In this article, a new comprehensive system for distributed multisensor multitarget tracking is proposed. All of its functions, including track initiation, confirmation, maintenance and termination, and track-to-track association and fusion, are built around the concept of the probability of target existence (PTE). A new track maintenance algorithm is composed of the correction part, with data association requiring computations linearly depending on the number of measurements and tracks, and the prediction part, containing an adaptive consensus scheme, coping with limited sensor observability. A new track-to-track association and fusion algorithm based on an approximation of track association probability is also proposed, ensuring consistency and continuity of individual tracks. Track initiation and termination algorithms are derived on the basis of local PTEs. Stability of the proposed system is studied for the steady state and time-varying regimes. The system as a whole achieves high performance close to the centralized solution, outperforming all the comparable existing state-of-the-art approaches, keeping much lower communication and computation requirements.

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements.

The authors propose a new algorithm for distributed target tracking by sensor networks with sensors having limited sensing range (radars, sonars, cameras), using an adaptive multi‐step consensus scheme. The proposed distributed adaptation strategy represents the core element of the algorithm, providing: (i) asymptotic consensus gains giving the desired importance to the nodes observing the target, and (ii) fast convergence rate of the consensus scheme, enabling efficient implementation. Another contribution of this study is a theoretical study of the algorithm properties, including asymptotic stability and reduction of noise influence. The given simulation results show that the proposed algorithm outperforms the existing algorithms, preserving, at the same time, much lower communication and computation requirements.

The Internet of Things (IoT) connects a wide range of sensors and devices in environments that are often dynamic and resource-constrained, where efficient distributed solutions are essential for ensuring robust and scalable operation. This article presents a novel adaptive consensus algorithm, tailored for distributed signal processing in heterogeneous sensor networks, with a focus on distributed estimation and target tracking. The algorithm addresses the challenge posed by networks where intelligent sensors have limited sensing, computation, and communication capabilities, resulting in diverse quality of locally available information, and leading to neighbor-based information exchanges. It employs asynchronous gossip protocols to randomly exchange information between nodes, ensuring robustness to time synchronization and network topology uncertainties, while limiting computational and communication costs. The adaptation mechanism operates in two complementary ways. First, we account for variations in the quality of local processing results, ensuring that asymptotic behavior of the consensus scheme accurately reflects this diversity. Second, we introduce a novel adaptation of the rates at which nodes initiate communication, using the available local information. This enables fast information dissemination and provides a solution that is both effective and efficient. We show that, under appropriate network connectivity assumptions, the results obtained by the algorithm converge to the desired asymptotic values in the mean square sense. Numerical simulations demonstrate the algorithm’s properties and effectiveness, particularly in modeling visual surveillance networks where fixed cameras are augmented by moving drones to extend the coverage area.