefficient localization. To address these issues, in this article, an indoor positioning algorithm based on Higher-Order Singular Value Decomposition (HOSVD) is proposed. First, the CSI data are reconstructed as a new measurement matrix by borrowing subcarriers, and a third-order tensor is constructed. Next, tensor compression techniques are used to reduce computational complexity and the signal subspace is obtained by HOSVD. Then, the AoA is obtained by the Reduced Dimension Multiple Signal Classification (RD-MUSIC) method. Finally, the coordinates of the target can be obtained by triangulating the AoAs of the three Access Points (APs). According to the simulation experiments, the AoA can be estimated accurately at a low SNR and with low snapshots. In practical experiments, we can successfully estimate the AoA in complex indoor environments with shorter timelines using HOSVD without modifications to commercial hardware and produce a lower AoA error and localization error rates compared to other algorithms. The effectiveness of our proposed algorithm is proven by simulations and practical experiments.

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions.&#x0D; HIGHLIGHTS&#x0D; &#x0D; A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition&#x0D; A reflector’s pose is determined based on the signal received from beacons&#x0D; The reflector’s pose is used to determine the location of the RF source&#x0D; One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low&#x0D; &#x0D; GRAPHICAL ABSTRACT

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

In order to solve the highly nonlinear nonconvex problem of the mathematical model in Bluetooth (angle of arrival (AOA)) localization, this paper proposes an alternating minimization algorithm for three‐dimensional AOA localization. The algorithm method converts the solving error minimization solving three‐dimensional coordinate problem in Bluetooth three‐dimensional AOA localization model to solving four single‐variable problems and alternately optimizes each variable to get the global optimal solution, which reduces the complexity of the model solving and achieves the high‐precision localization performance without auxiliary information. The test is carried out in two states: dynamic line of sight (LOS) and static non–line of sight (NLOS), and the results show that the proposed method meets the demand for indoor positioning accuracy with a positioning error of 16 cm in LOS environment and 34 cm in NLOS environment.

In the era of the Internet of everything (IoE) and the metaverse, there is a growing demand for high-accuracy indoor positioning for applications such as autonomous robots, virtual reality devices, and smartphones. This paper proposed a monocular resonant beam phase based localization (MRBPL) system optimized for high-precision indoor positioning in multi-access scenarios. By leveraging the self-alignment characteristic and integrating the analysis of resonant beam phase, angle of arrival (AoA) matching, and time-of-flight (ToF) method for 3D point coordinate acquisition, the MRBPL system achieves monocular passive multi target 3D positioning with an error of less than 4 cm at a distance of 8 m. We present a novel multi-access angle of arrival (AoA) estimation method and integrate it with the ToF method to enable multi-target localization from a single base station. Simulations demonstrate the system’s feasibility and high precision.

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme.

Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently direction-finding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

Indoor localization has been actively researched in recent years due to the increasing demand for location-awareness services. However, to balance localization accuracy and system cost is always a challenge for indoor localization systems. Radio frequency identification (RFID) is a promising technology to achieve both goals, because of its reasonable cost and reliability. In this paper, we propose a novel RFID indoor localization system based on angle of arrival (AoA) and phase of arrival (PoA) methods. This system leverages RFID's two experimental signal diffusion characteristics to estimate AoA. One is that the interrogation zone is constrained in a lobe, and only in this area the tag can be queried. The second is that there exits a stable pattern of received signal strength (RSS) on angle changes. We use the two features to find a general area and to pinpoint the AoA consecutively. This effectively narrows the sampling zone (where signal needs to be sampled), and helps to reduce computational complexity. In addition, we reduce the multipath effect on range estimation by determining the AoA and rotating the reader into the direction of the target. Moreover, we exploit two signals with a slightly different frequency to eliminate the phase ambiguity issue. Our system takes only one reader and achieves mean accuracy of 23 cm. The simplicity and effectiveness of our system make it convenient to be used in practice.