Resources such as bandwidth and energy are limited in many wireless communications use cases, especially when large numbers of sensors and fusion centers need to exchange information frequently. One opportunity to overcome resource constraints is the use of event-based transmissions and estimation to transmit only information that contributes significantly to the reconstruction of the system's state. The design of efficient triggering policies and estimators is crucial for successful event-based transmissions. While previously deterministic and stochastic event triggering policies have been treated separately, this paper unifies the two approaches and gives insights into the design of reliable trigger-matching estimators. Two different estimators are presented, and different pairs of triggers and estimators are evaluated through simulation studies.

Maritime transport and vessel monitoring rely on multiple systems for positioning, such as the Automatic Identification System, electro-optical systems, and shore-based radar systems, to improve safety and efficiency in vessel tracking. However, each system has inherent limitations, including coverage gaps, reliance on vessel compliance, and limited real-time monitoring capabilities. As a complementary approach to existing methods and systems, this paper presents the SeaSentry system, a passive sensor network designed to detect, position, and track vessels in real time, thus eliminating the need for onboard installations. The sensors detect radar pulses emitted by the vessels' rotating radar antennas and compute time stamps as the radar beams pass over them. Geometric constraints can be derived from time differences of arrival to localize the vessels, with time error and synchronization demands in the millisecond range. Along with some initial results, this paper discusses the SeaSentry setup and data processing pipeline.

Distributed sensor fusion requires the transmission of intermediate fusion results, consisting of point estimates and associated error covariance or information matrices. Bandwidth constraints necessitate data compression techniques for error covariance and information matrices, which typically dominate data volume. To ensure the safe use of the fusion results for decision-making, these techniques must be conservative, i.e., not lead to the compressed error covariance or information matrices underestimating the true estimate error. This work introduces a novel approach for the conservative compressed transmission of information matrices, that builds on a previous event-based method for covariance matrices. The proposed method allows the entire sensor fusion pipeline to operate in ‘information space’, facilitating efficient fusion operations without the need to compute corresponding covariance matrices. Contributions include an event-trigger for information matrices and a robust-optimization-based bounding mechanism ensuring conservativeness. The proposed approach is evaluated in the context of transmitting error information matrices generated by extended information filter SLAM to a receiver for further processing.

Many automation tasks require to fuse information that is acquired by distributed sensors and passed through a wireless network across multiple nodes. The growing number of connected sensors and agents increases the burden on the communications network and the energy consumption. Further challenges in information fusion arise from correlated data shared between nodes. To mitigate the negative effects, an efficient multi-sensor fusion approach is presented in this paper. A system design that uses stochastic event-based instead of periodic transmissions is proposed based on two different algorithms, the augmented state approach and fast covariance intersection. Furthermore, two different network topologies are investigated and a methodology to handle correlations among both finite impulse response and recursive estimates is developed. Together, the results represent a wide range of network topologies and possible correlation structures and give insights into the estimation performance and network utilization.

Decentralized data fusion algorithms are fundamentally built on the exchange of estimates and covariance matrices between the individual components. This leads to a high volume of data, mainly caused by the covariance matrices, which can be problematic, especially in environments with limited bandwidth. In order to guarantee the proper functioning of decentralized estimation algorithms, data reduction methods for covariance matrices must ensure that the reduced matrices are conservative, i.e., do not underestimate the actual uncertainty. Motivated by these considerations, this paper presents an elementwise event-triggered method for the data-reduced transmission of covariance matrices that takes into account the aforementioned condition concerning uncertainty. For this purpose, several event triggers are proposed and, based on the event data and diagonal dominance, upper bounds for the actual covariance matrices are derived. An investigation of the data reduction and its influence on the estimation results is performed in a decentralized tracking scenario. The results show that substantial data reduction is possible with only minor losses in estimation quality.

Networks consisting of several spatially distributed sensor nodes are useful in many applications. While distributed processing of information can be more robust and flexible than centralized filtering, it requires careful consideration of dependencies between local state estimates. This paper proposes an algorithm to keep track of dependencies in decentralized systems where no dedicated fusion center is present. Specifically, it addresses double counting of measurement information due to intermediate fusion results as well as correlations due to common process noise and common prior information. To limit the necessary amount of data, this paper introduces a method to bound correlations partially, leading to a more conservative fusion result while reducing the necessary amount of data. Simulation studies compare the performance and convergence rate of the proposed algorithm to other state-of-the-art methods.

