Biases of initial direction estimation and focusing frequency selection affect the final focusing effect and may even cause algorithm failure in determining the focusing matrix in the coherent signal–subspace method. An optimized sonar broadband focused beamforming algorithm is proposed to address these defects. Initially, the robust Capon beamforming algorithm was used to correct the focusing matrix, and the broadband signals were then focused on the optimal focusing frequency by the corrected focusing matrix such that the wideband beamforming was transformed into a narrowband problem. Finally, the focused narrowband signals were beamformed by the second-order cone programming algorithm. Computer simulation results and water pool experiments verified that the proposed algorithm provides a good performance.

Aiming at the problem of redundant calculation of the Interactive Multi-Model-Joint Probabilistic Data Association Filtering (IMM-JPDAF) algorithm, an Interactive Multi-Model Independent Joint Probabilistic Data Association Filtering algorithm is proposed. The algorithm uses model probabilities as priori information to reliably estimate the parameters of the maneuvering target motion model, and then uses them in the JPDA module, so that the JPDA can exist independently of the model filters. The simulation results show that the proposed algorithm can save about 20% of the calculation amount without reducing the accuracy of the target tracking, effectively reducing the redundancy.

Laser-induced breakdown spectroscopy (LIBS) can be used for the rapid detection of heavy metal contamination of Tegillarca granosa (T. granosa), but an appropriate classification model needs to be constructed. In the one-class classification method, only target samples are needed in training process to achieve the recognition of abnormal samples, which is suitable for rapid identification of healthy T. granosa from those contaminated with uncertain heavy metals. The construction of a one-class classification model for heavy metal detection in T. granosa by LIBS has faced the problem of high-dimension and small samples. To solve this problem, a novel one-class classification method was proposed in this study. Here, the principal component scores and the intensity of the residual spectrum were combined as extracted features. Then, a one-class classifier based on Mahalanobis distance using the extracted features was constructed and its threshold was set by leave-one-out cross-validation. The sensitivity, specificity and accuracy of the proposed method were reached to 1, 0.9333 and 0.9667 respectively, which are superior to the previously reported methods. Keywords: laser-induced breakdown spectroscopy, Heavy metal contamination, Tegillarca granosa, one-class classification DOI: 10.25165/j.ijabe.20231604.7666 Citation: Xie Z H, Feng X A, Chen X, Huang G Z, Chen X J, Li L M, et al. Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS. Int J Agric & Biol Eng, 2023; 16(4): 201－206.

A federated fusion algorithm of joint multi-Gaussian mixture multi-Bernoulli (JMGM-MB) filters is proposed to achieve optimal fusion tracking of multiple u