This paper will introduce a new Multitarget Multi-Bernoulli (MeMBer) recursion for tracking targets traveling
under multiple motion models. The proposed interacting multiple model MeMBer (IMM-MeMBer) filter uses
Jump Markov Models (JMM) to extended the basic MeMBer recursion to allow for multiple motion models. This
extension is implemented using both the SMC and GM based MeMBer approximations. The recursive prediction
and update equations are presented for both implementations. Each multiple model implementation is validated
against its respective standard MeMBer implementation as well as against each other. This validation is done
using a simulated scenario containing multiple maneuvering targets. A variety of metrics are observed including
target detection capability, estimate accuracy and model likelihood determination.
The most popular and well-studied estimation method is the Kalman filter (KF), which was introduced in the
1960s. It yields a statistically optimal solution for linear estimation problems. The smooth variable structure
filter (SVSF) is a relatively new estimation strategy based on sliding mode theory, and has been shown to be
robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary
layer to keep the estimates bounded within a region of the true state trajectory. This article discusses the
application of two estimation strategies (the KF and the SVSF) on a multi-target tracking problem.
The Probability Hypothesis Density (PHD) filter is a powerful new tool in the field of multitarget tracking. Unlike
classical multi-target tracking approaches, such as Multiple Hypothesis Tracking (MHT), in each scan it provides a
complete solution to multi-target state estimation without the necessity for explicit measurement-to-track data
association. The PHD filter recursively propagates the first order moment of the multi-target posterior. This allows us to
determine the expected number of targets as well as their state estimates at each scan. However, there is no implicit
connection between the target state estimates in consecutive scans. In this paper, a new cluster-based approach is
proposed for track labeling in the Sequential Monte Carlo (SMC i.e. particle filter based) PHD filter. The method
associates a likelihood vector to each particle in the SMC estimate. This vector indicates the likelihood that the particle
estimate belongs to each of the established target tracks. This likelihood vector is propagated along with the PHD
moment and updated with the PHD function. By maintaining a set of associations from scan to scan, the new method
provides a complete PHD solution for a multi-target tracking application over time. The method is tested on both clean
and noisy multi-target tracking scenarios and the results are compared to some previously published methods.
In this paper, we study a nonlinear bearing-only target tracking problem using four different
estimation strategies and compare their performances. This study is based on a classical ground
surveillance problem, where a moving airborne platform with a sensor is used to track a moving
target. The tracking scenario is set in two dimensions, with the measurement providing angle
observations. Four nonlinear estimation strategies are used to track the target: the popular
extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new
smooth variable structure filter (SVSF). The SVSF is a predictor-corrector method used for state
and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure
that the estimates converge to true state values. An internal model of the system, either linear or
nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to
calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The
performances of these methods applied on a bearing-only target tracking problem are compared
in terms of estimation accuracy and filter robustness.
This paper describes an empirical approach to characterizing and simulating sea clutter based on the identification
and grouping of so called 'clutter events'. Clutter events are grouped based on azimuth width (or equivalently
existence time). The groups are characterized with regards to mean spike amplitude and relative occurrence
rate of events. The character of the azimuth amplitude profile within a group is further characterized in terms
of associated amplitude probability distribution function (apdf), amplitude profile and variance. The multiparameter
characterization is shown to be sufficiently robust to allow the simulation of a scene that exhibits
not only a qualitative similarity to the real clutter but a demonstrable quantitative correspondence. When the
cumulative distribution function (cdf) of the clutter simulated per the new approach is compared with that of
the real sea clutter returns an excellent match is achieved. Thus the new simulation method is shown to be
consistent with the simpler but widely used apdf characterization.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.