In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both algorithms were compared and experimentally validated for a small ground robot. Moreover, data were collected throughout the onboard sensors. The results show better prediction performance in the case of the GPR method, as an estimation algorithm and in providing a measure of uncertainty.

Tracking highly maneuvering, non-cooperative UAVs poses significant challenges due to rapid and unpredictable changes in target dynamics. Under such conditions, traditional single-model filters often fail to maintain reliable state estimates, resulting in degraded tracking performance. Multiple-Model Kalman Filter (MMKF) approaches, including the Generalized Pseudo Bayesian (GPB1) and Interacting Multiple-Model (IMM) algorithms, improve robustness by simultaneously considering multiple candidate motion models and weighting them according to the observed target behavior. Adaptive strategies, such as ${\chi }^2$-test-based or t-test-based methods, further enhance performance by dynamically responding to changes in maneuvering patterns. This paper presents a multi-criteriacomparative assessment of four MMKF formulations—GPB1, IMM, ${\chi }^2$-test-based, and t-test-based filters—under a consistent modeling and simulation framework. Particular emphasis is placed on systematically analyzing the role of the transition probability matrix (TPM), investigating how fixed, adaptive, and TPM-free strategies affect estimation accuracy, robustness to noise, and mode-identification performance. Beyond conventional Root Mean Square Error (RMSE) metrics, the filters’ comparison is carried out through confusion matrices and dwell time analysis to highlight performance nuances and trade-offs. This allows to establish which filter formulation is preferable in different operational conditions.

This letter concerns decentralized multi-agent systems in which individual agents operate under partial observability and uncertainty, but with the option of invoking inter-agent communication. When information exchange is infeasible or prohibitively expensive —owing to bandwidth limits, energy constraints, or the demands of clandestine operation— one is prevented from continually maintaining a joint belief over the underlying system state. In such cases, the decision of when to request information from others is key. In revisiting the prior work addressing this decision head-on, we see conservative strategies: agents are constrained to act solely on information that is accessible to all, with useful local observations being ignored whenever communication is too costly. Such an approach, though apposite to high-risk settings when consistent decision-making must be guaranteed, can degrade overall performance significantly. We explore how agents may integrate their local information, even when it has not been globally shared, and then communicate only when prudent—we term this an ask-type paradigm. This re-formulation, in which the agents are not compelled to synchronize, leads to a novel method that enables the decentralized execution of a centralized policy by reasoning, at execution time, about whether the cost of exchanging information will be offset by the information's value. We empirically evaluate our approach across several benchmarks and a case study, assessing in each case its effectiveness in trading between tolerating some loss of coordination and reducing communication costs.