GNSS-based passive radar techniques for maritime surveillance

The improvement of maritime traffic safety and security is a subject of growing interest, since the traffic is constantly increasing. In fact, a large number of human activities take place in maritime domain, varying from cruise and trading ships up to vessels involved in nefarious activities such as piracy, human smuggling or terrorist actions. The systems based on Automatic Identification System (AIS) transponder cannot cope with non-cooperative or non-equipped vessels that instead can be detected, tracked and identified by means of radar system. In particular, passive bistatic radar (PBR) systems can perform these tasks without a dedicated transmitter, since they exploit illuminators of opportunity as transmitters. The lack of a dedicated transmitter makes such systems low cost and suitable to be employed in areas where active sensors cannot be placed such as, for example, marine protected areas. Innovative solutions based on terrestrial transmitters have been considered in order to increase maritime safety and security, but these kinds of sources cannot guarantee a global coverage, such as in open sea. To overcome this problem, the exploitation of global navigation satellites system (GNSS) as transmitters of opportunity is a prospective solution. The global, reliable and persistent nature of these sources makes them potentially able to guarantee the permanent monitoring of both coastal and open sea areas. To this aim, this thesis addresses the exploitation of Global Navigation Satellite Systems (GNSS) as transmitters of opportunity in passive bistatic radar (PBR) systems for maritime surveillance. The main limitation of this technology is the restricted power budget provided by navigation satellites, which makes it necessary to define innovative moving target detection techniques specifically tailored for the system under consideration. For this reason, this thesis puts forward long integration time techniques able to collect the signal energy over long time intervals (tens of seconds), allowing the retrieval of suitable levels of signal-to-disturbance ratios for detection purposes. The feasibility of this novel application is firstly investigated in a bistatic system configuration. A long integration time moving target detection technique working in bistatic range&Doppler plane is proposed and its effectiveness is proved against synthetic and experimental datasets. Subsequently the exploitation of multiple transmitters for the joint detection and localization of vessels at sea is also investigated. A single-stage approach to jointly detect and localize the ship targets by making use of long integration times (tens of seconds) and properly exploiting the spatial diversity offered by such a configuration is proposed. Furthermore, the potential of the system to extract information concerning the detected target characteristics for further target classification is assessed

MIMO Radar Waveform Design and Sparse Reconstruction for Extended Target Detection in Clutter

This dissertation explores the detection and false alarm rate performance of a novel transmit-waveform and receiver filter design algorithm as part of a larger Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) bistatic radar system amidst clutter. Transmit-waveforms and receiver filters were jointly designed using an algorithm that minimizes the mutual coherence of the combined transmit-waveform, target frequency response, and receiver filter matrix product as a design criterion. This work considered the Probability of Detection (P D) and Probability of False Alarm (P FA) curves relative to a detection threshold, τ th, Receiver Operating Characteristic (ROC), reconstruction error and mutual coherence measures for performance characterization of the design algorithm to detect both known and fluctuating targets and amidst realistic clutter and noise. Furthermore, this work paired the joint waveform-receiver filter design algorithm with multiple sparse reconstruction algorithms, including: Regularized Orthogonal Matching Pursuit (ROMP), Compressive Sampling Matching Pursuit (CoSaMP) and Complex Approximate Message Passing (CAMP) algorithms. It was found that the transmit-waveform and receiver filter design algorithm significantly outperforms statically designed, benchmark waveforms for the detection of both known and fluctuating extended targets across all tested sparse reconstruction algorithms. In particular, CoSaMP was specified to minimize the maximum allowable P FA of the CS radar system as compared to the baseline ROMP sparse reconstruction algorithm of previous work. However, while the designed waveforms do provide performance gains and CoSaMP affords a reduced peak false alarm rate as compared to the previous work, fluctuating target impulse responses and clutter severely hampered CS radar performance when either of these sparse reconstruction techniques were implemented. To improve detection rate and, by extension, ROC performance of the CS radar system under non-ideal conditions, this work implemented the CAMP sparse reconstruction algorithm in the CS radar system. It was found that detection rates vastly improve with the implementation of CAMP, especially in the case of fluctuating target impulse responses amidst clutter or at low receive signal to noise ratios (β n). Furthermore, where previous work considered a τ th=0, the implementation of a variable τ th in this work offered novel trade off between P D and P FA in radar design to the CS radar system. In the simulated radar scene it was found that τ th could be moderately increased retaining the same or similar P D while drastically improving P FA. This suggests that the selection and specification of the sparse reconstruction algorithm and corresponding τ th for this radar system is not trivial. Rather, a tradeoff was noted between P D and P FA based on the choice and parameters of the sparse reconstruction technique and detection threshold, highlighting an engineering trade-space in CS radar system design. Thus, in CS radar system design, the radar designer must carefully choose and specify the sparse reconstruction technique and appropriate detection threshold in addition to transmit-waveforms, receiver filters and building the dictionary of target impulse responses for detection in the radar scene

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks

Joint detection and localization of vessels at sea with a GNSS-Based multistatic radar

