Mohammadreza Alimadadi
ABSTRACT
We address the problem of object tracking in an underwater acoustic sensor network in which distributed nodes measure the strength of field generated by moving objects, encode the measurements into digital data packets, and transmit the packets to a fusion center in a random access manner. We allow for imperfect communication links, where information packets may be lost due to noise and collisions. The packets that are received correctly are used to estimate the objects' trajectories by employing an extended Kalman Filter, where provisions are made to accommodate a randomly changing number of obseravtions in each iteration. An adaptive rate control scheme is additionally applied to instruct the sensor nodes on how to adjust their transmission rate so as to improve the location estimation accuracy and the energy efficiency of the system. By focusing explicitly on the objects' locations, rather than working with a pre-specified grid of potential locations, we resolve the spatial quantization issues associated with sparse identification methods. Finally, we extend the method to address the possibility of objects entering and departing the observation area, thus improving the scalability of the system and relaxing the requirement for accurate knowledge of the objects' initial locations. Performance is analyzed in terms of the mean-squared localization error and the trade-offs imposed by the limited communication bandwidth.
- I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow, and P. Polakos, "Wireless sensor network virtualization: A survey," IEEE Commun. Surveys Tuts, vol. 18, no. 1, pp. 553--576, 2016.Google Scholar
Digital Library
- U. Mitra, S. Choudhary, F. Hover, R. Hummel, N. Kumar, S. Naryanan, M. Stojanovic, and G. Sukhatme, "Structured sparse methods for active ocean observation systems with communication constraints," IEEE Commun. Mag., vol. 53, no. 11, pp. 88--96, 2015.Google ScholarDigital Library
- J. Heidemann, M. Stojanovic, and M. Zorzi, "Underwater sensor networks: applications, advances and challenges," Phil. Trans. R. Soc. A, vol. 370, no. 1958, pp. 158--175, 2012.Google ScholarCross Ref
- K. Kerse, F. Fazel, and M. Stojanovic, "Target localization and tracking in a random access sensor network," in Sig., Syst. and Comput., 2013 Asilomar Conf. on. IEEE, 2013, pp. 103--107.Google Scholar
- M. A. Kafi, J. B. Othman, and N. Badache, "A survey on reliability protocols in wireless sensor networks," ACM Comput. Surveys (CSUR), vol. 50, no. 2, p. 31, 2017. Google ScholarDigital Library
- J. Lin, W. Xiao, F. L. Lewis, and L. Xie, "Energy-efficient distributed adaptive multisensor scheduling for target tracking in wireless sensor networks," IEEE Trans. Instrum. Meas., vol. 58, no. 6, pp. 1886--1896, 2009.Google ScholarCross Ref
- A. L. Rodriguez and M. Stojanovic, "Adaptive object tracking in a sensor network," in OCEANS 2015-Genova. IEEE, 2015, pp. 1--5.Google Scholar
- F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P.-J. Nordlund, "Particle filters for positioning, navigation, and tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 425--437, 2002. Google ScholarDigital Library
- P. Closas and M. F. Bugallo, "Improving accuracy by iterated multiple particle filtering," IEEE Signal Process. Lett., vol. 19, no. 8, pp. 531--534, 2012.Google ScholarCross Ref
- F. Fazel, M. Fazel, and M. Stojanovic, "Random access compressed sensing over fading and noisy communication channels," IEEE Trans. Wireless Commun., vol. 12, no. 5, pp. 2114--2125, 2013.Google ScholarCross Ref
Recommendations
Object Tracking Based on Genetic Algorithm and Kalman Filter
CIS '08: Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 01Robust object tracking is quite important in computer vision. In this paper, a novel tracking approach for single object which combines genetic algorithm and Kalman filter is proposed. Genetic algorithm is introduced and reasonably applied to find the ...
Simplification of Unscented Kalman Filter for Orbit Object Tracking
ITCS '10: Proceedings of the 2010 Second International Conference on Information Technology and Computer ScienceAim at the application of orbit object tracking; we adopt certain simplification technology to UKF (Unscented Kalman Filter), which reduce the computational complexity considerably. The state space equation in orbit object tracking is linear; the sigma ...
Object tracking method based on data computing
The data computing process is utilized in various areas such as autonomous driving. Autonomous vehicles are intended to detect and track nearby moving objects avoiding collisions and to navigate in complex situations, such as heavy traffic and dense ...
Comments