With the advent of cheap sensor technology, multisensor data fusion algorithms have been becoming a key enabler for efficient in-network processing of sensor data. The information filter, in particular, has proven useful due to its simple additive structure of the measurement update equations. In order to exploit this structure for an efficient in-network processing, each node in the network is supposed to locally process and combine data from its neighboring nodes. The aspired in-network processing, at first glance, prohibits efficient privacy-preserving communication protocols, and encryption schemes that allow for algebraic manipulations are often computationally too expensive. Partially homomorphic encryption schemes constitute far more practical solutions but are restricted to a single algebraic operation on the corresponding ciphertexts. In this paper, an additive-homomorphic encryption scheme is used to derive a privacy-preserving implementation of the information filter where additive operations are sufficient to distribute the workload among the sensor nodes. However, the encryption scheme requires the floating-point data to be quantized, which impairs the estimation quality. The proposed filter and the implications of the necessary quantization are analyzed in a simulated multisensor tracking scenario.

In a surveillance environment with high clutter, finding the correct measurement to track associations becomes extremely important for efficient target tracking. This study offers a novel algorithm to retrodict the data association probabilities at any past time instant, when the batch set of measurements is kept in memory. For the retrodiction procedure, the batch association cost is first written explicitly as a binary integer optimization problem with a quadratic cost function and it is shown that the relaxed form of the problem is convex. From the relaxed problem, a lower bound for the optimal association cost is derived, and this lower bound is used as the data association probabilities pertaining to that selected time instant in the past. Due to its consideration of the batch set of data in a retrospective manner, we will call this algorithm as Retrodictive Probabilistic Data Association, RPDA. For simplification of the mathematical analysis, a single point target with no missing measurements, i.e. PD= 1, is taken into account.

Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this paper, a novel technique is presented that provides the means to reconstruct the required correlation structure. For this purpose, each node computes a set of deterministic samples that provides all the information required to reassemble the cross-covariance matrix for each pair of estimates. As the number of samples is increasing over time, a method to reduce the size of the sample set is presented and studied. In doing so, communication expenses can be reduced significantly, but approximation errors are possibly introduced by neglecting past correlation terms. In order to keep approximation errors at a minimum, an appropriate set size can be determined and a trade-off between communication expenses and estimation quality can be found.

Decentralized data fusion is a challenging task. Either it is too difficult to maintain and track the information required to perform fusion optimally, or too much information is discarded to obtain informative fusion results. A well-known solution is Covariance Intersection, which may provide too conservative fusion results. A less conservative alternative is discussed in this paper, and generalizations are proposed in order to apply it to a wide class of fusion problems. The Inverse Covariance Intersection algorithm is about finding the maximum possible common information shared by the estimates to be fused. A bound on the possibly shared common information is derived and removed from the fusion result in order to guarantee consistency. It is shown that the conditions required for consistency can be significantly relaxed, and also other causes of correlations, such as common process noise, can be treated.

For distributed estimation, algorithms have to be specifically crafted to minimize communication between the sensor nodes. As an adjusted version of the regular Kalman filter, the distributed Kalman filter (DKF) allows for deriving optimal results while not requiring regular communication. To achieve this, the DKF requires that each node has full knowledge about the system model and measurement models of all nodes. However, the DKF is not sufficient if the characteristics of the errors in the system and measurement models are not purely stochastic. In this paper, we present a distributed version of a combined stochastic and set-membership Kalman filter. The proposed filter optimizes the approximations of the set-membership uncertainties and can even yield better results than the regular centralized filter.

With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes that all knowledge about the measurements, except the measurements themselves, are known to all nodes. If this condition is met, the DKF allows deriving the optimal estimate if all information is combined in one node at an arbitrary time step. In this paper, we present an information form of the distributed Kalman filter (IDKF) that allows the use of explicit system inputs at the individual nodes while still yielding the same results as a centralized Kalman filter.