201 research outputs found
Knowledge-Aided STAP Using Low Rank and Geometry Properties
This paper presents knowledge-aided space-time adaptive processing (KA-STAP)
algorithms that exploit the low-rank dominant clutter and the array geometry
properties (LRGP) for airborne radar applications. The core idea is to exploit
the fact that the clutter subspace is only determined by the space-time
steering vectors,
{red}{where the Gram-Schmidt orthogonalization approach is employed to
compute the clutter subspace. Specifically, for a side-looking uniformly spaced
linear array, the} algorithm firstly selects a group of linearly independent
space-time steering vectors using LRGP that can represent the clutter subspace.
By performing the Gram-Schmidt orthogonalization procedure, the orthogonal
bases of the clutter subspace are obtained, followed by two approaches to
compute the STAP filter weights. To overcome the performance degradation caused
by the non-ideal effects, a KA-STAP algorithm that combines the covariance
matrix taper (CMT) is proposed. For practical applications, a reduced-dimension
version of the proposed KA-STAP algorithm is also developed. The simulation
results illustrate the effectiveness of our proposed algorithms, and show that
the proposed algorithms converge rapidly and provide a SINR improvement over
existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic
Systems, 201
Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain
A significant challenge in autonomous driving systems lies in image
understanding within complex environments, particularly dense traffic
scenarios. An effective solution to this challenge involves removing the
background or static objects from the scene, so as to enhance the detection of
moving targets as key component of improving overall system performance. In
this paper, we present an efficient algorithm for background removal in
automotive radar applications, specifically utilizing a frequency-modulated
continuous wave (FMCW) radar. Our proposed algorithm follows a three-step
approach, encompassing radar signal preprocessing, three-dimensional (3D)
ego-motion estimation, and notch filter-based background removal in the
azimuth-elevation-Doppler domain. To begin, we model the received signal of the
FMCW multiple-input multiple-output (MIMO) radar and develop a signal
processing framework for extracting four-dimensional (4D) point clouds.
Subsequently, we introduce a robust 3D ego-motion estimation algorithm that
accurately estimates radar ego-motion speed, accounting for Doppler ambiguity,
by processing the point clouds. Additionally, our algorithm leverages the
relationship between Doppler velocity, azimuth angle, elevation angle, and
radar ego-motion speed to identify the spectrum belonging to background
clutter. Subsequently, we employ notch filters to effectively filter out the
background clutter. The performance of our algorithm is evaluated using both
simulated data and extensive experiments with real-world data. The results
demonstrate its effectiveness in efficiently removing background clutter and
enhacing perception within complex environments. By offering a fast and
computationally efficient solution, our approach effectively addresses
challenges posed by non-homogeneous environments and real-time processing
requirements
Different Approaches of Numerical Analysis of Electromagnetic Phenomena in Shaded Pole Motor with Application of Finite Elements Method
In this paper is used Finite Element Method-FEM
for analysis of electromagnetic quantities of small micro motor –
single phase shaded pole motor-SPSPM. FEM is widely used
numerical method for solving nonlinear partial differential
equations with variable coefficients. For that purpose motor
model is developed with exact geometry and material’s
characteristics. Two different approaches are applied in FEM
analysis of electromagnetic phenomena inside the motor:
magneto-static where all electromagnetic quantities are analysed
in exact moment of time meaning frequency f=0 Hz and timeharmonic
magnetic approach where the magnetic field inside the
machine is time varying, meaning frequency f=50 Hz. Obtained
results are presented and compared with available analytical
result
Sparsity based methods for target localization in multi-sensor radar
In this dissertation, several sparsity-based methods for ground moving target indicator (GMTI) radar with multiple-input multiple-output (MIMO) random arrays are proposed. MIMO random arrays are large arrays that employ multiple transmitters and receivers, the positions of the transmitters and the receivers are randomly chosen. Since the resolution of the array depends on the size of the array, MIMO random arrays obtain a high resolution. However, since the positions of the sensors are randomly chosen, the array suffers from large sidelobes which may lead to an increased false alarm probability. The number of sensors of a MIMO random array required to maintain a certain level of peak sidelobes is studied. It is shown that the number of sensors scales with the logarithm of the array aperture, in contrast with a ULA where the number of elements scales linearly with the array aperture. The problem of sparse target detection given space-time observations from MIMO random arrays is presented. The observations are obtained in the presence of Gaussian colored noise of unknown covariance matrix, but for which secondary data is available for its