Accurate Time Of Arrival Estimation Research Articles

Robust Indoor Positioning with Smartphone by Utilizing Encoded Chirp Acoustic Signal

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods.

Robust acoustic reflector localization using a modified EM algorithm

In robotics, echolocation has been used to detect acoustic reflectors, e.g., walls, as it aids the robotic platform to navigate in darkness and also helps detect transparent surfaces. However, the transfer function or response of an acoustic system, e.g., loudspeakers/emitters, contributes to non-ideal behavior within the acoustic systems that can contribute to a phase lag due to propagation delay. This non-ideal response can hinder the performance of a time-of-arrival (TOA) estimator intended for acoustic reflector localization especially when the estimation of multiple reflections is required. In this paper, we, therefore, propose a robust expectation-maximization (EM) algorithm that takes into account the response of acoustic systems to enhance the TOA estimation accuracy when estimating multiple reflections when the robot is placed in a corner of a room. A non-ideal transfer function is built with two parameters, which are estimated recursively within the estimator. To test the proposed method, a hardware proof-of-concept setup was built with two different designs. The experimental results show that the proposed method could detect an acoustic reflector up to a distance of 1.6 m with 60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$60\\%$$\\end{document} accuracy under the signal-to-noise ratio (SNR) of 0 dB. Compared to the state-of-the-art EM algorithm, our proposed method provides improved performance when estimating TOA by 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} under a low SNR value.

An Efficient Random Access Reception Algorithm for ToA Estimation in NB-IoT

Narrowband Internet of Things (NB-IoT) aims to provide wide coverage for a massive number of low-cost devices. Therefore, an NB-IoT physical random access channel (NPRACH) preamble based on a single tone signal with frequency hopping was designed, enabling the base station to estimate the time-of-arrival (ToA) values for realizing uplink synchronization among multiple users. However, due to residual carrier frequency offset (RCFO) in the NPRACH preamble, it is crucial to keep the accuracy of the ToA estimation. Recognizing this urgency, in this paper we first judiciously investigate the effect of the hooping distance on ToA estimation. With which, we propose an efficient receiving algorithm to improve the accuracy of ToA estimation. The main aim of the algorithm is to treat two consecutive symbol groups as a whole and then carry out difference calculations on the two newly constituted symbol groups to construct a more logical frequency hopping distance. Extensive numerical results validate the superiorly of our proposed scheme compared against conventional strategies, showing that the probability of the ToA estimation obtained by this method is 99% within the acceptable error range.

A deep-learning-based time of arrival estimation using kernel sparse encoding scheme

This letter proposes a deep-learning-based method for time of arrival (TOA) estimation with a new sparse encoding scheme, aiming to solve the problems caused by quantization errors and off-grid effects. The proposed method utilizes a convolutional neural network (CNN) to learn the relationship between the training signals and the TOA parameters encoded by the proposed kernel sparse representation. It encodes true TOA values into an approximate sparse representation by the sinc-kernel function. This scheme can reserve more off-grid features of the estimated TOA parameters to enhance the optimization efficiency of the CNN-based network, so it can effectively minimize the quantization errors and improve the estimation performance of different off-grid distributions. Numerical simulations show that the proposed encoding model is superior to the existing encoding models under the same CNN framework, and the proposed method achieves better TOA estimation accuracy than the competitive end-to-end CNN-based estimation methods and outperforms in accuracy and speed compared with other existing off-grid estimation methods.

Impact Analysis of Intercell Interference in Cellular Networks for Navigation Applications

This article studies the intercell interference (ICI) effects in cellular networks for navigation applications. This is achieved through the derivation of an analytical expression for a cellular navigation receiver postcorrelation signal-to-noise power ratio (SNR) in the presence of multiple asynchronous cells. It reveals that in addition to the channel power, the cell loading rate and data modulation order for the interfering cells also play important roles in affecting the received signal quality of the desired cell. Furthermore, the time-of-arrival (TOA) estimation and positioning accuracy degradation due to the ICI is characterized by the Cramer–Rao and Ziv–Zakai lower bounds based on the derived postcorrelation SNR. Simulations are performed to verify the theoretical expressions and the results indicate that the ICI term can be treated as an additional Gaussian disturbance for characterizing the TOA estimation accuracy in cellular navigation receivers.

Multipath tracking with LTE signals for accurate TOA estimation in the application of indoor positioning

ABSTRACT Indoor positioning with high accuracy plays an important role in different application scenarios. As a widely used mobile communication signal, the Long-Term Evolution (LTE) network can be well received in indoor and outdoor environments. This article studies a method of using different reference signals in the LTE downlink for carrier phase time of arrival (TOA) estimation. Specifically, a solution is proposed and a multipath tracking Software Defined Receiver (SDR) is developed for indoor positioning. With our SDR indoor positioning system, the pilot signals of the LTE signals are firstly obtained by the coarse synchronization and demodulation. Then, with the assistance of the pilot signals, the time delay acquisition, the multipath estimating delay lock loop (MEDLL) algorithm, and the multipath anomaly detection are sequentially carried out to obtain navigation observations of received signals. Furthermore, to compare the performance of different pilot signals, the Secondary Synchronous Signals (SSS) and Cell Reference Signals (CRS) are used as pilot signals for carrier phase-based TOA estimation, respectively. Finally, to quantify the accuracy of our multipath tracking SDR, indoor field tests are carried out in a conference environment, where an LTE base station is installed for commercial use. Our test results based on CRS show that, in the static test scenarios, the TOA accuracy measured by the 1-σ error interval is about 0.5 m, while in the mobile environment, the probability of range accuracy within 1.0 m is 95%.

Signal Tracking Algorithm With Adaptive Multipath Mitigation and Experimental Results for LTE Positioning Receivers in Urban Environments

Positioning with cellular signals has been gaining attention in urban and indoor environments, where global navigation satellite system signals have limited availability due to interference, blockage, or multipath. However, accurate and reliable tracking of cellular signals under highly dynamic urban channel conditions remains a challenging task. This article presents a cellular long-term evolution (LTE) signal tracking algorithm implemented by an adaptive multipath estimating delay lock loop (AMEDLL) to achieve carrier phase synchronization and time-of-arrival (TOA) tracking under severe multipath propagation conditions. The analytical expression of the coherently integrated correlation result over multiple slots with the LTE cell-specific reference signal is derived. A multipath estimator along with a simple yet efficient multipath estimation monitoring approach is developed to estimate the parameters of all detected multipath signals. Several heuristic monitoring criteria based on historical multipath parameter estimations are established to enable adaptive adjustment of the estimated path number. Real LTE signals are collected in an urban environment for the signal tracking performance evaluation. This article presents two case studies with varying levels of multipath effects and signal power to illustrate the effectiveness of the developed signal tracking algorithm. Instead of a TOA truth reference, open-loop carrier phase estimations are used to analyze the TOA tracking error. Our analyses demonstrate that the AMEDLL-based tracking algorithm provides improved TOA estimation accuracy over the existing super-resolution-algorithm-based and delay-lock-loop-based tracking schemes.

Time of Arrival Ranging and Localization Algorithm in Multi-Path and Non-Line-of-Sight Environments in OFDM System

Time of Arrival (TOA) is a widely used wireless cellular network ranging technology. How to perform accurate TOA estimation in multi-path and non-line-of-sight (NLOS) environments and then accurately calculating mobile terminal locations are two critical issues in positioning research. For NLOS identification, it can be performed in the TOA measurement part and the position calculation part, respectively. In this paper, for the above two steps, two schemes for mitigating NLOS errors are proposed. First of all, a TOA ranging method based on clustering theory is proposed to solve the problem of line of sight (LOS) path estimation in multi-path channels. We model TOA ranging as a Gaussian mixture model and illustrate how LOS and NLOS can be measured and identified based on non-parametric Bayesian methods when the wireless transmission environment is unknown. Moreover, for NLOS propagation channels, this paper proposes a user location estimator based on the maximum a posteriori (MAP) criterion. Combined with the TOA estimation and user location computation scheme proposed in this paper, the terminal's positioning accuracy is improved. Experiments show that the TOA measurement and localization algorithms presented in this paper have good robustness in complex wireless environments.

Simultaneous Model and Parameter Estimation for Joint Communication and Positioning

Joint communication and positioning based on a unified signal structure yields synergy and can complement and assist system designs enabling higher coverage and quality of service for both communication and positioning. For the time of arrival (TOA) based joint communication and positioning the TOA estimation accuracy is crucial. It is known to translate directly into position estimation accuracy. In the presence of multipath propagation, the estimation accuracy of signal arrival times in return strongly depends on the actual as well as the estimated number of the physical path parameters, the model order. In this work, we assess the performance and the mutual impact of simultaneous model order and parameter estimation for channel-estimation-based joint communication and positioning. Besides introducing a terrestrial channel-estimation-based unified joint communication and positioning system framework, we discuss and numerically compare different methods to sequentially or jointly estimate the parameters and the model order. We show that a TOA error-minimizing model order estimation is preferable over estimating the correct model order. Furthermore, we compare the performance with a proposed focused order-related lower bound. This bound determines the optimal model order for a chosen estimator. It depends on the actual and hypothetical model order and it replaces the here unsuitable Cramer-Rao lower bound. Secondly, the comparison shows that employing the parameter and model order-dependent inverse Fisher information matrix yields a close-to-optimal approach. We numerically show for a realistic channel scenario with many multipath parameters that the method is still accurate.

