FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area

Abstract

We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

“…To address these drawbacks of active schemes and extend the possible applications of localization systems, DFL schemes using cameras or radio signals, such as Wi-Fi, Zigbee, RFID, Bluetooth, and FMCW radars, were introduced [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Bhattacharya et al [34] developed a fall detection and breathing sensing technique based on radar technique to detect a fall after it has happened even when the person is static.…”

“…As part of conventional indoor localization technology, multilateration and fingerprint methods are well-established techniques that use radio signals, such as Wi-Fi, Zigbee, RFID, Bluetooth, and frequency-modulated continuous-wave (FMCW) radars [19][20][21][22]. As the multilateration method is based on range estimation, its localization performance relies on the accuracy of the estimated distance to the target [23].…”

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm

“…They proposed an alternative to multiple signal classification (MUSIC), an established algorithm for DoA estimation, and took a deep-learning-based approach, where they trained a 3D convolutional neural network (3D-CNN) for DoA estimation. Kim et al [47] have also recently proposed a new highresolution and low-complexity estimation algorithm for FMCW radars. They proposed using a 2D Fast Fourier transform (2D FFT) algorithm for initial range and DoA estimation, with the purpose to reduce the search area of the pseudo-spectrum.…”

Target classification using radar cross-section statistics of millimeter-wave scattering

“…Many approaches strongly rely on the particular signal processing architecture, especially frequency-modulated continuous wave (FMCW) radars, e.g. Kim et al (2022). Obviously, deep learning is also proposed for target classification: range-azimuth distributions of the power of the radar echo are considered as image data, which feed deep neural networks to predict some attributes of the target (Patel et al , 2019).…”