Stiven Schwanz Dias

Cooperative Target Tracking Using Decentralized Particle Filtering and RSS Sensors

This paper introduces new cooperative particle filter algorithms for tracking emitters using received-signal strength (RSS) measurements. In the studied scenario, multiple RSS sensors passively observe different attenuated and noisy versions of the same signal originating from a moving emitter and cooperate to estimate the emitter state. Assuming unknown sensor noise variances, we derive an exact decentralized implementation of the centralized particle filter solution for this problem in a fully connected network. Next, assuming only local internode communication, we introduce two fully distributed consensus-based solutions to the cooperative tracking problem using respectively average consensus iterations and a novel ordered minimum consensus approach. In the latter case, we are able to reproduce the exact centralized solution in a finite number of consensus iterations. To further reduce the communication cost, we derive in the sequel a new suboptimal algorithm which employs suitable parametric approximations to summarize messages that are broadcast over the network. Numerical simulations with small-scale networks show that the proposed approximation leads to a modest degradation in performance, but with much lower communication overhead. Finally, we introduce a second alternative low communication cost algorithm based on random information dissemination.

Cooperative particle filtering for emitter tracking with unknown noise variance

We introduce in this paper a novel cooperative particle filter algorithm for tracking a moving emitter using received-signal strength (RSS) measurements with unknown observation noise variance. In the studied scenario, multiple RSS sensors passively observe independently attenuated and perturbed versions of the same broadcast signal transmitted by an emitter which is moving through the sensor field and cooperate to estimate the emitter state. The new algorithm differs from previous methods by employing a parametric approximation to reduce the associated communication burden.

Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering

We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.

A Rao-Blackwellized Random Exchange Diffusion Particle Filter for distributed emitter tracking

We introduce in this paper the fully distributed, Rao-Blackwellized Random Exchange Diffusion Particle Filter (RB ReDif-PF) to track a moving emitter using multiple received-signal-strength (RSS) sensors with unknown noise variances. In a simulated scenario with a partially connected network, the proposed RB ReDif-PF outperformed a suboptimal tracker that assimilates local neighboring measurements only. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker, ReDif-PF showed a degradation in steady-state error performance. However, compared to alternative fully distributed consensus-based trackers in the literature, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurements arrivals.

Joint emitter detection and tracking using distributed Random Exchange Diffusion Particle Filtering

We introduce in this paper a new fully distributed particle filter (PF) algorithm based on random information diffusion that is capable of performing joint multi-frame detection and tracking of a single moving emitter using a cooperative network of multiple received-signal-strength (RSS) sensors. Unlike previous consensus-based distributed PF schemes, the proposed Random Exchange Diffusion Particle Filter (ReDif-PF) does not require multiple iterative inter-node communication in the time interval between the arrival of two consecutive sensor measurements. Inter-node communication cost is further reduced by suitable parametric approximations.

A hybrid GMM/SMC diffusion Bernoulli Filter for joint distributed detection and tracking

We introduce in this paper the Random Exchange Diffusion Bernoulli Filter (RndEx-BF), which enables joint target detection and tracking by a network of collaborative sensors. RndEx-BF is a fully distributed algorithm that, unlike consensus-based solutions, does not require iterative internode communication between sensor measurements. Internode communication cost is further reduced by a novel hybrid GMM/SMC implementation of the proposed filter. Experimental results show that RndEx-BF approaches the performance of a flooding-based implementation of the optimal centralized Bernoulli filter with much lower bandwidth requirements.

Performance bounds for cooperative RSS emitter tracking using diffusion particle filters

This paper introduces a methodology for numerical computation of the Posterior Cramér-Rao Lower Bound (PCRLB) for the position estimate mean-square error when a moving emitter is tracked by a network of received-signal-strength (RSS) sensors using a distributed, random exchange diffusion filter. The square root of the PCRLB is compared to the empirical root-mean-square error curve for a particle filter implementation of the diffusion filter, referred to as RndEx-PF, and to the square root of the PCRLB for the optimal centralized filter that assimilates all network measurements at each time instant. In addition, we also compare the proposed RndEx-PF algorithm to three alternative distributed trackers based on Kullback-Leibler fusion using both iterative consensus and non-iterative diffusion strategies.

Distributed Bernoulli Filters for Joint Detection and Tracking in Sensor Networks

We introduce in this paper a novel, fully distributed diffusion Bernoulli filter based on random information dissemination over a partially connected sensor network. The proposed algorithm allows the network nodes to cooperatively perform joint multiframe detection and tracking of an emitter that randomly appears in and disappears from a surveillance region. In addition, we also introduce an alternative flooding-based algorithm that reproduces exactly, in a fully distributed fashion, the optimal centralized Bernoulli filter based on all network node measurements. We derive two alternative low bandwidth implementations of the proposed filters, using respectively a marginalized sequential Monte Carlo (SMC) method and a hybrid Gaussian mixture model (GMM)/SMM scheme. The algorithms were evaluated in a simulated scenario where network nodes directly assimilate raw received signal strength measurements, subject only to possible measurement censoring.