The Dark Side of Decentralized Target Tracking : Unknown Correlations and Communication Constraints

Sammanfattning: Using sensors to observe real-world systems is important in many applications. A typical use case is target tracking, where sensor measurements are used to compute estimates of targets. Two of the main purposes of the estimates are to enhance situational awareness and facilitate decision-making. Hence, the estimation quality is crucial. By utilizing multiple sensors, the estimation quality can be further improved. Here, the focus is on target tracking in decentralized sensor networks, where multiple agents estimate a common set of targets. In a decentralized context, measurements undergo local preprocessing at the agent level, resulting in local estimates. These estimates are subsequently shared among the agents for estimate fusion. Sharing information leads to correlations between estimates, which in decentralized sensor networks are often unknown. In addition, there are situations where the communication capacity is constrained, such that the shared information needs to be reduced. This thesis addresses two aspects of decentralized target tracking: (i) fusion of estimates with unknown correlations; and (ii) handling of constrained communication resources. Decentralized sensor networks have unknown correlations because it is typically impossible to keep track of dependencies between estimates. A common approach in this case is to use conservative estimators, which can ensure that the true uncertainty of an estimate is not underestimated. This class of estimators is pursued here. A significant part of the thesis is dedicated to the widely-used conservative method known as covariance intersection (CI), while also describing and deriving alternative methods for CI. One major result related to aspect (i) is the conservative linear unbiased estimator (CLUE), which is proposed as a general framework for optimal conservative estimation. It is shown that several existing methods, including CI, are optimal CLUEs under different conditions. A decentralized sensor network allows for less data to be communicated compared to its centralized counterpart. Yet, there are still situations where the communication load needs to be further reduced. The communication load is mostly driven by the covariance matrices since, in this scope, estimates and covariance matrices are shared. One way to reduce the communication load is to only exchange parts of the covariance matrix. To this end, several methods are proposed that preserve conservativeness. Significant results related to aspect (ii) include several algorithms for transforming exchanged estimates into a lower-dimensional subspace. Each algorithm corresponds to a certain estimation method, and for some of the algorithms, optimality is guaranteed. Moreover, a framework is developed to enable the use of the proposed dimension-reduction techniques when only local information is available at an agent. Finally, an optimization strategy is proposed to compute dimension-reduced estimates while maintaining data association quality.