Maximum Likelihood Alternating Summation for Multistatic Angle-based Multitarget Localization

Recent advancements in Wi-Fi sensing have sparked interest in exploiting OFDM modulated communication signals for target detection and tracking. In this study, we address the angle-based localization of multiple targets using a multistatic OFDM radar. While the maximum likelihood approach optimally merges data from each radar pair comprised by the system, it entails a complex multi-dimensional search process. Leveraging pre-estimation of the targets' parameters obtained via the MUSIC algorithm, our method decouples this multi-dimensional search into a single two-dimensional estimator per target. The proposed alternating summation method allows the computation of a combined likelihood map aggregating contributions from each radar pair, enabling target detection via peak selection. Besides reducing computational complexity, the method effectively captures target interactions and accommodates varying radar pair localization abilities. Also, it requires transmitting only the estimated channel covariance matrices of each radar pair to the central processor. Numerical simulations demonstrate superior performance over existing approaches.

Multistatic OFDM Radar Fusion of MUSIC-based Angle Estimation

This study investigates the problem of angle-based localization of multiple targets using a multistatic OFDM radar. Although the maximum likelihood (ML) approach can be employed to merge data from different radar pairs, this method requires a high complexity multi-dimensional search process. The multiple signal classification (MUSIC) algorithm simplifies the complexity to a two-dimensional search, but no framework is derived for combining MUSIC pseudo-spectrums in a multistatic configuration. This paper exploits the relationship between MUSIC and ML estimators to approximate the multidimensional ML parameter estimation with a weighted combination of MUSIC pseudo-spectrum. This enables the computation of a likelihood map on which a peak selection is applied for target detection. In addition to reducing the computational complexity, the proposed method relies only on transmitting the estimated channel covariance matrices of each radar pair to the central processor. A numerical analysis is conducted to assess the benefits of the proposed fusion.

Deceptive Jamming in WLAN Sensing

Joint Communication and Sensing (JCAS) is taking its first shape in WLAN sensing under IEEE 802.11bf, where standardized WLAN signals and protocols are exploited to enable radar-like sensing. However, an overlooked problem in JCAS, and specifically in WLAN Sensing, is the sensitivity of the system to a deceptive jammer, which introduces phantom targets to mislead the victim radar receiver. Standardized waveforms and sensing parameters make the system vulnerable to physical layer attacks. Moreover, orthogonal frequency-division multiplexing (OFDM) makes deceptive jamming even easier as it allows digitally generated artificial range/Doppler maps. This paper studies deceptive jamming in JCAS, with a special focus on WLAN Sensing. The provided mathematical models give insights into how to design jamming signals and their impact on the sensing system. Numerical analyses illustrate various distortions caused by deceptive jamming, while the experimental results validate the need for meticulous JCAS design to protect the system against physical layer attacks in the form of deceptive jamming.