Geometry-Assisted Localization Algorithms for Wireless Networks

location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become unapplicable due to the insufficiency of signal sources. In this paper, a predictive location tracking (PLT) algorithm is proposed to alleviate this problem. The predictive information obtained from the Kalman filter is employed to provide the additional signal inputs for the location estimators. The proposed PLT scheme can offer persistent accuracy for location tracking of the mobile devices, especially with inadequate signal sources. Numerical results demonstrate that the proposed PLT algorithm can achieve better precision, comparing with other existing schemes, in mobile location estimation and tracking.

With the ever-growing number of networked devices and a higher likelihood of having line-of-sight communication most of the time, location estimation of a blind node in the 5th generation Ultra-Dense Networks (UDNs) system has gained considerable attention in recent years. One of main factors for accurate location estimates in 5G UDN is node randomness. Several location methods and their performance analyses have been addressed for localization in 5G UDN. Although the distribution of reference nodes (RNs) is considered in the literature, the information on spatial node distribution is only used to evaluate the average performance and is not utilized in the location methods. In this paper, a Cramer-Rao lower bound (CRLB) and three location estimators including the iterative, closed-form, and hybrid algorithms are proposed for localization in 5G UDN with randomly distributed RNs. Both range measurements and prior information on spatial node distribution are utilized for the proposed location methods and CRLB. Moreover, some characteristics of the CRLB for 5G UDN localization are derived in this paper. Detail comparison between the proposed CRLB and the previous performance study on CRLB for 5G UDN is given. Theoretical analysis proves that the proposed CRLB for the case with randomly distributed RNs is smaller than the average CRLB for the case with fixed location RNs. The top and bottom bounds of the proposed CRLB in the cases with low and high signal noise ratios are also given. Performance evaluation shows that the proposed methods perform better than the conventional methods only based on range measurements and can asymptotically attain the CRLB.

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, two predictive location tracking algorithms are proposed to alleviate this problem. The predictive location tracking (PLT) scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the geometric-assisted PLT (GPLT) scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed GPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the GPLT algorithm can achieve better precision in comparison with other network-based location tracking schemes.

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

SUMMARY Precise real time estimates of earthquake magnitude and location are essential for early warning and rapid response. While recently multiple deep learning approaches for fast assessment of earthquakes have been proposed, they usually rely on either seismic records from a single station or from a fixed set of seismic stations. Here we introduce a new model for real-time magnitude and location estimation using the attention based transformer networks. Our approach incorporates waveforms from a dynamically varying set of stations and outperforms deep learning baselines in both magnitude and location estimation performance. Furthermore, it outperforms a classical magnitude estimation algorithm considerably and shows promising performance in comparison to a classical localization algorithm. Our model is applicable to real-time prediction and provides realistic uncertainty estimates based on probabilistic inference. In this work, we furthermore conduct a comprehensive study of the requirements on training data, the training procedures and the typical failure modes. Using three diverse and large scale data sets, we conduct targeted experiments and a qualitative error analysis. Our analysis gives several key insights. First, we can precisely pinpoint the effect of large training data; for example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Secondly, the basic model systematically underestimates large magnitude events. This issue can be mitigated, and in some cases completely resolved, by incorporating events from other regions into the training through transfer learning. Thirdly, location estimation is highly precise in areas with sufficient training data, but is strongly degraded for events outside the training distribution, sometimes producing massive outliers. Our analysis suggests that these characteristics are not only present for our model, but for most deep learning models for fast assessment published so far. They result from the black box modeling and their mitigation will likely require imposing physics derived constraints on the neural network. These characteristics need to be taken into consideration for practical applications.

This paper presents the performance of an adaptive location estimator combining Kalman filtering (KF) scheme with vision-assisted scheme for wireless sensor networks. To improve the location accuracy, a KF tracking scheme is employed at a mobile terminal to track variations of the location estimate. In addition, with a vision-assisted calibration technique based on the normalized cross-correlation scheme, the proposed approach is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in measuring a dynamic environment. Therefore, using the vision-assisted approach to estimate the locations of the reference nodes as landmarks, a KF-based scheme with the landmark information can calibrate the location estimation and improve the corner effect. The experimental results demonstrate that more than 60 percent of the location estimates computed from the proposed approach have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.

The location estimation techniques based on 802.11 b/g wireless local area network (WLAN) have received significant attention in recent years. In this paper we report Cramer -Rao Lower bounds (CRLB) for WLAN user location estimation in a computer laboratory. The CRLBs are computed by using real time radio signal strength measurements performed by user's mobile terminal. This distance error lower bound provides the reference for the accuracy of node localization. The numerical results shows that the mean location error is 1.30 meters and is better as compared to the localization technique using linear least square estimate. The experimental results also demonstrate that the location accuracy improves for higher values of path loss exponent.

A common technique for wireless positioning is to estimate time-of-arrivals (TOAs) of signals traveling between a target node and a number of reference nodes, and then to determine the position of the target node based on those TOA parameters. In determining the position of the target node from TOA parameters, linear or nonlinear least-squares (LS) estimation techniques can be employed. Although the linear LS techniques are suboptimal in general, they facilitate low- complexity position estimation. In this paper, performance of various linear LS techniques are compared, and suboptimality of the linear approach is quantified in terms of the Cramer-Rao lower bound (CRLB). Simulations are performed to compare the performance of the linear LS approaches versus the CRLBs for linear and nonlinear techniques.

