Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

High-level Information Fusion for Constrained SMC Methods and Applications

Information Fusion is a field that studies processes utilizing data from various input sources, and techniques exploiting this data to produce estimates and knowledge about objects and situations. On the other hand, human computation is a new and evolving research area that uses human intelligence to solve computational problems that are beyond the scope of existing artificial intelligence algorithms. In previous systems, humans' role was mostly restricted for analysing a finished fusion product; however, in the current systems the role of humans is an integral element in a distributed framework, where many tasks can be accomplished by either humans or machines. Moreover, some information can be provided only by humans not machines, because the observational capabilities and opportunities for traditional electronic (hard) sensors are limited. A source-reliability-adaptive distributed non-linear estimation method applicable to a number of distributed state estimation problems is proposed. The proposed method requires only local data exchange among neighbouring sensor nodes. It therefore provides enhanced reliability, scalability, and ease of deployment. In particular, by taking into account the estimation reliability of each sensor node at any point in time, it yields a more robust distributed estimation. To perform the Multi-Model Particle Filtering (MMPF) in an adaptive distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node, along with a weighted Consensus Propagation (CP)-based distributed data aggregation scheme, are deployed to dynamically re-weight the particle clouds. The filtering is a soft-data-constrained variant of multi-model particle filter, and is capable of processing both soft human-generated data and conventional hard sensory data. If permanent noise occurs in the estimation provided by a sensor node, due to either a faulty sensing device or misleading soft data, the contribution of that node in the weighted consensus process is immediately reduced in order to alleviate its effect on the estimation provided by the neighbouring nodes and the entire network. The robustness of the proposed source-reliability-adaptive distributed estimation method is demonstrated through simulation results for agile target tracking scenarios. Agility here refers to cases in which the observed dynamics of targets deviate from the given probabilistic characterization. Furthermore, the same concept is applied to model soft data constrained multiple-model Probability Hypothesis Density (PHD) filter that can track agile multiple targets with non-linear dynamics, which is a challenging problem. In this case, a Sequential Monte Carlo-Probability Hypothesis Density (SMC-PHD) filter deploys a Random Set (RS) theoretic formulation, along with Sequential Monte Carlo approximation, a variant of Bayes filtering. In general, the performance of Bayesian filtering-based methods can be enhanced by using extra information incorporated as specific constraints into the filtering process. Following the same principle, the new approach uses a constrained variant of the SMC-PHD filter, in which a fuzzy logic approach is used to transform the inherently vague human-generated data into a set of constraints. These constraints are then enforced on the filtering process by applying them as coefficients to the particles' weights. Because the human generated Soft Data (SD), reports on target-agility level, the proposed constrained-filtering approach is capable of dealing with multiple agile target tracking scenarios

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity