CSIT Group - Research Areas
Bayesian algorithms for radio localization of mobile terminals
Accurate location in
radio systems
has received great attention over the last years. Without exploiting
satellite-aided positioning systems (e.g., GPS), several radio
positioning techniques have been proposed by exploiting only local
radio measurements while transmitting.
These techniques are based on one or more measurements types such as angle (AOA - Angle of Arrival), time (TOA - Time Of Arrival) or time difference (TDOA - Time Difference of Arrival) of arrivals and power profiles (RSS - Received Signal Strength). In a complex network-based radio localization scenario these angular or distances values are not directly measurable, but have to be estimated from a wide set of observed data; typically, those data consist in raw radio signals exchanged between the base anchor nodes or access points (AP) and the mobile terminal (MT). In addition, in real radio systems, false localizations occur due to measurement impairment, calibration errors, multipath effects, inaccurate delay/distance estimations just to name a few.
In order to reduce this bias and alleviate the dense multipath effects, both locality of the MT position and line-of-sight/non-line-of-sight (LOS/NLOS) conditions for all the MT/AP links must be exploited by using a Hidden Markov Model (HMM) framework. Through the use of HMM concept, the hidden state of our dynamic system is characterized by the MT position and by its LOS/NLOS conditions across the radio cell, both modelled as homogeneous first-order Markov chains.
The proposed methods rely on the maximization of an a-posteriori probability of the joint position/sight conditions state for each MT exploiting all the independent signal MT/AP measurements, available up to the current instant. With respect to other algorithms, such as the extended Kalman filter (EKF), the proposed methods does not rely on linearization and Gaussian assumptions still having the same computational complexity. In the proposed algorithms, the computation of state probabilities is carried out on a finite and discrete regular grid of states or, in a more efficient way, by using the Particle Filter approach. According to the Jump Markov System technique, the target probability density function is evaluated only on a limited set of points, or particles, using the concept of importance sampling of the probability space.
These techniques are based on one or more measurements types such as angle (AOA - Angle of Arrival), time (TOA - Time Of Arrival) or time difference (TDOA - Time Difference of Arrival) of arrivals and power profiles (RSS - Received Signal Strength). In a complex network-based radio localization scenario these angular or distances values are not directly measurable, but have to be estimated from a wide set of observed data; typically, those data consist in raw radio signals exchanged between the base anchor nodes or access points (AP) and the mobile terminal (MT). In addition, in real radio systems, false localizations occur due to measurement impairment, calibration errors, multipath effects, inaccurate delay/distance estimations just to name a few.
In order to reduce this bias and alleviate the dense multipath effects, both locality of the MT position and line-of-sight/non-line-of-sight (LOS/NLOS) conditions for all the MT/AP links must be exploited by using a Hidden Markov Model (HMM) framework. Through the use of HMM concept, the hidden state of our dynamic system is characterized by the MT position and by its LOS/NLOS conditions across the radio cell, both modelled as homogeneous first-order Markov chains.
The proposed methods rely on the maximization of an a-posteriori probability of the joint position/sight conditions state for each MT exploiting all the independent signal MT/AP measurements, available up to the current instant. With respect to other algorithms, such as the extended Kalman filter (EKF), the proposed methods does not rely on linearization and Gaussian assumptions still having the same computational complexity. In the proposed algorithms, the computation of state probabilities is carried out on a finite and discrete regular grid of states or, in a more efficient way, by using the Particle Filter approach. According to the Jump Markov System technique, the target probability density function is evaluated only on a limited set of points, or particles, using the concept of importance sampling of the probability space.
Related Publications:
•
Spagnolini
U., Rampa V., Multitarget Detection/Tracking for monostatic Ground
Penetrating Radar: application to pavement profiling, IEEE Transactions
on Geoscience and Remote Sensing, vol 37, N. 1, pp. 383-394, Jan. 1999.
• Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for multidimensional wavefront tracking, Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, vol. 40, N. 3, pp. 651 - 662, March 2002.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for radio localization of moving terminals in LOS/NLOS conditions, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’05), vol. 4, pp. 877-880, 18-23 march 2005.
• Nicoli M., Morelli C., Rampa V., Spagnolini U., HMM-based tracking of moving terminals in dense multipath indoor environments, Proc. 13th European Signal Processing Conference (EUSIPCO '05), Antalya, September 2005.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for radio localization in mixed LOS/NLOS conditions, IEEE Trans. on Signal Processing, Vol. 55, No. 4, pp. 1525-1542, April 2007.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Alippi C., Particle filters for RSS-based localization in wireless sensor networks: An experimental study, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’06), Toulouse, 14-19 Maggio 2006.
• M. Nicoli, C. Morelli, V. Rampa, A jump Markov particle filter for localization of moving terminals in dense multipath indoor scenarios, IEEE Trans. Signal Processing, Vol. 56, N. 8, pp. 3801-3809, August 2008.
• M. Nicoli, V. Rampa, Bayesian Algorithms for Indoor Radio Localization, Ercim News, N. 71, pp. 50-51, Oct. 2007,
http://ercim-news.ercim.eu/images/stories/EN71/EN71-web.pdf
• Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for multidimensional wavefront tracking, Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, vol. 40, N. 3, pp. 651 - 662, March 2002.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for radio localization of moving terminals in LOS/NLOS conditions, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’05), vol. 4, pp. 877-880, 18-23 march 2005.
• Nicoli M., Morelli C., Rampa V., Spagnolini U., HMM-based tracking of moving terminals in dense multipath indoor environments, Proc. 13th European Signal Processing Conference (EUSIPCO '05), Antalya, September 2005.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Hidden Markov Models for radio localization in mixed LOS/NLOS conditions, IEEE Trans. on Signal Processing, Vol. 55, No. 4, pp. 1525-1542, April 2007.
• Morelli C., Nicoli M., Rampa V., Spagnolini U., Alippi C., Particle filters for RSS-based localization in wireless sensor networks: An experimental study, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’06), Toulouse, 14-19 Maggio 2006.
• M. Nicoli, C. Morelli, V. Rampa, A jump Markov particle filter for localization of moving terminals in dense multipath indoor scenarios, IEEE Trans. Signal Processing, Vol. 56, N. 8, pp. 3801-3809, August 2008.
• M. Nicoli, V. Rampa, Bayesian Algorithms for Indoor Radio Localization, Ercim News, N. 71, pp. 50-51, Oct. 2007,
http://ercim-news.ercim.eu/images/stories/EN71/EN71-web.pdf
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