Posterior-Aware Differential Channel Tracking for Reliable Single-Stream DAB+ Passive Radar

Digital audio broadcasting plus (DAB+) is an attractive illuminator for passive radar because it provides persistent, high-power, and geographically widespread very high frequency (VHF) orthogonal frequency-division multiplexing (OFDM) signals. A channel state information (CSI) sensing approach can convert a single received DAB+ stream into a CSI sequence for radar sensing, avoiding the need for a separately received reference signal in conventional passive radars. However, CSI estimation in DAB+ is challenging due to the differentially encoded communication symbols across time. A wrong symbol transition estimation leads to a persistent multiplicative error in the sequential CSI sequence within a DAB+ frame. This paper formulates single-stream DAB+ passive radar as a posterior-probability-aware differential CSI tracking problem. The proposed method uses the previously tracked CSI as a channel prior, performs prediction-aided maximum a posteriori detection of current symbol, converts posterior transition reliability into observation uncertainty, and applies linear minimum mean squared error fusion to obtain a stable tracking CSI. A reliability-informed CSI fusion strategy is also introduced to preserve weak target information. Theoretical analysis is provided, showing guaranteed performance again in symbol and CSI estimation. Simulation results show that the proposed method can reduce CSI estimation error by over 15~dB compared with prior art. It also improves median target-to-background ratio by more than 11~dB in random fading scenes. Experiments in Sydney, Australia demonstrate improved range-Doppler maps for commercial aircraft sensing.

Rethinking RSSI for WiFi Sensing

The Received Signal Strength Indicator (RSSI) is widely available on commodity WiFi devices but is commonly regarded as too coarse for fine-grained sensing. This paper revisits its sensing potential and presents WiRSSI, a bistatic WiFi sensing framework for passive human tracking using only RSSI measurements. WiRSSI adopts a 1Tx-3Rx configuration and is readily extensible to Multiple-Input Multiple-Output (MIMO) deployments. We first reveal how CSI power implicitly encodes phase-related information and how this relationship carries over to RSSI, showing that RSSI preserves exploitable Doppler, Angle-of-Arrival (AoA), and delay cues associated with human motion. WiRSSI then extracts Doppler-AoA features via a 2D Fast Fourier Transform and infers delay from amplitude-only information in the absence of subcarrier-level phase. The estimated AoA and delay are then mapped to Cartesian coordinates and denoised to recover motion trajectories. Experiments in practical environments show that WiRSSI achieves median XY localization errors of 0.905 m, 0.784 m, and 0.785 m for elliptical, linear, and rectangular trajectories, respectively. In comparison, a representative CSI-based method attains median errors of 0.574 m, 0.599 m, and 0.514 m, corresponding to an average accuracy gap of 0.26 m. These results demonstrate that, despite its lower resolution, RSSI can support practical passive sensing and offers a low-cost alternative to CSI-based WiFi sensing.

Towards SISO Bistatic Sensing for ISAC

Integrated Sensing and Communication (ISAC) is a key enabler for next-generation wireless systems. However, real-world deployment is often limited to low-cost, single-antenna transceivers. In such bistatic Single-Input Single-Output (SISO) setup, clock asynchrony introduces random phase offsets in Channel State Information (CSI), which cannot be mitigated using conventional multi-antenna methods. This work proposes WiDFS 3.0, a lightweight bistatic SISO sensing framework that enables accurate delay and Doppler estimation from distorted CSI by effectively suppressing Doppler mirroring ambiguity. It operates with only a single antenna at both the transmitter and receiver, making it suitable for low-complexity deployments. We propose a self-referencing cross-correlation (SRCC) method for SISO random phase removal and employ delay-domain beamforming to resolve Doppler ambiguity. The resulting unambiguous delay-Doppler-time features enable robust sensing with compact neural networks. Extensive experiments show that WiDFS 3.0 achieves accurate parameter estimation, with performance comparable to or even surpassing that of prior multi-antenna methods, especially in delay estimation. Validated under single- and multi-target scenarios, the extracted ambiguity-resolved features show strong sensing accuracy and generalization. For example, when deployed on the embedded-friendly MobileViT-XXS with only 1.3M parameters, WiDFS 3.0 consistently outperforms conventional features such as CSI amplitude, mirrored Doppler, and multi-receiver aggregated Doppler.

Water Level Sensing via Communication Signals in a Bi-Static System

Accurate water level sensing is essential for flood monitoring, agricultural irrigation, and water resource optimization. Traditional methods require dedicated sensor deployments, leading to high installation costs, vulnerability to interference, and limited resolution. This work proposes PMNs-WaterSense, a novel scheme leveraging Channel State Information (CSI) from existing mobile networks for water level sensing. Our scheme begins with a CSI-power method to eliminate phase offsets caused by clock asynchrony in bi-static systems. We then apply multi-domain filtering across the time (Doppler), frequency (delay), and spatial (Angle-of-Arrival, AoA) domains to extract phase features that finely capture variations in path length over water. To resolve the $2\pi$ phase ambiguity, we introduce a Kalman filter-based unwrapping technique. Additionally, we exploit transceiver geometry to convert path length variations into water level height changes, even with limited antenna configurations. We validate our framework through controlled experiments with 28 GHz mmWave and 3.1 GHz LTE signals in real time, achieving average height estimation errors of 0.025 cm and 0.198 cm, respectively. Moreover, real-world river monitoring with 2.6 GHz LTE signals achieves an average error of 4.8 cm for a 1-meter water level change, demonstrating its effectiveness in practical deployments.

Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. Firstly, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Secondly, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Thirdly, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject's body movement. Experimental results show that, the 90${^{th}}$ percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively.