Laser speckle interferometry is used to detect micro-structure and its dynamic behavior of a sample surface by the statistical analysis of its laser speckle images. In this paper, the methodology of laser speckling and statistical analysis are studied with the aims to develop a non-contact, non-destructive surface sensing technique which potentially devices a compact, cost-effective tool for measuring the biological status of a plant by scanning its leaves. First, an auto-correlation based analysis method is proposed for the discrimination of various surface roughness levels using their laser speckle statistics. Second, techniques for dynamic speckle pattern analysis for the detection of the evolution of a time-varying sample surface are discussed. The effectiveness of proposed measurement methods are demonstrated via the experiment on a detached leaf.
In a practical multi-sensor tracking network, the sensor data
processors communicate only a subset of the data available from
each sensor, usually in the form of tracks due to constraints in
the communications bandwidth. This paper investigates source
coding methods by which track information may be communicated with
varying levels of available bandwidth with the aim of minimizing
the number of bits required by the fusion center for a given
estimation error. In particular, we have formulated the
distributed sensor fusion problem subject to network constraints
as a minimization problem in terms of the Kullback-Leibler
distance (KLD). We have shown that for the multi-sensor track
fusion problem, the global KLD will be minimized when the
individual, local KLDs are minimized. The solutions to some
special cases are derived and the losses in the accuracy of the
target state estimates that result from the process of source
coding and subsequent interpretation is also determined. Simulated
results demonstrate the consistency between both theoretical and
In this paper, the Variable Structure Multiple Model(VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design -- the Minimal Sub-Model-Set Switching (MSMSS) algorithm for tracking a maneuvering target is presented. In this algorithm: -- a core model is used to represent the most likely true system mode;
-- edge models are used to represent other possible true system modes based on their connectivity with the core model; -- a sub-model-set is defined by the core and edge models and their transition probabilities as determined from the model transition probability matrix for the full model set. The MSMSS algorithm adaptively determines the minimal sub-model-set of models from the total model set and uses this to perform interacting multiple model (IMM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulation results demonstrate that, compared to a standard IMM, the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.
Proc. SPIE. 5096, Signal Processing, Sensor Fusion, and Target Recognition XII
KEYWORDS: Switching, Detection and tracking algorithms, Data modeling, Matrices, Silicon, Personal digital assistants, Monte Carlo methods, Algorithm development, Performance modeling, Systems modeling
Variable Structure Multiple Model (VSMM) estimation generalizes Multiple Model (MM) estimation by assuming that the set of models used for MM estimation is time varying. By using VSMM estimation, a large model set which may cover all possible target maneuvers can be used without significant increase of computational load, while maintaining a reasonable estimation accuracy. Various implementable VSMM algorithms, like Model Group Switching (MGS), Likely Model Set (LMS) and Minimal Sub-Model-Set Switching (MSMSS) using the Interacting Multiple Model (IMM) algorithm with a sub-model-set adaptation logic have appeared in the recent literature. However, the use of these algorithms for tracking maneuvering target in clutter has not been explored. In presence of clutter, one need to use data association technique to differentiate target originated measurement from clutter. The probabilistic data association (PDA) has been popularly adopted to many algorithms for tracking in clutter. In this paper, we integrate PDA technique with MSMSS and propose a VSIMM-PDA algorithm for maneuvering target tracking in clutter. A new grating technique to account for potential model errors is used. A numeric example via multiple Monte Carlo runs, which compares the performance of the new algorithm to a standard IMM-PDA in terms of Root Mean Squared error (RMS) and percentage of track loss, is presented.