High Accurate Time-of-Arrival Estimation With Fine-Grained Feature Generation for Internet-of-Things Applications

Conventional schemes often require extra reference signals or more complicated algorithms to improve the time-of-arrival (TOA) estimation accuracy. However, in this letter, we propose to generate fine-grained features from the full band and resource block (RB) based reference signals, and calculate the cross-correlations accordingly to improve the observation resolution as well as the TOA estimation results. Using the spectrogram-like cross-correlation feature map, we apply the machine learning technology with decoupled feature extraction and fitting to understand the variations in the time and frequency domains and project the features directly into TOA results. Through numerical examples, we show that the proposed high accurate TOA estimation with fine-grained feature generation can achieve at least 51% root mean square error (RMSE) improvement in the static propagation environments and 38 ns median TOA estimation errors for multipath fading environments, which is equivalently 36% and 25% improvement if compared with the existing MUSIC and ESPRIT algorithms, respectively.

TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional match filtering (MF)-based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier domain at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative “cleaning” process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the MF estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators.

Effective Frequency Hopping Pattern for ToA Estimation in NB-IoT Random Access

In narrow band-Internet of Things (NB-IoT) systems, a preamble of physical random access channel (NPRACH) is based on a single-tone waveform combined with frequency hopping, which is appropriate to achieve the goals of IoT, such as long battery lifetime and extended coverage. Since the hopping pattern of a preamble is predetermined, it is possible to estimate time-of-arrival (ToA) using phase difference. The accurate ToA is very important for device positioning and uplink signal decoding. In this paper, we design a new frequency hopping pattern of NPRACH preamble which fully utilizes all feasible hopping distances for a given number of subcarriers in NPRACH. The proposed hopping pattern maintains the compatibility with the standard system, by keeping the NPRACH structure in standard with minimal change. The simulation results show that the proposed hopping pattern greatly improves the accuracy of ToA estimation without additional system overhead.

UWB pulse detection and TOA estimation using GLRT

In this paper, a novel statistical approach is presented for time-of-arrival (TOA) estimation based on first path (FP) pulse detection using a sub-Nyquist sampling ultra-wide band (UWB) receiver. The TOA measurement accuracy, which cannot be improved by averaging of the received signal, can be enhanced by the statistical processing of a number of TOA measurements. The TOA statistics are modeled and analyzed for a UWB receiver using threshold crossing detection of a pulse signal with noise. The detection and estimation scheme based on the Generalized Likelihood Ratio Test (GLRT) detector, which captures the full statistical information of the measurement data, is shown to achieve accurate TOA estimation and allows for a trade-off between the threshold level, the noise level, the amplitude and the arrival time of the first path pulse, and the accuracy of the obtained final TOA.

Pseudorange error analysis for precise indoor positioning system

Abstract There is a currently developed system of a transmitter indoor localization intended for fire fighters or members of rescue corps. In this system the transmitter of an ultra-wideband orthogonal frequency-division multiplexing signal position is determined by the time difference of arrival method. The position measurement accuracy highly depends on the directpath signal time of arrival estimation accuracy which is degraded by severe multipath in complicated environments such as buildings. The aim of this article is to assess errors in the direct-path signal time of arrival determination caused by multipath signal propagation and noise. Two methods of the direct-path signal time of arrival estimation are compared here: the cross correlation method and the spectral estimation method.

Correlation-sum-deviation ranging method for vehicular node based on IEEE 802.11p short preamble

Growth of the traffic flow and traffic accident has raised more and more demands on wireless communication and positioning technologies that can provide new services such as vehicle collision warning and traffic management. Currently, the global navigation satellite system such as global positioning system and BeiDou satellite positioning system is widely used in vehicles and is fairly accurate in flat open areas. However, the global navigation satellite system can only work in line of sight environment, and it fails to operate in non-line of sight tunnels or downtown areas where blockage of satellite signals is frequent. Because of the shortages of global navigation satellite system, the wireless ranging or positioning system using the short preamble of IEEE 802.11p is provided. Typically, accurate time of arrival estimation is very important for positioning estimation. In order to improve the precision of the time of arrival estimation, a correlation-sum-deviation method for ranging using the IEEE 802.11p short preamble is proposed. Simulation results are presented which show that in both the additive white Gaussian noise channel and the international telecommunications union multipath channel for vehicular environments, the proposed method provides better precision and is less complex than other techniques, particularly when the signal-to-noise ratio is low.

IEEE 802.15.4 ZigBee-Based Time-of-Arrival Estimation for Wireless Sensor Networks.

Precise time-of-arrival (TOA) estimation is one of the most important techniques in RF-based positioning systems that use wireless sensor networks (WSNs). Because the accuracy of TOA estimation is proportional to the RF signal bandwidth, using broad bandwidth is the most fundamental approach for achieving higher accuracy. Hence, ultra-wide-band (UWB) systems with a bandwidth of 500 MHz are commonly used. However, wireless systems with broad bandwidth suffer from the disadvantages of high complexity and high power consumption. Therefore, it is difficult to employ such systems in various WSN applications. In this paper, we present a precise time-of-arrival (TOA) estimation algorithm using an IEEE 802.15.4 ZigBee system with a narrow bandwidth of 2 MHz. In order to overcome the lack of bandwidth, the proposed algorithm estimates the fractional TOA within the sampling interval. Simulation results show that the proposed TOA estimation algorithm provides an accuracy of 0.5 m at a signal-to-noise ratio (SNR) of 8 dB and achieves an SNR gain of 5 dB as compared with the existing algorithm. In addition, experimental results indicate that the proposed algorithm provides accurate TOA estimation in a real indoor environment.

High Resolution TOA Estimation Based on Compressed Sensing

This paper proposes a novel time of arrival (TOA) estimation method which achieves the sub-chip resolution. In this proposed method, the sparsity of the wireless channel is utilized to adopt the emerging technique of compressive sensing (CS) for accurate TOA estimation. The received signal is first processed to make the sensing matrix satisfy the restrict isometry property requirement of CS framework; then the signal samples with the amplified noise are removed to improve the recovery performance; finally, the dantzig selector method is used to recover the sparse channel for TOA estimation.

Circular Arrays and Inertial Measurement Unit for DOA/TOA/TDOA-Based Endoscopy Capsule Localization: Performance and Complexity Investigation

This paper investigates the performance of wireless capsule endoscopy (WCE) tracking within the irregular environment of digestion system by integrating directional-of-arrival (DOA), time-of-arrival (TOA) [or time-difference-of-arrival (TDOA)], and inertial measurement unit (IMU) measurements using the extended Kalman filter (EKF). Here, an emitter transmits a signal to the WCE, and the WCE replies the signal back to a set of antenna arrays. Then, the round trip TOA, and the DOA of the signals transmitted from WCE to the antenna arrays are measured. The digestion system path model is considered unknown, and the movement model between two consecutive signals is assumed linear. Simulations are conducted to investigate the EKF performance as a function of the number of antenna arrays, antenna array configuration, and TOA (and TDOA) estimation accuracy. In addition, the impact of IMU on the localization performance will be studied. Simulations confirm the proposed WCE tracking performance. In addition, the computational complexity of the EKF with respect to the number of antenna array is studied.

Time-of-arrival estimation through WLAN physical layer systems

Time-of-arrival (ToA) has been the most popular measurement parameter when estimating distance between two nodes, i.e., ranging. Several localization-only systems such as radar systems, satellite positioning systems, ultra-wideband systems provide their accurate ToA estimation methodologies for the ranging in their physical layer. In contrast to the localization-only systems, wireless LAN (WLAN) has no standard rule for accurate ranging method. Delay and channel uncertainty is the major problem of the ToA estimation. In this paper, delay characteristic of WLAN system is analyzed and how accurate ranging is possible using ToA estimation through WLAN physical layer round-trip time measurement. Experimental results of indoor/outdoor ranging and positioning are also provided.

Cramer-Rao lower bound for wideband ToA estimation in the presence of RF nonideality

In wideband localization systems based on time of arrival (ToA), RF nonideality, such as carrier phase noise, IQ imbalance, phase and frequency offset, plays an important role for ToA estimation accuracy. In this work, Cramer-Rao lower bound (CRB) for ToA estimation in the presence of RF nonideality is first proposed as criterion for ToA estimator performance in practical ToA localization system. Evaluation of CRB with and without phase noise has been performed, especially closed form of CRB without phase noise has been derived. Theoretical and numerical results show that the ToA accuracy presents floor phenomenon in the presence of RF nonideality, and phase noise plays the dominant role. The effect of record length has also been evaluated.