Implementation of a coherent real-time noise radar system

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse-Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real-time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200\ua0MHz bandwidth, and 268\ua0ms integration time while processing a range of about 8.5\ua0km. Additionally, the system can perform real-time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off-chip dynamic random access memory

Electronic countermeasures applied to passive radar

Passive Radar (PR) is a form of bistatic radar that utilises existing transmitter infrastructure such as FM radio, digital audio and video broadcasts (DAB and DVB-T/T2), cellular base station transmitters, and satellite-borne illuminators like DVB-S instead of a dedicated radar transmitter. Extensive research into PR has been performed over the last two decades across various industries with the technology maturing to a point where it is becoming commercially viable. Nevertheless, despite the abundance of PR literature, there is a scarcity of open literature pertaining to electronic countermeasures (ECM) applied to PR. This research makes the novel contribution of a comprehensive exploration and validation of various ECM techniques and their effectiveness when applied to PR. Extensive research has been conducted to assess the inherent properties of the lluminators of Opportunity to identify their possible weaknesses for the purpose of applying targeted ECM. Similarly, potential jamming signals have also been researched to evaluate their effectiveness as bespoke ECM signals. Whilst different types of PR exist, this thesis focuses specifically on ECM applied to FM radio and DVB-T2 based PR. The results show noise jamming to be effective against FM radio based PR where jamming can be achieved with relatively low jamming power. A waveform study is performed to determine the optimal jamming waveform for an FM radio based PR. The importance of an effective direct signal interference (DSI) canceller is also shown as a means of suppressing the jamming signal. A basic overview of counter-ECM (ECCM) is discussed to counter potential jamming of FM based PR. The two main processing techniques for DVB-T2 based PR, mismatched and inverse filtering, have been investigated and their performance in the presence of jamming evaluated. The deterministic components of the DVB-T2 waveform are shown to be an effective form of attack for both mismatched filtering and inverse filtering techniques. Basic ECCM is also presented to counter potential pilot attacks on DVB-T2 based PR. Using measured data from a PR demonstrator, the application and effectiveness of each jamming technique is clearly demonstrated, evaluated and quantified

Low cost passive radar through software defined radio

Passive radars utilise existing terrestrial radio signals, such as those produced by radio or television stations, to track objects within their range. This project aims to determine the suitability of low cost USB TV tuners as hardware receivers for a Software Defined Radio (SDR) based passive radar receiver. Subsequently determining its effectiveness in producing inverse synthetic aperture radar images using data collected from Digital Television signals. Since the initial identification of passive radar, Militaries the world over have been using it as a part of electronic warfare. The evolution of SDR has enabled greater access to the technologies required to implement passive radar, with the greatest limitation being the cost of the required hardware. The availability of low cost hardware was therefore investigated to determine its suitability and subsequently the availability of passive radar to a wider audience. Research was conducted into the available SDR receivers, and comparison of specifications was made against the low cost receiver used in the project. A functional hardware platform based around the Realtek RTL2832U chipset has been developed to determine its suitability as a low cost receiver verifying its ability to coherently receive radio signals for target identification. A complex ambiguity function was implemented to interpret sampled data windows, with the output of these windows to be compared to the requirements for an inverse synthetic aperture radar input, thus determining the suitability of the device. Interpretation of the received data has identified that although the hardware is capable, a real time implementation of data processing is not yet possible, impeding the ability to determine the suitability of the receiver as an inverse synthetic aperture receiver. The results of testing show that the hardware is capable of receiving and producing radar images, however due to the bandwidth of DVB-T signals , and the bandwidth limitations inherent in RTL-SDR dongles, they have proven not to be suitable for DVB-T based inverse synthetic aperture radar receivers

People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network

Accurately counting numbers people is useful in many applications. Currently, camera-based systems assisted by computer vision and machine learning algorithms represent the state-of-the-art. However, they have limited coverage areas and are prone to blind spots, obscuration by walls, shadowing of individuals in crowds, and rely on optimal positioning and lighting conditions. Moreover, their ability to image people raises ethical and privacy concerns. In this paper we propose a distributed multistatic passive WiFi radar (PWR) consisting of 1 reference and 3 surveillance receivers, that can accurately count up to six test subjects using Doppler frequency shifts and intensity data from measured micro-Doppler (µ-Doppler) spectrograms. To build the person-counting processing model, we employ a multi-input convolutional neural network (MI-CNN). The results demonstrate a 96% counting accuracy for six subjects when data from all three surveillance channels are utilised

Passive WiFi Radar for Human Sensing Using A Stand-Alone Access Point

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in ehealthcare, security and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of Channel State Information (CSI) data which originates from commodity WiFi Access Points (AP) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate OFDM signals, or periodic WiFi beacon signals whilst in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, whilst a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In the paper we present experimental data which verifies our proposed methods for using any type of signal transmission from a stand-alone WiFi device, and demonstrate the capability for human activity sensing

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system