RF-based Direction Finding of UAVs Using DNN

By Samith Abeywickrama, Lahiru Jayasinghe, Hua Fu, Subashini Nissanka, Chau Yuen. 2017

37 citations recorded by the source corpus.

This paper presents a sparse denoising autoencoder (SDAE)-based deep neural network (DNN) for the direction finding (DF) of small unmanned aerial vehicles (UAVs). It is motivated by the practical challenges associated with classical DF algorithms such as MUSIC and ESPRIT. The proposed DF scheme is practical and low-complex in the sense that a phase synchronization mechanism, an antenna calibration mechanism, and the analytical model of the antenna radiation pattern are not essential. Also, the proposed DF method can be implemented using a single-channel RF receiver. The paper validates the proposed method experimentally as well.

Subfields: Aerospace Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Chau Yuen
2. Shuzhi Sam Ge
3. Takateru Urakubo

In Signal Processing

1. Chau Yuen

Citations in the corpus

• A survey of data fusion in smart city applications (2019, neutral): “It aids consumers to trace the origin of food using Radio Frequency Identification (RFID) along with database or WSN.”
• Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery (2018, background): “Recently, convolutional neural networks (CNN) and long short-term memory (LSTM) networks have emerged as efficient methods in many pattern recognition application domains such as computer vision [6], [7], surveillance [8], [9], and medicine [10].”