Lang Hong

Multiresolutional distributed filtering

An algorithm for optimal and dynamic multiresolutional distributed filtering is derived. The wavelet transform is utilized as a bridge linking signals at different resolution levels. The algorithm can be employed for dynamic multisensor/data fusion. >

A comparison of nonlinear filtering approaches with an application to ground target tracking

With an application to ground target tracking, two groups of nonlinear filtering approaches are compared in this paper: Gaussian approximation and Monte Carlo simulation. The former group, consisting of the extended Kalman filter (EKF), Gauss-Hermite filter (GHF) and unscented Kalman filter (UKF), approximates probability densities of nonlinear systems using either single or multiple points in a state space, while the latter group, being particle filters, estimates probability densities using random samples. There are two sources contributing to nonlinearity in the ground target tracking problem: terrain and road constrained kinematic modeling and polar coordinate sensing. When tracking ground maneuvering targets with multiple models, one faces another problem, i.e., non-Gaussianity. This paper also compares interacting multiple model (IMM)-based filters IMM-EKF, IMM-GHF and IMM-UKF with particle-based multiple model filters for their capability in handling the non-Gaussian problem. Simulation results show that: (1) all the filters achieve a comparable performance when tracking non-maneuvering ground targets; (2) particle-based multiple model filters are superior to IMM-based filters in maneuvering ground target tracking.

Multiresolutional multiple-model target tracking

A multiresolutional multiple-model target tracking algorithm is developed which uses the wavelet transform as a means for mapping data between different resolution levels. The algorithm is effective for tracking maneuvering targets given measurements with a low S/N ratio. >

Multiresolutional filtering using wavelet transform

An algorithm for optimal and dynamic multiresolutional distributed filtering is derived. The wavelet transform is utilized as a bridge linking signals at different resolution levels. The algorithm can be employed for dynamic multisensor/data fusion. >

Data association through fusion of target track and identification sets

A joint probability data association tracking algorithm typically associates only position measurements. With multiple-interacting targets in the presence of clutter, data association can be confused by spurious measurements. In this paper, we propose a set-based track and identification data association (SBDA) technique to leverage object identification information. We investigate the SBDA technique for a scenario in which a tracker has access to both coarse position measurements and belief identification information to enhance data association.

An interacting multi-pattern probabilistic data association (IMP-PDA) algorithm for target tracking

A theoretical development of a novel approach for target tracking based on multiple patterns extracted from measurement sequences is presented in this paper. The introduction of patterns leads to a new paradigm for developing high performance algorithms. An interacting multi-pattern probabilistic data association (IMP-PDA) algorithm is developed, taking the advantage of clever formulation of the interacting multiple model approach. The IMP-PDA algorithm employs distance, directional and maneuver information for data association, which enhances significantly the capability of discriminating correct measurements from false measurements.

Adaptive distributed filtering in multicoordinated systems

The direct estimation of optimal steady-state gain in the single filtering process introduced by B. Carew et al. (1973) is extended to multicoordinated systems, and the distributed optimal steady-state gains are directly estimated for adaptive distributed filtering. The correlation method using distributed innovation processes is used. The algorithm assumes little prior information about the unknown covariances and adaptively changes the weights to best integrate the distributed estimates obtained in local filtering processes. The term best is used in the sense that the result of the adaptive distributed filtering is as close to that of the optimal distributed filtering as possible. >

A genetic algorithm based multi-dimensional data association algorithm for multi-sensor--multi-target tracking

The central problem in multitarget-multisensor tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of true tracks can be found. The data association problem is formed as an N-dimensional (N-D) assignment problem, which is a state-of-the-art method and is NP-hard for N >= 3 sensor scans. This paper proposes a new genetic algorithm for solving the above problem which is typically encountered in the application of target tracking. The data association capacities of the genetic algorithm have been studied in different environments, and the results are presented.

Recursive temporal-spatial information fusion with applications to target identification

Centralized/distributed recursive algorithms for temporal-spatial information integration using the Dempster-Shafer technique are developed. Compared with the Bayesian approach, the Dempster-Shafer technique has the strong capability of handling information uncertainties, which are particularly desirable in many applications. In the centralized integration algorithm, all information is pooled into the central processor and integrated. In contrast, the distributed integration algorithm shares the computational burden among the local processors, which increases the computational efficiency. The developed algorithms are effectively applied to a target identification problem with three sensors: identification of friend-foe-neutral (IFFN), electronic support measurement (ESM), and infrared search and track (IRST). >

Centralized and distributed multisensor integration with uncertainties in communication networks

Algorithms in which each sensor is represented in a local coordinate system and the communication networks between sensors have uncertainties are considered. The algorithms are general and can be applied to various integration tasks. The effects of the communication network uncertainties are minimized in the local estimation and central fusion processes. In the centralized multisensor integration, the local measurements and local measurement models are transferred to the central coordinate system and the optimal integration is obtained at the central process. In contrast, the local measurements, together with the previous central estimate transmitted from the communication network, are locally processed in the distributed multisensor integration algorithm. Because the distributed algorithm uses the communication networks twice, more errors are introduced, so that when the uncertainties are large, the centralized algorithm is preferred. Although the algorithms are developed in the three-dimensional coordinate system, with straightforward extension they can be applied to N-dimensional coordinate systems. >