Novel Power Control Scheme for Target Tracking in Radar Network with Passive Cooperation

Distributed radar network systems (DRNS) have been shown to provide significant performance improvement. With the recent development, radar network has become an attractive platform for target tracking. In practice, the netted radars in DRNS are supposed to maximize their transmitting power to achieve better target tracking performance, which may be in contradiction with low probability of intercept (LPI). This paper investigates the problem of adaptive resource scheduling based on time difference of arrival (TDOA) cooperation for target tracking by DRNS consisting of a dedicated radar netting station and multiple netted radars. Firstly, the standard interacting multiple model (IMM) algorithm incorporating extended Kalman filter (EKF) is improved by modifying the Markov transition probability with current measurements. Then, a novel resource scheduling strategy based on TDOA cooperation is presented, in which the LPI perfor¬mance for target tracking in DRNS is improved by optimiz¬ing the radar revisit interval and the transmitted power for a predefined target tracking accuracy. The comparison of the predictive error covariance matrix and the expected error covariance matrix is utilized to control the radar netting station under intermittent-working state with TDOA cooperation. Due to the lack of analytical closed-form expression for receiver operating characteristics (ROC), we utilize several popular information-theoretic criteria, namely, Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, and mutual information (MI) as the metrics for target detection performance in target tracking process. The resulting optimization problems which are associated with different information-theoretic criteria are unified under a common framework. The non¬linear programming (NP) based genetic algorithm (GA) or else known as NPGA is employed to encounter with the highly nonconvex and nonlinear optimization problems in the framework. Numerical results demonstrate that the proposed algorithm not only has excellent target tracking accuracy, but also has better LPI performance comparing to other methods

Single data set detection for multistatic doppler radar

The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO) radar to increase their accuracy and performance. The algorithms make use of the observations obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance estimation and inversion to perform target detection. One of the main interferers for a Doppler radar has always been the radar’s own signal being reflected off the surroundings. The reflections of the transmitted waveforms from the ground and other stationary or slowly-moving objects in the background generate observations that can potentially raise false alarms. This creates the problem of searching for a target in both additive white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP deals with the problem by using target-free training data to study this environment and build its characteristic covariance matrix. The data usually comes from range gates neighbouring the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this training data may not reflect the statistics of the CUT accurately, which justifies the need to develop SDS methods for radar detection. The maximum likelihood estimation detector (MLED) and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank STAP algorithms that eliminate the need for training data when mapping the statistics of the background interference. The work in this thesis is largely based on these two algorithms. The first work derives the optimal maximum likelihood (ML) solution to the target detection problem when the MLED and GMLED are used in a multistatic radar scenario. This application assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing the multistatic outcome to the target detection problem. It is shown that the derived MIMO detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations to the statistics of the multistatic MLED and GMLED are derived in order to provide a more in-depth analysis of the algorithms. The viability of the theoretical models and their approximations are tested against a numerical simulation of the systems. The second work focuses on the synchronisation of the spatio-temporal Doppler frequency data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands the idea to a form that could be implemented in a practical radar scenario. To reduce the information shared between the bistatic STAP channels, a data compression method is proposed that extracts the significant contributions of the MLED likelihood function before transmission. To perform the inter-channel synchronisation, the Doppler frequency data is projected into the space of potential target velocities where the multistatic likelihood is formed. Based on the expected structure of the velocity likelihood in the presence of a target, a modification to the multistatic MLED is proposed. It is demonstrated through numerical simulations that the proposed modified algorithm performs better than the basic multistatic MLED while having the benefit of reducing the data exchange in the MIMO radar system

Development and performance evaluation of a multistatic radar system

Multistatic radar systems are of emerging interest as they can exploit spatial diversity, enabling improved performance and new applications. Their development is being fuelled by advances in enabling technologies in such fields as communications and Digital Signal Processing (DSP). Such systems differ from typical modern active radar systems through consisting of multiple spatially diverse transmitter and receiver sites. Due to this spatial diversity, these systems present challenges in managing their operation as well as in usefully combining the multiple sources of information to give an output to the radar operator. In this work, a novel digital Commercial Off-The-Shelf (COTS) based coherent multistatic radar system designed at University College London, named ‘NetRad’, has been developed to produce some of the first published experimental results, investigating the challenges of operating such a system, and determining what level of performance might be achievable. Full detail of the various stages involved in the combination of data from the component transmitter-receiver pairs within a multistatic system is investigated, and many of the practical issues inherent are discussed. Simulation and subsequent experimental verification of several centralised and decentralised detection algorithms in terms of localisation (resolution and parameter estimation) of targets was undertaken. The computational cost of the DSP involved in multistatic data fusion is also considered. This gave a clear demonstration of several of the benefits of multistatic radar. Resolution of multiple targets that would have been unresolvable in a conventional monostatic system was shown. Targets were also shown to be plotted as two-dimensional vector position and velocities from use of time delay and Doppler shift information only. A range of targets were used including some such as walking people which were particularly challenging due to the variability of Radar Cross Section (RCS). Performance improvements were found to be dependant on the type of multistatic radar, method of data fusion and target characteristics in question. It is likely that future work will look to further explore the optimisation of multistatic radar for the various measures of performance identified and discussed in this work

Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications

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

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics