Anti-Jam GPS Controlled Reception Pattern Antennas for Man-Portable Applications

Military GPS receivers provide crucial information to soldiers in the field, however, the performance of these devices is degraded by in band RF interference, making GPS susceptible to jamming. Anti-jam techniques for aircraft and vehicular platforms have been developed, but at present there is no system for dismounted soldiers. There is a need for an anti-jam system which meets the demands of a dismounted soldier and conforms to the size, weight, and power requirements of a portable device. A controlled reception pattern antenna, or CRPA, is a potential solution for jammer mitigation. These devices work by steering reception pattern nulls toward the jammer direction, reducing the jammer power which reaches the GPS receiver. Prior CRPA realizations have been designed for use on vehicular and aircraft applications, however, these platforms do not suffer from the same limitations as a man-portable CRPA. Three considerations which are more pertinent for man-portable designs than prior work are (i) distributed antenna element positions and orientations dynamically change during use changing the reception pattern characteristics, (ii) the user is lower to the ground and moves through the environment meaning that multipath propagation can have a greater effect on CRPA performance, and (iii) the size weight and power constraints for a portable system limit the number of antenna elements reducing the degrees of freedom that can be used for cancellation. To address these challenges, a framework for man-portable CRPA modeling is presented. This includes development of efficient modeling methods which enable investigations into element perturbations to address the dynamic orientation problem. These and other methods are presented in Chapter 3, along with a discussion of the relative strengths and weaknesses of each. Additionally, a mixed scattering channel model is applied to the CRPA reception patterns, combining diffuse and specular reflection in Chapter 4. Discussion of this model centers around the eigenvalues of the signal covariance matrix and the effect of coherence between multipath components. Following this, Chapter 5 examines the performance of polarimetric CRPAs and space-time adaptive processing for man-portable CRPAs with limited degrees of freedom

Adaptive beamforming and switching in smart antenna systems

The ever increasing requirement for providing large bandwidth and seamless data access to commuters has prompted new challenges to wireless solution providers. The communication channel characteristics between mobile clients and base station change rapidly with the increasing traveling speed of vehicles. Smart antenna systems with adaptive beamforming and switching technology is the key component to tackle the challenges. As a spatial filter, beamformer has long been widely used in wireless communication, radar, acoustics, medical imaging systems to enhance the received signal from a particular looking direction while suppressing noise and interference from other directions. The adaptive beamforming algorithm provides the capability to track the varying nature of the communication channel characteristics. However, the conventional adaptive beamformer assumes that the Direction of Arrival (DOA) of the signal of interest changes slowly, although the interference direction could be changed dynamically. The proliferation of High Speed Rail (HSR) and seamless wireless communication between infrastructure ( roadside, trackside equipment) and the vehicles (train, car, boat etc.) brings a unique challenge for adaptive beamforming due to its rapid change of DOA. For a HSR train with 250km/h, the DOA change speed can be up to 4⁰ per millisecond. To address these unique challenges, faster algorithms to calculate the beamforming weight based on the rapid-changing DOA are needed. In this dissertation, two strategies are adopted to address the challenges. The first one is to improve the weight calculation speed. The second strategy is to improve the speed of DOA estimation for the impinging signal by leveraging on the predefined constrained route for the transportation market. Based on these concepts, various algorithms in beampattern generation and adaptive weight control are evaluated and investigated in this thesis. The well known Generalized Sidelobe Cancellation (GSC) architecture is adopted in this dissertation. But it faces serious signal cancellation problem when the estimated DOA deviates from the actual DOA which is severe in high mobility scenarios as in the transportation market. Algorithms to improve various parts of the GSC are proposed in this dissertation. Firstly, a Cyclic Variable Step Size (CVSS) algorithm for adjusting the Least Mean Square (LMS) step size with simplicity for implementation is proposed and evaluated. Secondly, a Kalman filter based solution to fuse different sensor information for a faster estimation and tracking of the DOA is investigated and proposed. Thirdly, to address the DOA mismatch issue caused by the rapid DOA change, a fast blocking matrix generation algorithm named Simplifized Zero Placement Algorithm (SZPA) is proposed to mitigate the signal cancellation in GSC. Fourthly, to make the beam pattern robust against DOA mismatch, a fast algorithm for the generation of at beam pattern named Zero Placement Flat Top (ZPFT) for the fixed beamforming path in GSC is proposed. Finally, to evaluate the effectiveness and performance of the beamforming algorithms, wireless channel simulation is needed. One of the challenging aspects for wireless simulation is the coupling between Probability Density Function (PDF) and Power Spectral Density (PSD) for a random variable. In this regard, a simplified solution to simulate Non Gaussian wireless channel is proposed, proved and evaluated for the effectiveness of the algorithm. With the above optimizations, the controlled simulation shows that the at top beampattern can be generated 380 times faster than iterative optimization method and blocking matrix can be generated 9 times faster than normal SVD method while the same overall optimum state performance can be achieved

