A Fast Direct Position Determination with Embedded Convolutional Neural Network

Abstract

AbstractThe direct position determination (DPD) method is more accurate than the previous two-step method in passive positioning. Enormous computational complexity in DPD is a severe drawback, which causes both real-time and high-accuracy to be challenging to satisfy. We integrate the formulas of the DPD and the image regression technique in computer vision, offering the unified computational graphs of both to investigate the fundamental reason for large time consumption in DPD. To achieve efficient DPD, we propose a fast DPD with an embedded convolutional neural network (CNNDPD), which is an end-to-end passive positioning network. We use a wavelet transform two-dimensionalization to convert the time domain signal into a time-frequency map and extract the time-frequency attributes effectively for the received data required for positioning. Other information required for positioning is stitched with the findings of time-frequency map processing and sent into fully-connected networks, allowing fuse with time-frequency information effectively. The simulation results show that the CNNDPD has the advantage of fast and highly accurate positioning. In a wide-area localization setting, CNNDPD has 26 times and 46 times faster inference speed than exhaustive search DPD and genetic algorithm DPD, respectively, without reducing accuracy. Furthermore, CNNDPD has a lower false alarm rate than the two benchmarks.KeywordsDirect position determinationComputer visionConvolutional neural networkMaximum likelihood estimation