The conventional dynamic programming-based track-before-detect (DP-TBD) methods are usually intractable in multi-target scenarios. The adjacent targets may interfere with each other, and the computational complexity is increased with the number of targets. In this paper, a DP-TBD method using parallel computing (PC-DP-TBD) is proposed to solve the above problems. The search region of the proposed PC-DP-TBD is divided into several parts according to the possible target movement direction. The energy integration is carried out independently and parallel in each part. This contributes to reducing the computational complexity in each part, since the divided search region is smaller than the whole one. In addition, the target energy can only be integrated adequately in the part in which the search direction matches the target movement. This is beneficial to improve the ability to detect the targets with various movement directions in different parts with different search directions. The solution to the problem of the adjacent targets interfering with each other is discussed. The procedure of the parallel computing in the proposed PC-DP-TBD is presented in detail. Simulations are conducted to verify the superiority of the proposed PC-DP-TBD in terms of detection probability and computational complexity.

According to the problem that liquid turbidity detection is vulnerable to the noise, a novel liquid turbidity detection method based on Bidimensional Empirical Mode Decomposition (BEMD) and Robert operator is proposed. The key part of method is the BEMD algorithm, with which, liquid images can be decomposed to several Intrinsic Mode Functions (IMFs), then we can use Robert operator to detect the edge of each IMF to reconstruct the image edges selectively for highlighting edge details of the liquid and impurity. Experimental results show that the method presented can reduce the influence of random noise on the turbidity detection effectively, and improve the accuracy of turbidity detection.