PRIMEX (PRIME-based Graph Encoding and Extraction for Information Fusion) is a novel framework designed to enhance distributed information fusion while minimizing communication overhead and computational complexity. Traditional information graph (IG)-based approaches require frequent synchronization and large-scale graph updates, leading to significant communication demands. PRIMEX overcomes these challenges by encoding information pedigree of state estimates as products of distinct prime numbers, allowing fusion to be performed using lightweight arithmetic operations such as greatest common divisor (GCD) for redundancy removal and least common multiple (LCM) for data integration. This eliminates the need for transmitting complex graph structures and instead leverages prime factorization-based queries to efficiently identify shared information, significantly improving scalability. PRIMEX is particularly well-suited for edge computing environments and decentralized systems, where reducing communication, computation, and memory overhead is crucial. By supporting federated learning principles, PRIMEX enhances system adaptability while preserving data privacy, making it a practical solution for scalable, distributed information fusion in applications such as autonomous systems, multi-agent networks, and large-scale sensing platforms.

This paper reviews forty years of distributed estimation research since the first papers on decentralized filtering appeared in 1978. Starting with a formulation of the problem, it reviews the assumptions and objectives of the main approaches, including information decorrelation, cross-covariance fusion, channel filters, covariance intersection, maximum a posteriori probability fusion, best linear unbiased estimate, and distributed Kalman filters based on pseudo estimates and augmented state estimates. It also reviews algorithms motivated by sensor networks with flexible communication including consensus and diffusion filters. Suggestions for future research are provided.

Multiple hypothesis tracking addresses difficult multiple target tracking problems by making association decisions using multiple scans or frames of data. This paper reviews forty years of its development, including the original measurement-oriented approach of Reid, track-oriented approach first formulated by Morefield, distributed processing, and recent graph-based approaches. It also discusses its relationship with random set approaches for tracking.

This paper describes a generalization of Murty's algorithm generating ranked solutions for classical assignment problems. The generalization extends the domain to a general class of zero-one integer linear programming problems that can be used to solve multiframe data association problems for track-oriented multiple hypothesis tracking (MHT). The generalized Murty's algorithm mostly follows the steps of Murty's ranking algorithm for assignment problems. It was implemented in a hybrid data fusion engine, called All- Source Track and Identity Fusion (ATIF), to provide a kbest multiple-frame association hypothesis selection capability, which is used for output ambiguity assessment, hypothesis space pruning, and multi-modal track outputs.

In this paper, we derive the updating formula of the cardinalized probability hypothesis density (CPHD) filter recently developed in [1-4], from the non-Poisson multiple-hypothesis tracking (MHT) algorithm developed earlier [23,24]. The particular form of the CPHD updating formula developed in this paper is expressed only with the probability hypothesis density (PHD) or the a posteriori intensity measure density and the a posteriori probability of the number of targets, without using the probability generating function, and is consistent with the updating formula in [5]. Several issues concerning the CPHD updating formula and the sensor modeling are discussed together with a couple of very simple but illustrative examples.

Sensor resource management is usually formulated as an optimization problem under uncertainty. We use a decision-theoretic model to show that the objective function should not be just tracking or target identification performance. Instead, it should represent the expected value of the outcome from using the collected data. This outcome depends on how the collected data and fusion results are used in making other resource management decisions such as weapon to target assignment. We argue the need for considering the integrated sensor/weapon management problem and propose a decomposition to make the solution feasible

Track fusion over a network of sensors requires association of the tracks before the state estimates can be combined. Track association generally involves two steps: evaluating an association metric to score each track-to-track association hypothesis, and selecting the best assignment between two sets of tracks. In many applications feature-aided track association can provide better performance than association with only kinematic data (e.g., position and velocity) when the target density is high. This paper develops a general association metric to support feature-aided track association that considers similarity in both the feature and kinematic domains. The association metric is based upon the maximum a posteriori probability (MAP) approach and can be used for general target and sensor models. Special forms of the association metric are given for some common situations. Numerical results illustrate the performance of different feature association metrics

This paper describes a new approach to tracking multiple groups of targets using the concept of the generalized Janossy measure density function. Each group is modeled by a group state that consists of a group common state and an unknown number of individual target component states. In order to represent a probability distribution of such a group state, we propose to use a generalized version of the Janossy measure density function. We will formulate our tracking problem as a distributed estimation problem in which each local sensor processing unit (node) produces single-frame-based group-level detections and a central fusion center (node) fuses those detections into group tracks. A simple illustrative example will be given