Location estimating in indoor environments is an important research topic. RSSI can be expressed as a function of distance. RSSI based location estimating techniques are widely popular since no additional hardware is required. However, RSSI measurements collected in indoor environments are too noisy due to reflecting and scattering signals, and packet loss. Methods utilizing noisy signals, the RSSI measurements have large positioning errors that are unacceptable for indoor environments. In this paper, location estimation is aimed to be improved by adaptively filtering the RSSI measurements collected at indoor environment with Zigbee modules.

In this paper, we propose a novel method for location estimation of smart devices considering a generic shadowed $\alpha-\kappa-\mu$ distribution based $\alpha$-KMS fading environment, which is not considered for localization hitherto. Most of the existing path loss-based methods utilize a standard log-normal model only for localization; however, fading effects need to be considered to appropriately model the Received Signal Strength (RSS) values. Some of the localization methods utilize standard fading models such as Rayleigh, Nakagami-m, and Rician, to name a few; however, such assumptions lead to erroneous location estimates. The generic location estimator is applicable for all environments and provides accurate location estimates with correct estimates of $\alpha-\kappa-\mu$. We propose a feedback-induced gradient ascent algorithm based on feedback distance that maximizes the derived log-likelihood estimate of the actual location. The proposed method also addresses the non-convex nature of the maximum likelihood estimator and is computationally efficient. The performance is evaluated on a simulated testbed, and the localization results outperform existing state-of-the-art methods.

The location of mobile terminals in cellular networks is an important problem with applications in resource allocation, location sensitive browsing, and emergency communications. Finding cost effective location estimation techniques that are robust to non-line of sight (NLOS) propagation, quantization, and measurement noise is a key problem in this area. Quantized time difference of arrival (TDoA) and received signal strength (RSS) measurements can be made simultaneously by CDMA cellular networks at low cost. The different sources of errors for each measurement type cause RSS and TDoA measurements to contain independent information about mobile terminal location. This paper applies data fusion to combine the information of RSS and TDoA measurements to calculate a superior location estimate. Nonparametric estimation methods, that are robust to variations of measurement noise and quantization, are employed to calculate the location estimates. It is shown how the data fusion location estimators are robust, provide lower error than the estimators based on the individual measurements, and have low implementation cost.

Enhanced 911 is a first line of assistance for practically every emergency situation, and many cell phone users today expect the same results from an emergency call no matter where they are-whether on the side of the road, in the woods, or in a building. It is a vital part of our nation's emergency response and disaster preparedness system. In the context of 911 service, demand for providing reliable and accurate mobile station (MS) location estimation has become a high priority and has gained momentum in recent years. A major challenge in mobile station location estimation is locating an emergency caller within desired accuracy in an adverse environment where non-line-of-site (NLOS) propagation exists. This paper develops a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. A unique feature of this methodology development, compared to other approaches in the literature, is the application of the systems engineering process. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. The methodology consists of two stages. In the first stage, a series of time-of-arrival range measurements are made from each base station (BS) to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. Based on a simulation study, the methodology appears to have the potential to significantly improve the accuracy of location estimates in certain situations.

The Maximum Likelihood (ML) and Best Linear Unbiased (BLU) estimators of the location and scale parameters of an extreme value distribution (Lawless [1982]) are compared under conditions of small sample sizes and Type I censorship. The comparisons were made in terms of the mean square error criterion. According to this criterion, the ML estimator of σ in the case of very small sample sizes (n < 10) and heavy censorship (low censoring time) proved to be more efficient than the corresponding BLU estimator. However, the BLU estimator for σ attains parity with the corresponding ML estimator when the censoring time increases even for sample sizes as low as 10. The BLU estimator of σ attains equivalence with the ML estimator when the sample size increases above 10, particularly when the censoring time is also increased. The situation is reversed when it came to estimating the location parameter μ, as the BLU estimator was found to be consistently more efficient than the ML estimator despite the improved performance of the ML estimator when the sample size increases. However, computational ease and convenience favor the ML estimators. Keywords: maximum likelihood estimatorsbest linear unbiased estimatorslife-testingreliability

Position accuracy at any point in time has been a thing of great concern around the globe. Solution has been provided through radio devices with location estimation capabilities. Results have shown that the available location estimation algorithm has recorded improvement in terms of location accuracy and reliability. However, improving location accuracy should be a continuous process as security of life, and properties ought to be given a high priority. For this reason, currently, many localization algorithms are available. This work investigated two localization algorithms that were formulated based on the principle of multiple linear regression, namely weighted multiple linear regression (WMLR) and improved weighted multiple linear regression (iWMLR) algorithms, in order to suggest a better terminal localization algorithm. Location accuracy, range of errors, and R2 scores are the basis on which the aforementioned algorithms are evaluated. Finally, localization algorithm with high location estimation accuracy is proposed.

Localization of the nodes in wireless networks is crucial for supporting the location-based services (LBSs). In this study, we investigate the fundamental limits for time delay based location estimation in amplify-and-forward (AF) cooperative relay networks considering multipath environment. The limits are presented by obtaining Cramer-Rao Lower Bounds (CRLBs) expressions. Simulations are performed to verify the CRLB expressions and show the effects of node topology of the network, number of relay nodes, and number of multipath components (MPCs) on the location estimation of source node. According to the results, the placement of the relay nodes is very important to lower the location estimation error. Furthermore, using more relay nodes decreases the location estimation error, dramatically. Finally, processing more MPCs at the destination node is crucial to improve the estimation accuracy for both relaying strategy.