Ondrej Hlinka

Distributed particle filtering in agent networks: A survey, classification, and comparison

Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.

Likelihood Consensus and Its Application to Distributed Particle Filtering

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task-based on the past and current measurements of all sensors-using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This “likelihood consensus” method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem.

Distributed Gaussian particle filtering using likelihood consensus

We propose a distributed implementation of the Gaussian particle filter (GPF) for use in a wireless sensor network. Each sensor runs a local GPF that computes a global state estimate. The updating of the particle weights at each sensor uses the joint likelihood function, which is calculated in a distributed way, using only local communications, via the recently proposed likelihood consensus scheme. A significant reduction of the number of particles can be achieved by means of another consensus algorithm. The performance of the proposed distributed GPF is demonstrated for a target tracking problem.

Likelihood consensus: Principles and application to distributed particle filtering

We propose a distributed method for computing the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. A consensus algorithm is used for a decentralized, iterative calculation of a sufficient statistic that describes an approximation to the JLF. After convergence of the consensus algorithm, the approximate JLF—which epitomizes the measurements of all sensors—is available at each sensor. This “likelihood consensus” method requires only communications between neighboring sensors. We implement the likelihood consensus method in a distributed particle filtering scheme. Each sensor runs a local particle filter that computes a global state estimate. The updating of the particle weights of each local particle filter uses the JLF. The performance of this distributed particle filter is demonstrated on a target tracking problem.

Sigma Point Belief Propagation

The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.

Simultaneous distributed sensor self-localization and target tracking using belief propagation and likelihood consensus

We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements. Starting from a factor graph formulation of the CoSLAT problem, we develop a particle-based, distributed message passing algorithm for CoSLAT that combines nonparametric belief propagation with the likelihood consensus scheme. The proposed CoSLAT algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging probabilistic information between CSL and DTT. Simulation results demonstrate substantial improvements in both self-localization and tracking performance.

Distributed Localization and Tracking of Mobile Networks Including Noncooperative Objects

We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between agents and objects and between agents. For a distributed operation and low complexity, we combine particle-based belief propagation with a consensus or gossip scheme. High localization accuracy is achieved through a probabilistic information transfer between the CS and DT parts of the underlying factor graph. Simulation results demonstrate significant improvements in both agent self-localization and object localization performance compared to separate CS and DT, and very good scaling properties with respect to the numbers of agents and objects.

Consensus-based Distributed Particle Filtering With Distributed Proposal Adaptation

We develop a distributed particle filter for sequential estimation of a global state in a decentralized wireless sensor network. A global state estimate that takes into account the measurements of all sensors is computed in a distributed manner, using only local calculations at the individual sensors and local communication between neighboring sensors. The paper presents two main contributions. First, the likelihood consensus scheme for distributed calculation of the joint likelihood function (used by the local particle filters) is generalized to arbitrary local likelihood functions. This generalization overcomes the restriction to exponential-family likelihood functions that limited the applicability of the original likelihood consensus (Hlinka et al., “Likelihood consensus and its application to distributed particle filtering,” IEEE Trans. Signal Process., vol. 60, pp. 4334-4349, Aug. 2012). The second contribution is a consensus-based distributed method for adapting the proposal densities used by the local particle filters. This adaptation takes into account the measurements of all sensors, and it can yield a significant performance improvement or, alternatively, a significant reduction of the number of particles required for a given level of accuracy. The performance of the proposed distributed particle filter is demonstrated for a target tracking problem.

Time-space-sequential distributed particle filtering with low-rate communications

We present a distributed particle filtering scheme for time-space-sequential Bayesian state estimation in wireless sensor networks. Low-rate inter-sensor communications between neighboring sensors are achieved by transmitting Gaussian mixture (GM) representations instead of particles. The GM representations are calculated using a clustering algorithm. We also propose a ¿look-ahead¿ technique for designing the proposal density used for importance sampling. Simulation results for a target tracking application demonstrate the performance of our distributed particle filter and, specifically, the advantage of the look-ahead proposal design over a conventional design.

Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation

We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.