Kalman filtered Compressed Sensing

@article{Vaswani2008KalmanFC,
  title={Kalman filtered Compressed Sensing},
  author={N. Vaswani},
  journal={2008 15th IEEE International Conference on Image Processing},
  year={2008},
  pages={893-896}
}
  • N. Vaswani
  • Published 2008
  • Mathematics, Computer Science
  • 2008 15th IEEE International Conference on Image Processing
  • We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incoherent" measurements, in real-time. The signals are sparse in some transform domain referred to as the sparsity basis. For a single spatial signal, the solution is provided by Compressed Sensing (CS). The question that we address is, for a sequence of sparse signals, can we do better than CS, if (a) the sparsity pattern of… CONTINUE READING
    292 Citations

    Figures and Topics from this paper.

    Analyzing Least Squares and Kalman Filtered Compressed Sensing
    • N. Vaswani
    • Mathematics, Computer Science
    • 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
    • 2009
    • 37
    • PDF
    Compressed sensing of time-varying signals
    • 103
    Kalman filtered compressive sensing with intermittent observations
    • 4
    LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual
    • N. Vaswani
    • Mathematics, Computer Science
    • IEEE Transactions on Signal Processing
    • 2010
    • 142
    • PDF
    A Simple Method for Sparse Signal Recovery from Noisy Observations Using Kalman Filtering
    • 14
    • Highly Influenced
    • PDF
    Tracking dynamic sparse signals using Hierarchical Bayesian Kalman filters
    • 39
    • PDF
    Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing
    • 139
    • Highly Influenced
    • PDF
    Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms
    • 126
    • Highly Influenced
    Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors
    • 1

    References

    SHOWING 1-10 OF 21 REFERENCES
    Bayesian Compressive Sensing
    • 1,774
    • PDF
    Compressed Sensing Image Reconstruction Via Recursive Spatially Adaptive Filtering
    • 142
    • PDF
    Exploiting Prior Knowledge in The Recovery of Signals from Noisy Random Projections
    • 26
    Compressed sensing in dynamic MRI
    • 504
    Sparse MRI: The application of compressed sensing for rapid MR imaging
    • 4,929
    • PDF
    Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
    • 13,505
    • PDF
    Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit
    • 891
    • PDF
    Particle Filters for Infinite (or Large) Dimensional State Spaces-Part 2
    • 19
    • PDF
    An Architecture for Compressive Imaging
    • 335
    • PDF
    The Dantzig selector: Statistical estimation when P is much larger than n
    • 3,010
    • PDF