71 research outputs found
QoS-Aware Precoder Optimization for Radar Sensing and Multiuser Communications Under Per-Antenna Power Constraints
In this work, we concentrate on designing the precoder for the multiple-input multiple-output (MIMO) dual functional radar-communication (DFRC) system, where the dual-functional waveform is designed for performing multiuser downlink transmission and radar sensing simultaneously. Specifically, considering the signal-independent interference and signal-dependent clutter, we investigate the optimization of transmit precoding for maximizing the sensing signal-to-interference-plus-noise ratio (SINR) at the radar receiver under the constraint of the minimum SINR received at multiple communication users and per-antenna power budget. The formulated problem is challenging to solve due to the nonconovex objective function and nonconvex per-antenna power constraint. In particular, for the signal-independent interference case, we propose a distance-majorization induced algorithm to approximate the nonconvex problem as a sequence of convex problems whose optima can be obtained in closed form. Subsequently, our complexity analysis shows that our proposed algorithm has a much lower computational complexity than the widely-adopted semidefinite relaxation (SDR)-based algorithm. For the signal-dependent clutter case, we employ the fractional programming to transform the nonconvex problem into a sequence of subproblems, and then we propose a distance-majorization based algorithm to obtain the solution of each subproblem in closed form. Finally, simulation results confirm the performance superiority of our proposed algorithms when compared with the SDR-based approach. In conclusion, the novelty of this work is to propose an efficient algorithm for handling the typical problem in designing the DFRC precoder, which achieves better performance with a much lower complexity than the state-of-the-art algorithm
Mismatched Processing for Radar Interference Cancellation
Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets
ECOS 2012
The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology
Sparsity-based algorithms for inverse problems
Inverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and engineering. Inverse problems are often ill-posed, which means that the observations of the system do not uniquely define the parameters we seek to estimate, or that the solution is highly sensitive to small changes in the observation. In order to solve such problems, therefore, we need to make use of additional knowledge about the system at hand. One such prior information is given by the notion of sparsity. Sparsity refers to the knowledge that the solution to the inverse problem can be expressed as a combination of a few terms. The sparsity of a solution can be controlled explicitly or implicitly. An explicit way to induce sparsity is to minimize the number of non-zero terms in the solution. Implicit use of sparsity can be made, for e.g., by making adjustments to the algorithm used to arrive at the solution.In this thesis we studied various inverse problems that arise in different application areas, such as tomographic imaging and equation learning for biology, and showed how ideas of sparsity can be used in each case to design effective algorithms to solve such problems.Financial support was provided by the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie grant agreement no.~765604Number theory, Algebra and Geometr
Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques
This thesis presents a study into nonlinearities in the radar receiver and investigates
advanced digital signal processing (DSP) techniques capable of mitigating the resultant
deleterious effects. The need for these mitigation techniques has become more prevalent
as the use of commercial radar sensors has increased rapidly over the last decade. While
advancements in low-cost radio frequency (RF) technologies have made mass-produced
radar systems more feasible, they also pose a significant risk to the functionality of the
sensor. One of the major compromises when employing low-cost commercial off-theshelf
(COTS) components in the radar receiver is system linearity. This linearity trade-off
leaves the radar susceptible to interfering signals as the RF receiver can now be driven
into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear
regime as distortion is observed in the radar output if they do. If radars are to maintain
operational performance in an RF environment that is becoming increasingly crowded,
novel techniques that allow the sensor to operate in the nonlinear regime must be developed.
Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear
receiver problem in modern radar. While there is very little work published on this topic
in the radar literature, inspiration can be taken from the related field of communications
where techniques have been successfully employed.
It is clear from the communications literature that for any mitigation algorithm to be
successful, the mechanisms driving the nonlinear distortion in the receiver must be understood
in great detail. Therefore, a behavioural modelling technique capable of capturing
both the nonlinear amplitude and phase effects in the radar receiver is presented before
any mitigation techniques are studied. Two distinct groups of mitigation algorithms
are then developed specifically for radar systems with their performance tested in the
medium pulse repetition frequency (MPRF) mode of operation. The first of these is the
look-up table (LUT) approach which has the benefit of being mode independent and computationally
inexpensive to implement. The limitations of this communications-based
technique are discussed with particular emphasis placed on its performance against receiver
nonlinearities that exhibit complex nonlinear memory effects. The second group
of mitigation algorithms to be developed is the forward modelling technique. While this
novel technique is both mode dependent and computationally intensive to implement,
it has a unique formalisation that allows it to be extended to include nonlinear memory
effects in a well-defined manner. The performance of this forward modelling technique
is analysed and discussed in detail.
It was shown in this study that nonlinearities generated in the radar receiver can be
successfully mitigated using advanced DSP techniques. For this to be the case however,
the behaviour of the RF receiver must be characterised to a high degree of accuracy both
in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects
are significant in the radar receiver, it was shown that memoryless mitigation techniques
can become decorrelated drastically reducing their effectiveness. Importantly however, it
was demonstrated that the LUT and forward modelling techniques can both be extended
to compensate for complex nonlinear memory effects generated in the RF receiver. It was
also found that the forward modelling technique dealt with the nonlinear memory effects
in a far more robust manner than the LUT approach leading to a superior mitigation
performance in the memory rich case
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
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