TITLE

Blending Sensor Scheduling Strategy with Particle Filter to Track a Smart Target

AUTHOR(S)
Bin Liu; Chunlin Ji; Yangyang Zhang; Chengpeng Hao
PUB. DATE
November 2009
SOURCE
Wireless Sensor Network;Nov2009, Vol. 1 Issue 4, p300
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the problem of tracking a 'smart' target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kalman filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer's concealment.
ACCESSION #
45453351

 

Related Articles

  • FEATURE AIDED SWITCHING MODEL SET APPROACH FOR MANEUVERING TARGET TRACKING. Fan, J. P.; Zhu, Y. L.; Fan, S. J.; Fan, H. Q.; Fu, Q. // Progress in Electromagnetics Research B;2012, Vol. 45, p251 

    Feature aided maneuver detector is popular for its low detection delay and high detection probability in decision-based single-model maneuvering target tracking (MTT) algorithms. We propose a switching model-set approach based on the feature aided maneuver detector for MTT. The filtering error...

  • Time-Expanded Sampling for Ensemble Kalman Filter: Assimilation Experiments with Simulated Radar Observations. Qin Xu; Huijuan Lu; Shouting Gao; Ming Xue; Mingjing Tong // Monthly Weather Review;Jul2008, Vol. 136 Issue 7, p2651 

    A time-expanded sampling approach is proposed for the ensemble Kalman filter (EnKF). This approach samples a series of perturbed state vectors from each prediction run not only at the analysis time (as the conventional approach does) but also at other time levels in the vicinity of the analysis...

  • LMMSE Estimation Based on Counting Observations. Fernández-Alcalá, Rosa; Navarro-Moreno, Jesús; Ruiz-Molina, Juan Carlos; Oya, Antonia // International Journal of Applied Mathematics;2007, Vol. 37 Issue 2, p145 

    The problem of estimating the intensity process of a doubly stochastic Poisson process is analyzed. Using the knowledge of the first and second-order moments of the intensity process, a recursive linear minimum mean-square error estimate is designed. Moreover, an efficient procedure for the...

  • New Investigative Findings from the Debiased Converted-Measurement Kalman Filter. Spitzmiller, John N.; Adhami, Reza R. // Intelligent Information Management;Jul2010, Vol. 2 Issue 7, p431 

    The original algorithm for the 2-D debiased converted-measurement Kalman filter (CMKF) specified, with incorrect mathematical justification, a requirement for evaluating the average true bias and covariance with the best available polar estimate, rather than exclusively with the polar...

  • Tracking with Estimate-Conditioned Debiased 2-D Converted Measurements. Spitzmiller, John N.; Adhami, Reza R. // Engineering;Apr2010, Vol. 2 Issue 4, p286 

    This paper describes a new algorithm for the 2-D converted-measurement Kalman filter (CMKF) which estimates a target's Cartesian state given polar position measurements. At each processing index, the new algorithm chooses the more accurate of (1) the sensor's polar position measurement and (2)...

  • Generalized varying coefficient models with unknown link function. Kuruwita, C. N.; Kulasekera, K. B.; Gallagher, C. M. // Biometrika;Sep2011, Vol. 98 Issue 3, p701 

    We propose a new estimation method for generalized varying coefficient models where the link function is specified up to some smoothness conditions. Consistency and asymptotic normality of the estimated varying coefficient functions are established. Simulation results and a real data application...

  • THREE AXIS ROTATION MEASUREMENTS WITH KALMAN FILTER DATA-FUSION. SKULA, David; VESELY, Milos // Annals of DAAAM & Proceedings;Jan2009, p1079 

    This paper deals with measuring the angle of rotation in three mutually perpendicular axes. Sensors of angular velocity, linear acceleration and magnetic field are used for this purpose. Each of these sensors has advantages and disadvantages. Therefore a data fusion (Kalman filter) from all...

  • THE USE OF THE VARIOGRAM IN CONSTRUCTION OF STATIONARY TIME SERIES MODELS. Chunsheng Ma // Journal of Applied Probability;Dec2004, Vol. 41 Issue 4, p1093 

    This paper studies a class of stationary covariance models, in both the discrete- and the continuous-time domains, which possess a simple functional form ?(t +to)+ ?(t - to) - 2? (t), where t0 is a fixed lag and ? (t) is an intrinsically stationary variogram, and include the fractional Gaussian...

  • ON A TIME DEFORMATION REDUCING NONSTATIONARY STOCHASTIC PROCESSES TO LOCAL STATIONARITY.  // Journal of Applied Probability;Mar2004, Vol. 41 Issue 1, p236 

    A stochastic process is locally stationary if its covariance function can be expressed as the product of a positive function multiplied by a stationary covariance. In this paper, we characterize nonstationary stochastic processes that can be reduced to local stationarity via a objective...

Share

Read the Article

Courtesy of VIRGINIA BEACH PUBLIC LIBRARY AND SYSTEM

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics