5,915 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
Frequency-modulated continuous-wave LiDAR compressive depth-mapping
We present an inexpensive architecture for converting a frequency-modulated
continuous-wave LiDAR system into a compressive-sensing based depth-mapping
camera. Instead of raster scanning to obtain depth-maps, compressive sensing is
used to significantly reduce the number of measurements. Ideally, our approach
requires two difference detectors. % but can operate with only one at the cost
of doubling the number of measurments. Due to the large flux entering the
detectors, the signal amplification from heterodyne detection, and the effects
of background subtraction from compressive sensing, the system can obtain
higher signal-to-noise ratios over detector-array based schemes while scanning
a scene faster than is possible through raster-scanning. %Moreover, we show how
a single total-variation minimization and two fast least-squares minimizations,
instead of a single complex nonlinear minimization, can efficiently recover
high-resolution depth-maps with minimal computational overhead. Moreover, by
efficiently storing only data points from measurements of an
pixel scene, we can easily extract depths by solving only two linear equations
with efficient convex-optimization methods
Multiuser Millimeter Wave Beamforming Strategies with Quantized and Statistical CSIT
To alleviate the high cost of hardware in mmWave systems, hybrid
analog/digital precoding is typically employed. In the conventional two-stage
feedback scheme, the analog beamformer is determined by beam search and
feedback to maximize the desired signal power of each user. The digital
precoder is designed based on quantization and feedback of effective channel to
mitigate multiuser interference. Alternatively, we propose a one-stage feedback
scheme which effectively reduces the complexity of the signalling and feedback
procedure. Specifically, the second-order channel statistics are leveraged to
design digital precoder for interference mitigation while all feedback overhead
is reserved for precise analog beamforming. Under a fixed total feedback
constraint, we investigate the conditions under which the one-stage feedback
scheme outperforms the conventional two-stage counterpart. Moreover, a rate
splitting (RS) transmission strategy is introduced to further tackle the
multiuser interference and enhance the rate performance. Consider (1) RS
precoded by the one-stage feedback scheme and (2) conventional transmission
strategy precoded by the two-stage scheme with the same first-stage feedback as
(1) and also certain amount of extra second-stage feedback. We show that (1)
can achieve a sum rate comparable to that of (2). Hence, RS enables remarkable
saving in the second-stage training and feedback overhead.Comment: submitted to TW
White noise reduction for wideband linear array signal processing
The performance of wideband array signal processing algorithms is dependent on the noise level in the system. A method is
proposed for reducing the level of white noise in wideband linear arrays via a judiciously designed spatial transformation followed
by a bank of highpass filters. A detailed analysis of the method and its effect on the spectrum of the signal and noise is presented.
The reduced noise level leads to a higher signal to noise ratio (SNR) for the system, which can have a significant beneficial
effect on the performance of various beamforming methods and other array signal processing applications such as direction of
arrival (DOA) estimation. Here we focus on the beamforming problem and study the improved performance of two well-known
beamformers, namely the reference signal based (RSB) and the linearly constrained minimum variance (LCMV) beamformers.
Both theoretical analysis and simulation results are provided
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