Vehicle Velocity Estimation Based on RSS Measurements

Wireless Communications and Mobile Computing, 2007

Location Based Services (LBS) is a new type of services for mobile phone users based on mobile terminal (MT) location. A large number of service providers is developing LBS, however, each service has different requirements on accuracy, response time, signaling overhead and number of subscribers that can be localized at the same time. Therefore, the operators are trying to make use of such position location technologies that can bring the best results, also considering the cost. In our study we will present a technique that combines pattern recognition techniques with cellular signaling measurements and more precisely information extracted from Abis/Iub air interfaces in GSM and UMTS networks respectively. The pattern recognition is performed by Hidden Markov Model (HMM) which is trained with downlink prediction data modeling the strength of the received signals for specific areas employing K-means (KM) as the clustering method. The accurate results from a single probe vehicle show the potential of the method when applied to large scale of MTs for vehicle load estimation in main city routes providing in that way Traffic Information Service to mobile phone users. Another important issue is that this technique can be easily integrated in a cellular system and it also fulfils the requirements of a reliable localization technique.

—Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514), 2004

The fluctuations of mobile satellite channels are usually modelled by Markov chains. Existing models postulate the number of states, and their associated distributions based on physical considerations. This produces good models but that are not convenient in different contexts. In this paper, we focus on the methodology of extraction of Hidden Markov Model (HMM) from experimental data to describe the time fluctuations of received power in a mobile satellite service (MSS) context.

2005

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.

2011

In this paper, we consider the problem of tracking fine-grained speeds variations of vehicles using signal strength traces from GSM enabled phones. Existing speed estimation techniques using mobile phone signals can provide longer-term speed averages but cannot track short-term speed variations. Understanding short-term speed variations, however, is important in a variety of traffic engineering applications-for example, it may help distinguish slow speeds due to traffic lights from traffic congestion when collecting real time traffic information. Using mobile phones in such applications is particularly attractive because it can be readily obtained from a large number of vehicles. Our approach is founded on the observation that the large-scale path loss and shadow fading components of signal strength readings (signal profile) obtained from the mobile phone on any given road segment appear similar over multiple trips along the same road segment except for distortions along the time axis due to speed variations. We therefore propose a speed tracking technique that uses a Derivative Dynamic Time Warping (DDTW) algorithm to realign a given signal profile with a known training profile from the same road. The speed tracking technique then translates the warping path (i.e., the degree of stretching and compressing needed for alignment) into an estimated speed trace. Using 6.4 hours of GSM signal strength traces collected from a vehicle, we show that our algorithm can estimate vehicular speed with a median error of ± 5mph compared to using a GPS and can capture significant speed variations on road segments with a precision of 68% and a recall of 84%.

Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 2001

Estimating the velocity of the mobile units is of great importance in hierarchical cellular systems since the satisfactory handover of mobiles to macro or micro-cells depends on its present velocity. In this contribution, the first moment of the instantaneousfrequency (IF) of the received signal, is proposed as a new velocity estimator. The performance of this estimator in the presence of Rayleigh fading, log-normal shadowing, and additive white Gaussian noise is compared with the conventional zero crossing rate (ZCR) estimator. Simulations show that the performance of the proposed estimator is superior to that of the ZCR velocity estimator, in the presence of additive noise. Also it has been shown that the IF velocity estimator is robust in the presence of shadowing.

IEEE Transactions on Mobile Computing, 2005

We propose two algorithms for real-time tracking of the location and dynamic motion of a mobile station in a cellular network using the pilot signal strengths from neighboring base stations. The underlying mobility model is based on a dynamic linear system driven by a discrete command process that determines the mobile station's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. The first algorithm consists of an averaging filter for processing pilot signal, strength measurements and two Kalman filters, one to estimate the discrete command process and the other to estimate the mobility state. The second algorithm employs a single Kalman filter without prefiltering and is able to track a mobile station even when a limited set of pilot signal measurements is available. Both of the proposed tracking algorithms can be used to predict future mobility behavior, which can be, useful in resource allocation applications. Our numerical results show that the proposed tracking algorithms perform accurately over a wide range of mobility parameter values.

IEEE Transactions on Vehicular Technology, 2004

This paper presents a new mobile station velocity estimator based on the first moment of the instantaneous frequency (IF) of the received signal. The effects of shadowing, additive noise, and scattering distribution on the proposed velocity estimator are analyzed. We show that, unlike velocity estimators based on the envelope and quadrature components of the received signal, the proposed estimator is robust to shadowing. We also prove that the performance of the IF-based estimator is only mildly affected by the presence of additive noise. Finally, by using simulations we show that the performance of the proposed IF-based estimator is superior to that of existing velocity estimators.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012

The penetration of GSM capable devices is very high, especially in Europe. To exploit the potential of turning these mobile devices into dynamic data acquisition nodes that provides valuable data for Intelligent Transportation Systems (ITS), position information is needed. The paper describes the basic operation principles of the GSM system and provides an overview on the existing methods for deriving location data in the network. A novel positioning solution is presented that rely on handover (HO) zone measurements; the zone geometry properties are also discussed. A new concept of HO zone sequence recognition is introduced that involves application of Probabilistic Deterministic Finite State Automata (PDFA). Both the potential commercial applications and the use of the derived position data in ITS is discussed for tracking vehicles and monitoring traffic flow. As a practical cutting edge example, the integration possibility of the technology in the SafeTRIP platform (developed in an EC FP7 project) is presented.

In this paper, multiple ground vehicles passing through a region that are observed by audio sensor arrays are efficiently classified using a Hierarchical Hidden Markov Model (HHMM). The states in the HHMM contain another HMM which represents a time sequence of the vehicle acoustic signals. The HMM represents the distribution of the output of the HHMM, where The HMM models the features of the continuous acoustic emissions. The output of the states of this HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). The HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Simulation results demonstrate the efficiency of this scheme.