estimation. To solve the detection problem two sparsity-based algorithms, the MP-STAP and the MBMP-STAP algorithms are proposed that utilizes knowledge of the upper bound on the number of targets. A constant false alarm rate (CFAR) sparsity based detector that does not utilize any information on the number of targets referred to as MP-CFAR and MBMP-CFAR are also developed. A performance analysis for the new CFAR detector is also derived, the metrics used to describe the performance of the detector are the probability of false alarm and the probability of detection. A grid refinement procedure is also proposed to eliminate the need for a dense grid which would increase the computational complexity significantly. Expressions for the computational complexity of the proposed CFAR detectors are derived. It is shown that the proposed CFAR detectors outperforms the popular adaptive beamformer at a modest increase in computational complexity
Microwave Sensing and Imaging
In recent years, microwave sensing and imaging have acquired an ever-growing importance in several applicative fields, such as non-destructive evaluations in industry and civil engineering, subsurface prospection, security, and biomedical imaging. Indeed, microwave techniques allow, in principle, for information to be obtained directly regarding the physical parameters of the inspected targets (dielectric properties, shape, etc.) by using safe electromagnetic radiations and cost-effective systems. Consequently, a great deal of research activity has recently been devoted to the development of efficient/reliable measurement systems, which are effective data processing algorithms that can be used to solve the underlying electromagnetic inverse scattering problem, and efficient forward solvers to model electromagnetic interactions. Within this framework, this Special Issue aims to provide some insights into recent microwave sensing and imaging systems and techniques
Waveform Diversity and Range-Coupled Adaptive Radar Signal Processing
Waveform diversity may offer several benefits to radar systems though often at the cost of reduced sensitivity. Multi-dimensional processing schemes are known to offer many degrees of freedom, which can be exploited to suppress the ambiguity inherent to pulse compression, array processing, and Doppler frequency estimation. Spatial waveform diversity can be achieved by transmitting different but correlated waveforms from each element of an antenna array. A simple yet effective scheme is employed to transmit different waveforms in different spatial directions. A new reiterative minimum mean squared error approach entitled Space-Range Adaptive Processing, which adapts simultaneously in range and angle, is derived and shown in simulation to offer enhanced performance when spatial waveform diversity is employed relative to both conventional matched filtering and sequentially adapting in angle and then range. The same mathematical framework is utilized to develop Time-Range Adaptive Processing (TRAP) algorithm which is capable of simultaneously adapting in Doppler frequency and range. TRAP is useful when pulse-to-pulse changing of the center frequency or waveform coding is used to achieve enhanced range resolution or unambiguous ranging, respectively. The inherent computational complexity of the new multi-dimensional algorithms is addressed by segmenting the full-dimension cost functions, yielding a reduced-dimensional variants of each. Finally, a non-adaptive approach based on the multi-dimensional TRAP signal model is utilized to develop an efficient clutter cancellation technique capable of suppressing multiple range intervals of clutter when waveform diversity is applied to pulse-Doppler radar
Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array
To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV)
emitter in future wireless networks, a low-complexity direction of arrival
(DOA) estimation architecture for massive multiple input multiple output (MIMO)
receiver arrays is constructed. In this paper, we propose two strategies to
address the extremely high complexity caused by eigenvalue decomposition of the
received signal covariance matrix. Firstly, a rapid power-iterative rotational
invariance (RPI-RI) method is proposed, which adopts the signal subspace
generated by power iteration to gets the final direction estimation through
rotational invariance between subarrays. RPI-RI makes a significant complexity
reduction at the cost of a substantial performance loss. In order to further
reduce the complexity and provide a good directional measurement result, a
rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which
utilizes the noise subspace combined with polynomial solution method to get the
optimal direction estimation. In addition, the influence of initial vector
selection on convergence in the power iteration is analyzed, especially when
the initial vector is orthogonal to the incident wave. Simulation results show
that the two proposed methods outperform the conventional DOA estimation
methods in terms of computational complexity. In particular, the RPIPR method
achieves more than two orders of magnitude lower complexity than conventional
methods and achieves performance close to CRLB. Moreover, it is verified that
the initial vector and the relative error have a significant impact on the
performance of the computational complexity
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