This paper addresses the exploitation of global navigation satellite systems as opportunistic sources for the joint detection and localization of vessels at sea in a passive multistatic radar system. A single receiver mounted on a proper platform (e.g., a moored buoy) can collect the signals emitted by multiple navigation satellites and reflected from ship targets of interest. This paper puts forward a single-stage approach to jointly detect and localize the ship targets by making use of long integration times (tens of seconds) and properly exploiting the spatial diversity offered by such a configuration. A proper strategy is defined to form a long-time and multistatic range and Doppler (RD) map, where the total target power can be reinforced with respect to, in turn, the case in which the RD map is obtained over a short dwell and the case in which a single transmitter is employed. The exploitation of both the long integration time and the multiple transmitters can greatly enhance the performance of the system, allowing counteracting the low-power budget provided by the considered sources representing the main bottleneck of this technology. Moreover, the proposed single-stage approach can reach superior detection performance than a conventional two-stage process where peripheral decisions are taken at each bistatic link and subsequently the localization is achieved by multilateration methods. Theoretical and simulated performance analysis is proposed and also validated by means of experimental results considering Galileo transmitters and different types of targets of opportunity in different scenarios. Obtained results prove the effectiveness of the proposed method to provide detection and localization of ship targets of interest

Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.

This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to inference with synthetic aperture radars (SAR). Sparse scenarios are considered where the interesting element is embedded in a much larger signal space. Policies are examined that adaptively distribute the constrained resources by using observed measurements to inform the allocation at subsequent stages. This thesis studies adaptive allocation policies in three main directions. First, a framework for adaptive search for sparse targets is proposed to simultaneously detect and track moving targets. Previous work is extended to include a dynamic target model that incorporates target transitions, birth/death probabilities, and varying target amplitudes. Policies are proposed that are shown empirically to have excellent asymptotic performance in estimation error, detection probability, and robustness to model mismatch. Moreover, policies are provided with low computational complexity as compared to state-of-the-art dynamic programming solutions. Second, adaptive sensor management is studied for stable tracking of targets under different modalities. A sensor scheduling policy is proposed that guarantees that the target spatial uncertainty remains bounded. When stability conditions are met, fundamental performance limits are derived such as the maximum number of targets that can be tracked stably and the maximum spatial uncertainty of those targets. The theory is extended to the case where the system may be engaged in tasks other than tracking, such as wide area search or target classification. Lastly, these developed tools are applied to tracking targets using SAR imagery. A hierarchical Bayesian model is proposed for efficient estimation of the posterior distribution for the target and clutter states given observed SAR imagery. This model provides a unifying framework that models the physical, kinematic, and statistical properties of SAR imagery. It is shown that this method generally outperforms common algorithms for change detection. Moreover, the proposed method has the additional benefits of (a) easily incorporating additional information such as target motion models and/or correlated measurements, (b) having few tuning parameters, and (c) providing a characterization of the uncertainty in the state estimation process.PHDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97931/1/newstage_1.pd

OFDM passive radar employing compressive processing in MIMO configurations

A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability

Time and Frequency Transfer in a Coherent Multistatic Radar using a White Rabbit Network

Networks of coherent multistatic radars require accurate and stable time and frequency transfer (TFT) for range and Doppler estimation. TFT techniques based on global navigation satellite systems (GNSS), have been favoured for several reasons, such as enabling node mobility through wireless operation, geospatial referencing, and atomic clock level time and frequency stability. However, such systems are liable to GNSS-denial, where the GNSS carrier is temporarily or permanently removed. A denial-resilient system should consider alternative TFT techniques, such as the White Rabbit (WR) project. WR is an Ethernet based protocol, that is able to synchronise thousands of nodes on a fibre-optic based network with sub-nanosecond accuracy and picoseconds of jitter. This thesis evaluates WR as the TFT network for a coherent multistatic pulse-Doppler radar – NeXtRAD. To test the hypothesis that WR is suitable for TFT in a coherent multistatic radar, the time and frequency performance of a WR network was evaluated under laboratory conditions, comparing the results against a network of multi-channel GPS-disciplined oscillators (GPSDO). A WR-disciplined oscillator (WRDO) is introduced, which has the short-term stability of an ovenised crystal (OCXO), and long-term stability of the WR network. The radar references were measured using a dual mixer time difference technique (DMTD), which allows the phase to be measured with femtosecond level resolution. All references achieved the stringent time and frequency requirements for short-term coherent bistatic operation, however the GPSDOs and WRDOs had the best short-term frequency stability. The GPSDOs had the highest amount of long-term phase drift, with a peak-peak time error of 9.6 ns, whilst the WRDOs were typically stable to within 0.4 ns, but encountered transient phase excursions to 1.5 ns. The TFT networks were then used on the NeXtRAD radar, where a lighthouse, Roman Rock, was used as a static target to evaluate the time and frequency performance of the references on a real system. The results conform well to the laboratory measurements, and therefore, WR can be used for TFT in coherent radar

Surveillance of Space with Passive Radar using the Murchison Widefield Array

This thesis investigates the use of the Murchison Widefield Array as a passive radar receiver for the surveillance of space. Using FM radio as the transmitter source, the MWA is able to surveil a large volume of space to detect and track satellites, space debris and other objects in orbit. This thesis covers orbital-specific signal processing methods for radar product formation, as well as orbit determination, and culminates in an Australia-wide observation campaign