An efficient target detection algorithm via Karhunen‐Loève transform for frequency modulated continuous wave (FMCW) radar applications

Abstract

This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).

Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure

“…Direction-of-arrival (DOA) estimation using sensor arrays plays an important role in various applications such as radar, sonar, and industrial Internet of Things (IIoT) [1][2][3][4][5]. Existing methods include subspace-based methods [6,7], sparsity-based methods [8][9][10], beamforming-based methods [11,12], statistical reconstruction-based methods [13,14], and neural-network-based methods [15,16].…”