Extended object tracking, an important and emerging research field, has attracted attention for applications in civilian and military fields. However, the modeling and tracking of maneuvering extended objects still face challenges that need to be solved explicitly. Specifically, it is usually difficult to describe the irregular or complex extension dynamics (e.g., the object extension changes in shape and orientation over time) of a maneuvering extended object with random hypersurface. Further, it is tightly coupled to the centroid kinematics during the object maneuvers. Hence, this paper proposes a general approach for modeling a maneuvering extended object with random hypersurface. In this approach, both the object centroid and the extension dynamics for describing the turn maneuvers with different turn rates can be characterized accurately, in a concise linear form. This facilitates the design of an effective tracking algorithm to deal with different turn maneuvers of a maneuvering extended object with random hypersurface. The merits of this proposed model are illustrated by simulation results, where the kinematic state and object extension are estimated jointly and effectively.

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.

In wireless body area networks (WBANs) for healthcare monitoring applications, simultaneously achieving high energy efficiency and high reliability is challenging as it involves conflicting objectives. This article investigates the session-specific design to minimize both energy consumption and packet loss rate (PLR) for a WBAN transmission system with an automatic repeat request (ARQ) mechanism while meeting a certain latency requirement. We first formulate the design problem for a specific transmission session as a multiobjective optimization (MOO) task and then convert it into a multiobjective Markov decision process (MOMDP). To efficiently learn the tradeoffs between energy efficiency and reliability, we propose a multiobjective reinforcement learning (MORL) solution, enabling the learning of a complete and nonlinear Pareto front using a single policy network. Simulation results demonstrate that our proposed MORL solution effectively can adjust transmission parameter values for each transmission attempt of the session to effectively balance energy efficiency and reliability according to user preferences. Compared with existing MORL approaches, the proposed MORL solution with the penalty-based boundary intersection (PBI) scalarization approach can provide a more accurate and well-distributed Pareto front approximation, even when training with limited preference weights.