In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission.

Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

Accurately predicting the remaining useful life (RUL) of UAV bearings is challenging due to maneuver-shock-induced stochastic jumps during their two-phase degradation, while existing numerical methods are computationally too costly for UAV onboard computing. To address this, an analytical RUL prediction method considering stochastic jumps is proposed. A two-phase Wiener process incorporating stochastic jumps is constructed to model degradation processes involving shocks. Subsequently, a combined Kalman Filter–Rauch–Tung–Striebel Smoothing–Expectation Maximization (KF-EM-RTS) framework is developed for simultaneous online updating of drift and diffusion coefficients. Furthermore, utilizing Stein’s Lemma, an analytical expression under a fixed-change-point assumption for the RUL probability density function (PDF) of the proposed model is derived, thereby reducing the reliance on repeated numerical integration. Under the experimental settings used in this study, the analytical implementation reduces the single-point PDF calculation time by approximately 90% compared with the corresponding numerical integration implementation, which is important for compute-limited UAV platforms. Moreover, RMSE is decreased by 48% and 76% versus models ignoring jumps. This approach offers a lightweight solution for real-time predictive maintenance of UAVs.

In response to the challenges of high uncertainty in target threat assessment and weak coordination in situational evaluation in unmanned aerial vehicle (UAV) collaborative aerial detection, this paper designs an aerial detection collaborative threat assessment strategy based on fuzzy three support decision-making, effectively solving the problem of fuzzy information processing in aerial detection threat assessment. Firstly, a fuzzy threat model for aerial detection was established, simplifying the difficulty of the problem. Secondly, a threat degree calculation method is constructed using fuzzy multi-attribute decision-making theory, and a threat assessment classification method is constructed based on the fusion of improved prospect theory and three decision-making methods. This strengthens the evaluation algorithm's ability to process fuzzy information on the field, making the threat assessment and evaluation system more flexible and effective. Subsequently, based on the single aircraft threat assessment results, a coordinated threat item for aerial detection situation was designed to further enhance the formation coordination attribute of unmanned aerial vehicle autonomous threat assessment. Finally, the effectiveness of the designed method was verified through digital simulation in the presence/absence of collaborative aerial detection scenarios, demonstrating its ability to resist fuzzy interference.