Development of a Resource Manager Framework for Adaptive Beamformer Selection

Adaptive digital beamforming (DBF) algorithms are designed to mitigate the effects of interference and noise in the electromagnetic (EM) environment encountered by modern electronic support (ES) receivers. Traditionally, an ES receiver employs a single adaptive DBF algorithm that is part of the design of the receiver system. While the traditional form of receiver implementation is effective in many scenarios it has inherent limitations. This dissertation proposes a new ES receiver framework capable of overcoming the limitations of traditional ES receivers. The proposed receiver framework is capable of forming multiple, independent, simultaneous adaptive digital beams toward multiple signals of interest in an electromagnetic environment. The main contribution of the research is the development, validation, and verification of a resource manager (RM) algorithm. The RM estimates a set of parameters that characterizes the electromagnetic environment and selects an adaptive digital beam forming DBF algorithm for implementation toward all each signal of interest (SOI) in the environment. Adaptive DBF algorithms are chosen by the RM based upon their signal to interference plus noise ratio (SINR) improvement ratio and their computational complexity. The proposed receiver framework is demonstrated to correctly estimate the desired electromagnetic parameters and select an adaptive DBF from the LUT

Temporal and Spatial Interference Mitigation Strategies to Improve Radar Data Quality

The microwave band is well suited to wireless applications, including radar, communications, and electronic warfare. While radar operations currently have priority in a portion of the microwave band, wireless companies are lobbying to change that; such a change would force current operators into a smaller total bandwidth. Interference would occur, and has already occurred at the former National Weather Radar Testbed Phased Array Radar. The research in this dissertation was motivated by this interference --- it occurred even without a change to radar's primacy in the microwave band. If microwave operations had to squeeze into a smaller overall bandwidth, such interference, whether originating from other radars or some other source, would only become more common. The radio frequency interference (RFI) present at the National Weather Radar Testbed Phased Array Radar altered the statistical properties at certain locations, causing targets to be erroneously detected. While harmless enough in clear air, it could affect National Weather Service decisions if it occurred during a weather event. The initial experiments, covered in Chapter 2, used data comprised of a single channel of in-phase and quadrature (IQ) data, reflecting the resources available to the National Weather Service's weather radar surveillance network. A new algorithm, the Interference Spike Detection Algorithm, was developed with these restrictions in mind. This new algorithm outperforms several interference detection algorithms developed by industry. Tests on this data examined algorithm performance quantitatively, using real and simulated weather data and radio frequency interference. Additionally, machine learning classification algorithms were employed for the first time to the RFI classification problem and it was found that, given enough resources, machine learning had the potential to perform even better than the other temporal algorithms. Subsequent experiments, covered in Chapter 3, used spatial data from phased arrays and looked at methods of interference mitigation that leveraged this spatial data. Specifically, adaptive beamforming techniques could be used to mitigate interference and improve data quality. A variety of adaptive digital beamforming techniques were evaluated in terms of their performance at interference mitigation for a communications task. Additionally, weather radar data contaminated with ground clutter was collected from the sidelobe canceller channels of the former National Weather Radar Testbed Phased Array Radar and, using the reasoning that ground clutter is simply interference from the ground, adaptive digital beamforming was successfully employed to mitigate the impact of ground clutter and restore the data to reflect the statistics of the underlying weather data. Tests on digital equalization, covered in Chapter 4, used data from a prototype receiver for Horus, a digital phased array radar under development at the University of Oklahoma. The data suffered from significant channel mismatch, which can severely negatively impact the performance of phased arrays. Equalization, implemented both via older digital filter design methods and, for the first time, via newer machine learning regression methods, was able to improve channel matching. When used before adaptive digital beamforming, it was found that digital equalization always improved system performance

An Adaptive Approach for the Joint Antenna Selection and Beamforming Optimization

Adaptive beamforming techniques are widely known for their capability of leveraging the performance of antenna arrays. The effectiveness of such techniques typically increases as the number of antennas grows. In contrast, computational and hardware costs very often limit the deployment of beamforming in large-scale arrays. To circumvent this problem, antenna selection strategies have been developed aiming to maintain much of the performance gain obtained by using a large array while keeping computational and hardware costs at acceptable levels. In this context, the present paper is dedicated to the development of two new adaptive algorithms for solving the problem of joint antenna selection and beamforming for uplink reception in mobile communication systems. Both algorithms are based on an alternating optimization strategy and are designed to operate with a limited number of radio-frequency chains. The main difference between the proposed algorithms is that the first is formulated by considering the minimum mean-square error (MMSE) criterion, while the second is based on the minimum-variance distortionless-response (MVDR) approach. The numerical simulation results confirm the effectiveness of the proposed algorithms

Robust adaptive filtering algorithms for system identification and array signal processing in non-Gaussian environment

This dissertation proposes four new algorithms based on fractionally lower order statistics for adaptive filtering in a non-Gaussian interference environment. One is the affine projection sign algorithm (APSA) based on L₁ norm minimization, which combines the ability of decorrelating colored input and suppressing divergence when an outlier occurs. The second one is the variable-step-size normalized sign algorithm (VSS-NSA), which adjusts its step size automatically by matching the L₁ norm of the a posteriori error to that of noise. The third one adopts the same variable-step-size scheme but extends L₁ minimization to Lp minimization and the variable step-size normalized fractionally lower-order moment (VSS-NFLOM) algorithms are generalized. Instead of variable step size, the variable order is another trial to facilitate adaptive algorithms where no a priori statistics are available, which leads to the variable-order least mean pth norm (VO-LMP) algorithm, as the fourth one. These algorithms are applied to system identification for impulsive interference suppression, echo cancelation, and noise reduction. They are also applied to a phased array radar system with space-time adaptive processing (beamforming) to combat heavy-tailed non-Gaussian clutters. The proposed algorithms are tested by extensive computer simulations. The results demonstrate significant performance improvements in terms of convergence rate, steady-state error, computational simplicity, and robustness against impulsive noise and interference --Abstract, page iv