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Risk-sensitive filter is a robust and numerically efficient algorithm compared to risk neutral filter with model uncertainties. For nonlinear plant, the square root unscented Kalman risk-sensitive filter (SUKRSF) is proposed in this paper by using unscented transformation approximation. Square root unscented Kalman filter (SRUKF), a derivative-free nonlinear estimation tool is used to solve risk-sensitive...
The reception state of a satellite is an unavailable information for Global Navigation Satellite System receivers. His knowledge or estimation can be used to evaluate the pseudorange error. This article deals with the problem using three reception states: direct reception, alternate reception and blocked situation. This parameter, estimated using a Dirichlet distribution, is included in a particle...
This paper addresses the problem of state estimation with quantized measurements. In a system with quantized measurements, due to the nonlinearity of the quantizer, estimating the system state is a nonlinear and non-Gaussian estimation problem even if the system is linear and Gaussian. A numerical algorithm for approximate minimum mean square error (MMSE) state estimation with quantized measurement...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from the missed detection. In the case of zero...
The theoretic fundamentals of distributed information fusion are well developed. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication...
Tracking algorithms are often designed around optimistic assumptions on uncertainty model. Handling with conflicting data, however, requires specific strategies, that consider quality of information sources. To improve performance of tracking systems, the use of reliability, as evaluation of quality of data sources, has been proved to be a promising technique. In this paper we show how to use reliability...
As a state-of-the-art algorithm, the interacting multiple model (IMM) estimator is widely used for maneuvering target tracking. However, there still is uncertainty in how to design the multiple models used by an IMM estimator. In this context, this paper compares three maneuver models, namely, variable process noise, variable state dimension, and discrete acceleration inputs via computer simulations...
The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Extended objects may give rise to more than one detection per opportunity where the scattering centers may vary from scan to scan. On the other end, group targets (i. e., a number of closely spaced targets moving in a coordinated fashion) often will not...
In this paper, a two-tier hierarchical architecture is proposed to address the multi-target tracking problem using a particle probability hypothesis density filtering algorithm. According to a proposed cluster scheduling method, the base station selects active clusters at each time step and determines their order for the sequential data fusion in the second level of hierarchy. Within each active cluster,...
This paper presents Monte Carlo (MC) methods for multi-target tracking and data association. We focus on comparing different estimation methods based on joint and non-joint state particle filters (PF) and joint probabilistic data association (JPDA) techniques. A novel data association algorithm for PF, founded on a combination of PDA and nearest neighbour (NN) techniques, is also developed. In this...
The problem of tracking targets, where measurements may occasionally be masked by the Doppler blind zone of the sensor, arises in Ground Moving Target Indicator tracking and aerial surveillance. For such problems, no target return is registered when the range rate (Doppler) of the target falls below a sensor-specific threshold in magnitude. For this reason, possible missed detections provide kinematic...
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