A joint TDOA and FDOA estimation algorithm based on second-order cone programming

Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function

“…By increasing SNR, the proposed algorithm performs almost equally with the algorithm in [26]. When , the comparison between the proposed algorithm and the method in [27] is depicted in Tables 3 and 4.…”

“…Table 5 shows the computational load comparison between the proposed method and the convex optimisation toolbox [27]. It can be seen that the running time of the proposed algorithm is greatly reduced.…”

“…In [27], a fitting interpolation condition with equality and inequality constraints is proposed. The peak point of the CAF and the four points closest to the peak point are selected as the equality constraints, and the remaining points are used as the inequality constraints.…”

“…There, the CAF is approached by constructing a quadratic surface function, and the peak of the quadric surface is calculated using the least squares. In [27], a CSI‐based optimisation algorithm is proposed based on second‐order cone programming to convert the peak value of the curved surface to a cone optimisation problem, but its final solution is obtained using the convex optimisation toolbox.…”

“…The performance of the proposed method is further investigated by theoretically deriving CRLB for the problem and comparing it with the obtained performance. Meanwhile, finding the peak of the continues CAF is perused, having a limited number of signal samples. Similar to [27], the CSI is adopted here to convert this challenge into to optimisation problem. Unlike other literature, we develop the resulting problem to find a closed‐form solution through vector–matrix notations, hence avoiding the iterative or approximate methods.…”