KEYWORDS: Tunable filters, Modulation, Wavelets, Signal to noise ratio, Orthogonal frequency division multiplexing, Antennas, Telecommunications, Radar, Signal filtering, RF communications
Radio frequency (RF) communication and radar applications in the low frequency ranges (3 Hz – 300 kHz ELF-LF range and beyond) form an important subclass of RF use cases that have to utilize waveforms that are inherently limited in bandwidth and hence information throughput capability. Thus, the challenge is maximizing the performance of suitable classes of waveforms in terms of throughput, noise and interference suppression, power and practical realizability. In this paper, we summarize the limitations of the traditional approach using sinusoidal waveforms, and briefly describe alternative and modified approaches using OFDM, wavelets, filter bank-based methods and optimized prolate spheroidal waveforms.
KEYWORDS: Receivers, Digital filtering, Electronic filtering, Frequency conversion, Analog electronics, Linear filtering, Digital signal processing, Signal processing, Image processing, Digital image processing
Software defined radio (SDR) hardware platform is in high demand for ultra-wideband digital EW receiver to carry
out different mission requirements. Due to the limitations of current Analog-to-Digital conversion (ADC)
techniques, the ideal receiver structure of SDR, with digital RF frequency conversion, cannot be achieved. In this
article, a new channelization technique called ADC polyphase fast Fourier transformation (ADC PFFT) filter bank
channelization is demonstrated. The key concept is to separate the speed at which the two functional units of an
ADC - the sample and hold and the quantizer - operate. The sample and hold unit operates at the sampling
frequency fs and the quantizer (the speed limiting factor in ADCs) can operate at a much slower rate, fs/M, where M
is the decimation factor for digital filter bank. By integrated this ADC PFFT technique in ultra-wideband digital
channelized EW receivers, directly digital RF down conversion can be achieved. With the ADC PFFT
channelization for RF down conversion and polyphase FFT channelization for IF down conversion, 2-18 GHz
frequency coverage can be accomplished in such ultra-wideband digital channelized EW receivers without the
requirement of EW receivers being time-shared among outputs from many subbands due to bandwidth limitation in
digital IF channelized EW receivers. Because the frequency down conversion from RF to BB are all processed
digitally, issues such as image rejection and I/Q imbalance due to analog mixing will be eliminated in the ultrawideband
digital channelized EW receivers.
With the advent of a new sampling theory in recent years, compressed sensing (CS), it is possible to reconstruct signals
from measurements far below the Nyquist rate. The CS theory assumes that signals are sparse and that measurement
matrices satisfy certain conditions. Even though there have been many promising results, unfortunately there still exists a
gap between the theory and actual real world applications. This is because of the fundamental problem that the CS
formulation is discrete. We propose a sampling and reconstructing method for frequency-sparse signals that addresses
this issue. The signals in our scenario are supported in a continuous sparsifying domain rather than discrete. This work
focuses on a typical case in which the unknowns are frequencies and amplitudes. However, directly looking for the
unknowns that best fit the measurements in the least-squares sense is a non-convex optimization problem, because
sinusoids are oscillatory. Our approach extends the utility of CS to simplify this problem to a locally convex problem,
hence making the solutions tractable. Direct measurements are taken from non-uniform time-samples, which is an
extension of the CS problem with a subsampled Fourier matrix. The proposed reconstruction algorithm iteratively
approximates the solutions using CS and then accurately solves for the frequencies with Newton's method and for the
amplitudes with linear least squares solutions. Our simulations show success in accurate reconstruction of signals with
arbitrary frequencies and significantly outperform current spectral compressed sensing methods in terms of
reconstruction fidelity for both noise-free and noisy cases.
A new scheme for sampling and detecting as well as reconstructing analog signals residing in a wide spectrum band
through compressive sensing is proposed. By applying compressive sensing techniques, this scheme is able to detect
signals quickly over very wide bandwidth. Unlike existing compressive sensing approaches which carry out the
sampling and detection/reconstruction procedures separately, in this scheme, the sampling process and the
detection/reconstruction process has close interaction. Once a new signal is detected, resources are allocated for
further processing. Previously detected signals are then removed as interference to facilitate new signal detection.
Hence, this scheme can quickly adapt to the variation of the environment. Moreover, it avoids the complex iterative
optimization procedure in reconstruction and instead uses one step detection procedure to reaches real-time handling
of signals. Simulation results show that it can closely track the spectrum change even when the signal is weak.
Data aggregation in sensor networks refers to the task of accumulating sensed data at appropriate fusion centers within the network and making inferences based on the received information. This paper discusses methodologies for efficiently choosing these data aggregation centers or nodes within the wireless sensor network. We consider a plurality of sensor nodes that are deployed in a harsh terrain that consists of opaque obstructions. By making some simplifying assumptions with regards to the geometry of the region in which the nodes are deployed (domain) and some system parameters such as the positions of the individual nodes and the loading on those nodes, we show that the choice of the node best located to serve as a data aggregation center can be formulated as a convex optimization problem. Our simplest formulation is a linear program (LP) that computes the geographic of the nodes within a domain. Similar optimization formulations are then developed for more detailed scenarios. We also describe an implementation methodology for choosing the optimal aggregation centers. Simulation results show that by implementing our algorithms we see a 10-15% improvement in the achieved throughput per domain over methods that randomly select nodes to operate as the data fusion center. A corresponding improvement in the latency is also noted. We consider more intricate cases of the problem wherein, factors such as load and message priorities are taken into consideration when determining the optimal fusion center.
Conference Committee Involvement (1)
Cyber Sensing 2012
24 April 2012 | Baltimore, Maryland, United States
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