Radim Zemek

A Perturbation Analysis on the Performance of TOA and TDOA Localization in Mixed LOS/NLOS Environments

In cellular communication systems where non-line-of-sight (NLOS) channels are dominant as compared to line-of-sight (LOS) channels, a time difference of arrival (TDOA) localization method can outperform a time of arrival (TOA) localization method, although the TDOA method loses one degree of freedom in the number of usable range estimates when selecting a range estimate as the reference and doubles the variance of range sampling error when subtracting the reference range estimate. In this paper, we first show the computer simulation result on the localization performance of conventional TOA and TDOA methods in mixed LOS/NLOS environments, and then validate it by theoretical analysis. Here, we analyze the root mean square localization error by decomposing it into two factors; for one factor, which is the contribution from range sampling error, we evaluate it by the Cramer-Rao lower-bound, whereas for the other factor, which is the contribution from positively biased NLOS range error, we analyze it by perturbation method. We show that the theoretical result can well explain the localization performance by computer simulation for both the TOA and TDOA methods, and furthermore, we derive a simple condition among the number of cells, the average and variance of NLOS range error distribution which can correctly predict whether the TDOA method outperforms the TOA method or not for the case of all NLOS channels.

A Joint Estimation of Target Location and Channel Model Parameters in an IEEE 802.15.4-based Wireless Sensor Network

Many target localization techniques based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and its parameters for an area where a target node is localized. This is a limiting factor since an intensive pre- measurement campaign is required to determine the model and its parameters. Basing on channel measurement campaigns in different areas we confirmed that the variation in the RSSIs of the IEEE 802.15.4 signal can be described by a two-layer model. We therefore propose a novel target localization method with no prior knowledge of model parameter values. The method is based on a joint maximum likelihood target localization and channel model parameters estimation. The proposed method was experimentally verified in real environments and the results show that the location estimation accuracy outperforms the accuracy of the conventional method where the values of the channel model parameters are known in advance.

RSSI-based Localization without a Prior Knowledge of Channel Model Parameters

In target node localization problem, conventional methods based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and values of its parameters specific for an environment. This limits the conventional localization system to be set up quickly and effectively due to a necessary pre-measurement step to determine both the channel model and the values of its parameters. To address the limitation, a two-stage iterative algorithm which allows to localize a target node without any prior knowledge of the parameter values has been propose. Each stage of the algorithm can be implemented using different estimation methods, such as maximum likelihood (ML) and least square (LS) estimation which provides four different combinations. To determine the best combination, the location estimation performance for all four combinations is evaluated using experimental data collected in measurement campaigns on various indoor locations. The results reveal that the combination of ML estimation method implemented in both stages provides the best location estimation accuracy and the fastest convergence rate.

An Effect of Anchor Nodes Placement on a Target Location Estimation Performance

Many indoor localisation and tracking techniques in wireless sensor networks assume placing anchor nodes on a ceiling. However, placing the nodes on the ground can improve target localisation estimation accuracy based on received signal strength indicator as we show in this work. The presented results are based on an experiment conducted in a hall under two sets of conditions. In one case, people were present in the hall and in the other one, the hall was empty. For the case when people were present in the hall, the location estimation performance improved from 3.7 meters root-mean square error (RMSE) when using only the anchor nodes fixed to the ceiling to 2.2 meters RMSE when using only the anchor nodes placed on the ground. For the case when the hall was empty, the location estimation performance improved from 4.1 meters RMSE when using only the anchor nodes fixed to the ceiling to 2.4 meters RMSE when using only the anchor nodes placed on the ground. The target location estimation accuracy improved by 40% for both conditions

Analysis on TOA and TDOA Location Estimation Performances in a Cellular System

In a computer simulation result on the location estimation for a cellular system, we found that a Time Difference Of Arrival (TDOA) method outperforms a Time Of Arrival (TOA) method when Non-Line-Of-Sight (NLOS) channels are dominant, whereas the TOA method outperforms the TDOA method when LOS channels are dominant. We could not easily accept the result, because TDOA method loses one degree of freedom in the number of usable range estimates when selecting a range estimate as a reference and doubles the variance of range error when subtracting the reference range estimate. In this paper, we try to theoretically prove the interesting result by the computer simulation. We divide the root mean square location estimation error into two factors, such as the error variance contributed from range sampling error and the error bias contributed from positive NLOS range error. For the error variance, we evaluate it by the Cramer-Rao lower bound, whereas for the error bias, we analyze it by perturbation method.

Effect of Walking People on Target Location Estimation Performance in an IEEE 802.15.4 Wireless Sensor Network

Target location estimation is one of many promising applications of wireless sensor networks. However, until now only few studies have examined location estimation performances in real environments. In this paper, we analyze the effect of walking people on target location estimation performance in three experimental locations. The location estimation is based on received signal strength indicator (RSSI) and maximum likelihood (ML) estimation, and the experimental locations are a corridor of a shopping center, a foyer of a conference center and a laboratory room. The results show that walking people have a positive effect on the location estimation performance if the number of RSSI measurements used in the ML estimation is equal or greater than 3, 2 and 2 in the case of the experiments conducted in the corridor, foyer and laboratory room, respectively. The target location estimation accuracy ranged between 2.8 and 2.3 meters, 2.5 and 2.1 meters, and 1.5 and 1.4 meters in the case of the corridor, foyer and laboratory room, respectively.

A Belief Propagation-Based Iterative Location Estimation Method for Wireless Sensor Networks

Location estimation is one of the most attractive applications in wireless sensor networks. In this paper, we propose a new iterative location estimation method using radio signal strength indicators (RSSIs) not only among target and fixed nodes but also among target nodes. Our computer simulation shows that, if the RSSIs among target nodes are available whose locations are unknown so should be estimated, we can improve the location estimation performance by means of the proposed iterative method

Use of Soft-Decision TOA for Location Estimation

A soft-decision range estimation has been proposed, which outputs a list of likely discrete distances with a list of weights similar to likelihood values. Its application to location estimation has a potential for improving the estimation accuracy, but we need to consider two fundamental problems such as how to furthermore improve the performance of the soft-decision range estimation and how to modify the discrete output to be suited for the continuous maximization in location estimation. In this paper, we tackle the above two problems; to solve the first problem, we introduce the K-means algorithm instead of a conventional threshold-based clustering, and to solve the second problem, we propose a continualization method using an asymmetric Gaussian function which makes it possible to apply gradient-based maximization algorithms.

A Traffic Reduction Method for Centralized RSSI-Based Location Estimation in Wireless Sensor Networks

In a centralized localization scenario, the limited throughput of the central node constrains the possible number of target node locations that can be estimated simultaneously. To overcome this limitation, we propose a method which effectively decreases the traffic load associated with target node localization, and therefore increases the possible number of target node locations that can estimated simultaneously in a localization system based on received signal strength indicator (RSSI) and maximum likelihood estimation. Our proposed method utilizes a threshold which limits the amount of forwarded RSSI data to the central node. As the threshold is crucial to the method, we further propose a method to theoretically determine its value. We experimentally verified the proposed method in various environments and the experimental results revealed that the method can reduce the load by 32-64% without significantly affecting the estimation accuracy.

A threshold-based MIMO TOA location estimation for cellular systems

In a cellular system, Multiple-Input/Multiple-Output (MIMO) technique increases the number of links for not only spatially multiplexing data to transmit/receive but also measuring the distance between a base station (BS) and a mobile station (MS) at the same time. Therefore, if the MIMO technique is applied to a system-based location estimation, it must improve the accuracy of location estimate. This paper evaluates the performances of threshold-based Time of Arrival (TOA) location estimation methods in cellular systems. Computer simulation results on the location estimation error reveal that, if we employ no threshold, the selection method is a proper choice, whereas if we can optimally adjust the threshold according the Carrier-to-Noise power Ratio (CNR), the average method is a proper choice.