Optimized Data Rate Allocation for Dynamic Sensor Fusion over Resource Constrained Communication Networks

This paper presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss-Markov process and encoders that transmit the measurements to a data fusion center through the resource restricted communication network. The proposed approach heuristically minimizes a weighted sum of communication costs subject to a constraint on the state estimation error at the fusion center. The communication costs are quantified as the expected bitrates from the sensors to the fusion center. We show that the problem as formulated is a difference-of-convex program and apply the convex-concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and 2D target tracking by a drone swarm model for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity promoting, and an effective sensor selection heuristic

Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions

Transmission scheduling for multi-process multi-sensor remote estimation via approximate dynamic programming

In this paper, we consider a remote estimation problem where multiple dynamical systems are observed by smart sensors, which transmit their local estimates to a remote estimator over channels prone to packet losses. Unlike previous works, we allow multiple sensors to transmit simultaneously even though they can cause interference, thanks to the multi-packet reception capability at the remote estimator. In this setting, the remote estimator can decode multiple sensor transmissions (successful packet arrivals) as long as their signal-to-interference-and-noise ratios (SINR) are above a certain threshold. In this setting, we address the problem of optimal sensor transmission scheduling by minimizing a finite horizon discounted expected estimation error covariance cost across all systems at the remote estimator, subject to an average transmission cost. While this problem can be posed as a stochastic control problem, the optimal solution requires solving a Bellman equation for a dynamic programming (DP) problem, the complexity of which scales exponentially with the number of systems being measured and their state dimensions. In this paper, we resort to a novel Least Squares Temporal Difference (LSTD) Approximate Dynamic Programming (ADP) based approach to approximating the value function. More specifically, an off-policy based LSTD approach, named in short Enhanced-Exploration Greedy LSTD (EG-LSTD), is proposed. We discuss the convergence analysis of the EG-LSTD algorithm and its implementation. A Python based program is developed to implement and analyse the different aspects of the proposed method. Simulation examples are presented to support the results of the proposed approach both for the exact DP and ADP cases