On track fusion with communication constraints
暂无分享,去创建一个
[1] X.R. Li,et al. Optimal data compression for multisensor target tracking with communication constraints , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).
[2] Peter Willett,et al. Some approaches to quantization for distributed estimation with data association , 2005, IEEE Transactions on Signal Processing.
[3] Chongzhao Han,et al. Optimal linear estimation fusion .I. Unified fusion rules , 2003, IEEE Trans. Inf. Theory.
[4] Thiagalingam Kirubarajan,et al. Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion] , 2003 .
[5] J.E. Gray,et al. Theory of distributed estimation using multiple asynchronous sensors , 2005, IEEE Transactions on Aerospace and Electronic Systems.
[6] Ghassan Al-Regib,et al. Rate-Constrained Distributed Estimation in Wireless Sensor Networks , 2006, IEEE Transactions on Signal Processing.
[7] Oliver E. Drummond,et al. Track fusion with feedback , 1996, Defense, Security, and Sensing.
[8] Thia Kirubarajan,et al. Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .
[9] Zhi-Quan Luo,et al. Performance Bounds for the Rate-Constrained Universal Decentralized Estimators , 2007, IEEE Signal Processing Letters.
[10] G.B. Giannakis,et al. Distributed compression-estimation using wireless sensor networks , 2006, IEEE Signal Processing Magazine.
[11] Toby Berger,et al. The quadratic Gaussian CEO problem , 1997, IEEE Trans. Inf. Theory.
[12] Stergios I. Roumeliotis,et al. SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using The Sign Of Innovations , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[13] Haralabos C. Papadopoulos,et al. Sequential signal encoding from noisy measurements using quantizers with dynamic bias control , 2001, IEEE Trans. Inf. Theory.
[14] X.R. Li,et al. Optimal sensor data quantization for best linear unbiased estimation fusion , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).
[15] Zhi Tian,et al. MAP track fusion performance evaluation , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).
[16] T. Kirubarajan,et al. Performance Limits of Track-to-Track Fusion vs . Centralized Estimation : Theory and Application , 2001 .
[17] Kuo-Chu Chang,et al. Architectures and algorithms for track association and fusion , 2000 .
[18] Oliver E. Drummond,et al. Performance assessment and comparison of various tracklet methods for maneuvering targets , 2003, SPIE Defense + Commercial Sensing.
[19] X. Rong Li. Optimal linear estimation fusion-part VII: dynamic systems , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[20] Oliver E. Drummond,et al. Hybrid sensor fusion algorithm architecture and tracklets , 1997, Optics & Photonics.
[21] Sergio D. Servetto,et al. Lattice Quantization With Side Information: Codes, Asymptotics, and Applications in Sensor Networks , 2006, IEEE Transactions on Information Theory.
[22] Aaron D. Wyner,et al. The rate-distortion function for source coding with side information at the decoder , 1976, IEEE Trans. Inf. Theory.
[23] Chongzhao Han,et al. Optimal Linear Estimation Fusion — Part I : Unified Fusion Rules , 2001 .
[24] Yasutada Oohama,et al. The Rate-Distortion Function for the Quadratic Gaussian CEO Problem , 1998, IEEE Trans. Inf. Theory.