Generalized Singular Value Decompositions of Dual Quaternion Matrix Triplets
In signal processing and identification, generalized singular value decomposition (GSVD), related to a sequence of matrices in product/quotient form are essential numerical linear algebra tools. On behalf of the growing demand for efficient processing of coupled rotation-translation signals in modern engineering, we introduce the restricted SVD of a dual quaternion matrix triplet $(\boldsymbol{A},\boldsymbol{B},\boldsymbol{C})$ with $\boldsymbol{A}\in {\bf \mathbb{DQ}}^{m \times n}$, $\boldsymbol{B} \in {\bf \mathbb{DQ}}^{m \times p}$, $\boldsymbol{C} \in {\bf \mathbb{DQ}}^{q\times n}$, and the product-product SVD of a dual quaternion matrix triplet $(\boldsymbol{A},\boldsymbol{B},\boldsymbol{C})$ with $\boldsymbol{A}\in {\bf \mathbb{DQ}}^{m \times n}$, $\boldsymbol{B} \in {\bf \mathbb{DQ}}^{n \times p}$, $\boldsymbol{C} \in {\bf \mathbb{DQ}}^{p\times q}$. The two types of GSVDs represent a sophisticated matrix factorization that accounts for a given dual quaternion matrix in conjunction with two additional dual quaternion matrices. The decompositions can be conceptualized as an adaptation of the standard SVD, where the distinctive feature lies in the application of distinct inner products to the row and column spaces. Two examples are outlined to illustrate the feasibility of the decompositions.