In this study, we present ML-Track, an innovative uncooperative passive tracking system leveraging WiFi communication signals between multiple devices. Our approach is realized with three pivotal techniques. First, we introduce a novel protocol termed multi-link round-trip CSI, which enables multi-link bistatic Doppler detection within a WiFi network. Second, a phase error cancellation method is developed, and we demonstrate a 0.92 rad reduction in error (0.96 to 0.04 rad) experimentally. Lastly, we propose a particle-filter-based back-end to track a moving human in the room passively without the need for the participant to carry any type of cooperative or active device. A prototype system is constructed using four Raspberry Pi CM4 units and subjected to real-world evaluations. Experimental results indicate a median error of approximately 0.23 m for tracking, which corresponds to a relative error of 5.8% based on the 4 m side length of the experimental field. Compared to existing studies, a distinct advantage of our system is it can run with non-MIMO (single-antenna) WiFi devices, making it particularly suitable for budget or low-profile WiFi hardware. This compatibility makes it an ideal fit for real-world Internet-of-Things (IoT) devices. Moreover, in terms of computational demands, our solution excels, delivering real-time performance on the Raspberry Pi CM4 while utilizing just 20% of its CPU capability and drawing a modest 2.5 watts of power.

A power-efficient, adaptive and IoT-based volcanic eruption detector is realised to provide a robust trigger mechanism for a custom-built radar for pyroclastic flow monitoring. The design, simulation and testing of the detector, and its performance measured at Volcano Santiaguito in Guatemala over a three-month deployment are described in detail. The proposed detector controls the radar system and enables a fully automated remote sensing operation with a power consumption of 0.5 W. Typically, around 70% detection accuracy is obtained with almost no false detections and up to 80% less power consumption compared to continual capturing.

Indoor localization using WiFi signals has been studied since the emergence of WiFi communication. This paper presents a novel training-free approach to indoor localization using a customized WiFi protocol for data collection and a factor graph-based back-end for localization. The protocol measures the round-trip phase, which is very sensitive to small changes in displacement. This is because the sub-wavelength displacements introduce significant phase changes in WiFi signal. However, the phase cannot provide absolute range information due to angle wrap. Consequently, it can only be used for relative distance (displacement) measurement. By tracking the round-trip phase over time and unwrapping it, a relative distance measurement can be realized and achieve a mean absolute error (MAE) of 0.06m. For 2-D localization, factor graph optimization is applied to the round-trip phase measurements between the STA (station) and four APs (access points). Experiments show the proposed concept can offer a decimeter-level (0.26m MAE and 0.24m 50%CDF) performance for real-world indoor localization.

This paper presents a wireless Doppler sensing system using WiFi round-trip channel state information (RTCSI), which is implemented using the channel state information (CSI) from the Raspberry Pi CM4 onboard WiFi chip and a customized WiFi protocol. Utilizing the CSI phase in WiFi sensing is challenging as hardware asynchronization introduces significant phase errors. Similar to WiFi round-trip time (RTT) ranging, RTCSI was proposed to cancel the adverse effect of asynchronization through two-way communication. However, previous work mainly focuses on measuring RTCSI over frequency (different WiFi channels) to simulate a wide-band ranging. In this work, RTCSI is measured over time and a Doppler sensing prototype is built to detect a moving target in the wireless channel. Our findings show that this RTCSI-based Doppler sensing system is sensitive and effective in the real world. Moreover, it may be integrated with other techniques further to improve the performance in joint communications and sensing.

We propose an algorithm to evaluate the direction of arrival (DoA) estimation in the cross-track dimension of an airborne pulsed ice-sounding synthetic aperture radar (SAR), formed by a non-linear array. The conventional methods process either simulated data, meaning a lack of real scenario, or collected over regions previously mapped, where a limited number of DoAs is found. In our technique, we combine the echoes of different transmitted pulses from a real data take above the sea surface, planned to vary the roll angle of the aircraft. Due to the mirror-like behavior of the sea, when echoes at different roll angles are summed, we obtain raw data or images with several DoAs, relative to the main axes of the aircraft. This method is used to assess the estimation algorithms and choose the optimal for Antarctic bedrock 3D-imaging, identifying the DoAs to shape the true bed topography.

We propose an algorithm to calculate the refraction path travelled by a signal from a source to a remote scatterer, through a horizontally parallel multilayer medium with vertical-varying refractive index. The exact solution in a simple two-layered medium requires solving a degree-4 polynomial according to Snell's law, meaning a time-demanding solution. For narrow-beam sources a small-angle approximation is commonly used. In this new technique, we transformed the governing equations until the system can be solved by intersection of two curves. As these are monotonic with opposite slope, the crossing point can be estimated by iteration, with a minimum search algorithm. This method has been applied to focus synthetic aperture radar images of the Antarctic bedrock, improving the detected response in the wide-beam case, and reducing time regarding to compute the polynomial solution.

Jamming in defence applications is increasingly difficult because of advanced signal processing countermeasures. In this study, task‐dependent power‐constraint optimal jamming techniques are investigated. To prevent the target from being detected, a novel jamming technique is proposed to minimise the signal‐to‐interference‐plus‐noise ratio (SINR) of the radar for extended known and stochastic target. To impair the parameter estimation performance, another jamming technique is proposed which minimises the mutual information (MI) between the radar return and the stochastic target impulse response. The optimal jamming spectrum is obtained assuming that the jammer has intercepted the radar waveform generally. However, the precise characteristic of radar waveform is impossible to capture in practice. To model this, it is considered that the waveform spectrum lies in an uncertainty class confined by known upper and lower bounds. Then, the minimax robust jamming is designed based on the SINR and MI criteria, which optimises the worst‐case performance. Results demonstrate that the two criteria lead to different optimal jamming results but they have a close relationship from the Shannon's capacity equation which provides useful guidance on jamming power allocation for different jamming tasks. However, their behaviour with respect to the waveform uncertainty is the same.

Observation of the thickness of Antarctic ice shelves is vital for predicting the rise in sea levels caused by melting of the world's largest fresh water reservoir. This paper proposes a ground-based radar system using a MIMO phased array antenna layout to accurately monitor thickness changes and provide 2 dimensional cross-range information of the ice shelf. The radar system used to collect data is a Step Frequency (SFCW) Radar using multiple transmitting and receiving antennas which operate orthogonally in time. The MIMO antenna layout is shown together with the beam pattern from the associated virtual array. The aperture synthesis of the MIMO array using digital beamforming technique is discussed. Results of the aperture synthesis, showing the cross-range and depth profile of the ice shelf to be observed will be shown. The study conducted using the MIMO antennas is part of a future plan to replace the SFCW Radar system with an FMCW system that will allow long term deployments to observe variability in the rate of change of the ice shelf thickness. One of the conclusions of the paper is that a linear version of the MIMO array will make more efficient use of the antennas than a 2D version of the array.

This paper introduces a new method for estimating location of radio frequency identification (RFID) tags for application in indoor environments. The proposed positioning scheme employs a time-difference of arrival (TDOA) range estimation algorithm. The time-difference measurement is performed by taking advantage of the de-ramp properties of a linear frequency modulation (LFM) chirp. The paper provides details of the mechanism of this LFM-TDOA scheme from the system point of view. Compared to other popular radio technologies in the indoor positioning field, this new scheme is easy to design and requires only moderate computation ability. According to the experiment results summarized in the paper, the LFM-TDOA system shows good range estimation accuracy and thus has the potential to be used in accurate indoor location application.