We demonstrate burst-mode operation of a polarization-maintaining Yb-doped fiber amplifier capable of generating 60 μJ pulses within bursts of 11 pulses with extremely uniform energy distribution facilitated by a novel feedback mechanism shaping the seed of the burst-mode amplifier. The burst energy can be scaled up to 1 mJ, comprising 25 pulses with 40 μJ average individual energy. The amplifier is synchronously pulse pumped to minimize amplified spontaneous emission between the bursts. Pulse propagation is entirely in fiber and fiber-integrated components until the grating compressor, which allows for highly robust operation. The burst repetition rate is set to 1 kHz and spacing between individual pulses is 10 ns. The 40 μJ pulses are externally compressible to a full width at half-maximum of 600 fs. However, due to the substantial pedestal of the compressed pulses, the effective pulse duration is longer, estimated to be 1.2 ps.

Extended target tracking (ETT) is an issue in high resolution radar surveillance, ship tracking, and video tracking. Most previous works focus on tracking an ellipsoidal extended target without measurement origin uncertainty (missed detections and clutter). In this paper, a new estimator called the Gaussian process probabilistic data association (GP-PDA) filter is proposed to track an irregularly shaped extended target with measurement origin uncertainty. First, a generalized measurement model for ETT using Gaussian process (GP) is presented. Both the interior scattering points and the external clutter are considered in this model. Second, a GP-based gating technique is constructed to select validated measurements to feed the filter. Third, the GPPDA filter is proposed to simultaneously estimate the kinematic state and the contour state of the extended target with measurement origin uncertainty. It is proven that the GP-PDA is a generalized version of the classic PDA, and the latter is a special case of the former in the point target tracking applications. Finally, the GP-based posterior Cramer-Rao lower bound (PCRLB) is derived to evaluate performance of the ETT with measurement origin uncertainty. Two cases of the PCRLB are discussed: with the number of scattering points being known and unknown. Simulation results verify the effectiveness of the proposed method.

In this paper the ingestion and subsequent metabolism of a drug in a given individual, are investigated through the use of compartmental models. An adaptive formulation of the widely used extended Kalman filter (EKF) has been derived in order to solve the resulting nonlinear estimation problem at the output of the compartments. The adaptive EKF employs a forgetting factor to emphasize artificially the effect of current data by exponentially weighting the observations. The dependence of EKF's performance on the selection of appropriate values of the arbitrary matrices, the measurement covariance R and the process noise covariance Q has been demonstrated through simulations. With the appropriate choice of the matrices the EKF provides a very useful tool for online estimation of both the state and the parameters.

Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented. The concept of superposition is introduced and applied to the well-known Joint Probabilistic Data Association (JPDA) filter to derive the JPDA with superposition (JPDAS) filter. The JPDAS intensity function is evaluated to machine precision “for free” by computing the generating functional of the posterior process using complex arithmetic. A simulated example with eight targets is presented.

In this note, stability of the projection-based constrained discrete time extended Kalman filter (EKF) when applied to deterministic nonlinear systems has been studied. It is proved that, like the unconstrained case, under certain assumptions, the EKF with state equality constraints is an exponential observer, i.e., it keeps the dynamics of its estimation error exponentially stable. Also, it has been shown that a simple modification to the general definition of the EKF with exponential weighting increases the filter's degree of stability and convergence speed with or without state constraints.