Motivated by the maneuvering target tracking with sensors such as radar and sonar, this paper considers the joint and recursive estimation of the dynamic state and the timevarying process noise covariance in nonlinear state space models. Due to the nonlinearity of the models and the non-conjugate prior, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and unknown process noise covariance, resulting in the posterior probability distribution functions lacking closed-form solutions. This paper presents a recursive solution for joint nonlinear state estimation and model parameters identification based on the approximate Bayesian inference principle. The stochastic search variational inference is adopted to offer a flexible, accurate, and effective approximation of the posterior distributions. We make two contributions compared to existing variational inference-based noise adaptive filtering methods. First, we introduce an auxiliary latent variable to decouple the latent variables of dynamic state and process noise covariance, thereby improving the flexibility of the posterior inference. Second, we split the variational lower bound optimization into conjugate and non-conjugate parts, whereas the conjugate terms are directly optimized that admit a closed-form solution and the non-conjugate terms are optimized by natural gradients, achieving the trade-off between inference speed and accuracy. The performance of the proposed method is verified on radar target tracking applications by both simulated and real-world data.

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Cu-Sn-Fe alloys with different compositions were developed by casting, normalizing treatment, cold roll and subsequent annealing treatment. The results showed that the tensile strength and resistivity of the Cu-xSn-xFe alloys (where x represents wt.%) improved with increasing the content of Sn and Fe. Compared with the as-cast alloys, the resistivity and tensile strength of the Cu-xSn-xFe alloys after normalizing and cold rolling treatment increased. In addition, the resistivity and mechanical properties of the alloys after the annealing treatment were improved significantly. Finally, a conclusion could be drawn that the annealed Cu-2Sn-5Fe alloy had good mechanical properties and resistivity, and the values of the tensile strength, mechanical elongation and resistivity reached 552 MPa, 32 % and 1.92 µΩ·cm, respectively.

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Hybrid manufacturing (HM) Ti-6Al-4V components combining the advantages of forging and additive manufacturing were achieved. The bonding zone of the HMed component joins the substrate and the additive manufactured zone and its α -α/α /α m -α/β heterogeneous microstructure was attributed to the distinguishing cooling rate and pseudo-isothermal annealing temperature. The phase transformation mechanisms were clarified with a numerical simulation model for thermal analysis. Moreover, preheating manipulate the size, volume fraction and distribution of α /α m /α phases to tailor the heterogeneous microstructure. IMPACT STATEMENTPreheating can serve as an effective way to tailor the heterogeneous microstructure in the bonding zone of HMed Ti-6Al-4V by affecting the cooling rate and pseudo-isothermal annealing temperature.