Beamforming is an attractive technique to improve the system performance for multi-input-multi-output (MIMO) communications. Previous works mainly focus on improving the data transmission quality. However, the potential of beamforming for improving the localization quality is not yet fully studied. In this paper, we focus on active beamforming to reduce the user equipment (UE) localization error for millimeter-wave (mmWave) MIMO systems. Such beamforming for localization is of challenge because its optimization cost function (e.g., the localization error bound) also depends on the actual UE location and instantaneous channel states, which are unknown in advance. To address this challenge, a novel successive localization and beamforming (SLAB) scheme is proposed, where the long-term UE location and the instantaneous channel state will be jointly estimated and then the beamforming vector will be successively optimized as per the obtained estimation results. The proposed SLAB scheme will yield a sequence of beamforming weights and UE location estimates, which will converge to the stationary point of the associated optimization problem. Simulation results show that the proposed SLAB scheme achieves a huge performance gain for UE localization compared with state-of-the-art baselines.

Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, called R parameter, to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50% when compared to the conventional lambert model in scenes influenced by ambient light.

Visible light communication (VLC)-based simultaneous positioning and orientating (SPAO), using received signal strength (RSS) measurements, is studied in this paper. RSS-based SPAO for VLCs of great challenge as it is essentially a non-convex optimization problem due to the nonlinear RSS model. To address this non-convexity challenge, a novel particle-assisted stochastic search (PASS) algorithm is proposed. The proposed PASS-based SPAO scheme does not require the knowledge of the height of receiver, the perfect alignment of transceiver orientations or inertial measurements. This is a huge technical improvement over the existing VLC localization solutions. The algorithmic convergence is established to justify the proposed ASS algorithm. In addition, a closed-form Cramer-Rao lower bound (CRLB) on localization error is derived and analyzed to gain insights into how the VLC-based SPAO performance is related to system configurations. It is shown that the receiver's position and orientation accuracy is linear with signal-to-noise ratio and direction information. In addition, the position accuracy decays with six powers of the transceiver distance, while the orientation accuracy decays with four powers of the transceiver distance. Finally, simulation results verify the performance gain of the proposed PASS algorithm for VLC-based SPAO.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

In this paper, we aim at providing a unified performance analysis framework for visible light-based positioning (VLP) using received signal strength (RSS), which can be used to gain insights into improving the performance of RSS-based VLP systems. Specifically, we first obtain a closed-form Cramer- Rao lower bound (CRLB) for the user equipment (UE) location and orientation, respectively. Then, we reveal the impact of the signal-to-noise ratio (SNR), transmission distance, prior knowledge and the number of LED sources on the RSS-based VLP performance. Moreover, the impact of the non-line-ofsight (NLOS) propagation on the RSS-based VLP performance is studied. It is shown that the RSS-based VLP performance will hit an error floor caused by the unknown NLOS links. These NLOS-caused UE location and orientation error floors in the high SNR region are analyzed. Finally, the information contribution of each LED source is studied to give an intuitive understanding on the impact of the LED array geometry on the RSS-based VLP performance. The obtained CRLB and the associated VLP performance analysis form a theoretical basis for the design of efficient VLP algorithms and VLP performance optimization strategies (e.g., resource allocation and smart LED source selection).

Wireless fingerprint localization technologies have drawn significant attention because of their ubiquitous infrastructure and low energy consumption. However, they often suffer from the Received Signal Strength (RSS) instability (i.e., RSS variations across heterogeneous devices and RSS fluctuations over time) and fingerprint spatial ambiguity issues. Although various deep learning-based methods have been proposed recently, most of them only pursue remarkable accuracy in an ideal environment and escape the above-mentioned problems. The co-existence of these factors would make most existing methods suboptimal or invalid when applied in real scenarios. In this paper, to address the RSS instability, we define the fingerprint spatial gradient (FSG), which can convert the vulnerable and unstable fingerprints to stable relative values but lose absolute information for positioning. Next, to relieve the fingerprint instability and model the temporal sequential dependencies in a trajectory, we propose a Long Short-Term Memory network (LSTM)-based framework to fuse FSG and RSS, where an attention module is used to guide the fusion according to their contributions to the result. Finally, the beam search strategy is used to search for the optimal solution from outputs of the LSTM, eliminating the spatial ambiguity of fingerprints. The experiments show that DeepWiPos reduces average positioning error by 23.44% and 22.18% on Bluetooth and Wi-Fi, respectively when compared with the state-of-the-art.