Waveform shaping

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Waveform shaping in electronics is the modification of the shape of an electronic waveform. It is in close connection with waveform diversity [1] and waveform design, [2] which are extensively studied in signal processing. Shaping the waveforms are of particular interest in active sensing (radar, sonar) for better detection performance, as well as communication schemes (CDMA, frequency hopping), and biology (for animal stimuli design).

Electronics physics, engineering, technology and applications that deal with the emission, flow and control of electrons in vacuum and matter

Electronics comprises the physics, engineering, technology and applications that deal with the emission, flow and control of electrons in vacuum and matter. The identification of the electron in 1897, along with the invention of the vacuum tube, which could amplify and rectify small electrical signals, inaugurated the field of electronics and the electron age.

Waveform the shape and form of a signal such as a wave moving in a physical medium or an abstract representation

In electronics, acoustics, and related fields, the waveform of a signal is the shape of its graph as a function of time, independent of its time and magnitude scales and of any displacement in time.

Signal processing models and analyzes data representations of physical events

Signal processing is a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements. For example, signal processing techniques are used to improve signal transmission fidelity, storage efficiency, and subjective quality, and to emphasize or detect components of interest in a measured signal.

See also Modulation, Pulse compression, Spread spectrum, Transmit diversity, Ambiguity function, Autocorrelation, and Cross-correlation.

In electronics and telecommunications, modulation is the process of varying one or more properties of a periodic waveform, called the carrier signal, with a modulating signal that typically contains information to be transmitted. Most radio systems in the 20th century used frequency modulation (FM) or amplitude modulation (AM) for radio broadcast.

Pulse compression is a signal processing technique commonly used by radar, sonar and echography to increase the range resolution as well as the signal to noise ratio. This is achieved by modulating the transmitted pulse and then correlating the received signal with the transmitted pulse.

Spread spectrum Spreading the frequency domain of a signal

In telecommunication and radio communication, spread-spectrum techniques are methods by which a signal generated with a particular bandwidth is deliberately spread in the frequency domain, resulting in a signal with a wider bandwidth. These techniques are used for a variety of reasons, including the establishment of secure communications, increasing resistance to natural interference, noise and jamming, to prevent detection, and to limit power flux density.

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Radar object detection system based on radio waves

Radar is a detection system that uses radio waves to determine the range, angle, or velocity of objects. It can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, weather formations, and terrain. A radar system consists of a transmitter producing electromagnetic waves in the radio or microwaves domain, a transmitting antenna, a receiving antenna and a receiver and processor to determine properties of the object(s). Radio waves from the transmitter reflect off the object and return to the receiver, giving information about the object's location and speed.

Multistatic radar

A multistatic radar system contains multiple spatially diverse monostatic radar or bistatic radar components with a shared area of coverage. An important distinction of systems based on these individual radar geometries is the added requirement for some level of data fusion to take place between component parts. The spatial diversity afforded by multistatic systems allows different aspects of a target to be viewed simultaneously. The potential for information gain can give rise to a number of advantages over conventional systems.

Synthetic-aperture radar Form of radar

Synthetic-aperture radar (SAR) is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR uses the motion of the radar antenna over a target region to provide finer spatial resolution than conventional beam-scanning radars. SAR is typically mounted on a moving platform, such as an aircraft or spacecraft, and has its origins in an advanced form of side looking airborne radar (SLAR). The distance the SAR device travels over a target in the time taken for the radar pulses to return to the antenna creates the large synthetic antenna aperture. Typically, the larger the aperture, the higher the image resolution will be, regardless of whether the aperture is physical or synthetic – this allows SAR to create high-resolution images with comparatively small physical antennas.

In pulsed radar and sonar signal processing, an ambiguity function is a two-dimensional function of time delay and Doppler frequency showing the distortion of a returned pulse due to the receiver matched filter due to the Doppler shift of the return from a moving target. The ambiguity function is determined by the properties of the pulse and the matched filter, and not any particular target scenario. Many definitions of the ambiguity function exist; Some are restricted to narrowband signals and others are suitable to describe the propagation delay and Doppler relationship of wideband signals. Often the definition of the ambiguity function is given as the magnitude squared of other definitions (Weiss). For a given complex baseband pulse , the narrowband ambiguity function is given by

Beamforming signal processing technique used in sensor arrays for directional signal transmission or reception

Beamforming or spatial filtering is a signal processing technique used in sensor arrays for directional signal transmission or reception. This is achieved by combining elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. The improvement compared with omnidirectional reception/transmission is known as the directivity of the array.

Passive radar systems encompass a class of radar systems that detect and track objects by processing reflections from non-cooperative sources of illumination in the environment, such as commercial broadcast and communications signals. It is a specific case of bistatic radar, the latter also including the exploitation of cooperative and non-cooperative radar transmitters.

In wireless communications, channel state information (CSI) refers to known channel properties of a communication link. This information describes how a signal propagates from the transmitter to the receiver and represents the combined effect of, for example, scattering, fading, and power decay with distance. The method is called Channel estimation. The CSI makes it possible to adapt transmissions to current channel conditions, which is crucial for achieving reliable communication with high data rates in multiantenna systems.

Carrier interferometry

Carrier Interferometry(CI) is a spread spectrum scheme designed to be used in an Orthogonal Frequency-Division Multiplexing (OFDM) communication system for multiplexing and multiple access, enabling the system to support multiple users at the same time over the same frequency band.

Active sensory systems are sensory receptors that are activated by probing the environment with self-generated energy. Examples include echolocation of bats and dolphins and insect antennae. Using self-generated energy allows more control over signal intensity, direction, timing and spectral characteristics. By contrast, passive sensory systems involve activation by ambient energy. For example, human vision relies on using light from the environment.

Lee Swindlehurst is an electrical engineer who has made contributions in sensor array signal processing for radar and wireless communications, detection and estimation theory, and system identification, and has received many awards in these areas. He is currently a Professor of Electrical Engineering and Computer Science at the University of California at Irvine.

Peter Stoica Swedish academic

Peter (Petre) Stoica is a researcher and educator in the field of signal processing and its applications to radar/sonar, communications and bio-medicine. He is a professor of Signal and System Modeling at Uppsala University in Sweden, and a Member of the Royal Swedish Academy of Engineering Sciences, the United States National Academy of Engineering (Foreign Member), the Romanian Academy, the European Academy of Sciences, and the Royal Society of Sciences. He is also a Fellow of IEEE, EURASIP, and the Royal Statistical Society.

Georgios B. Giannakis Greek–American computer scientist

Georgios B. Giannakis is a Greek–American Professor, engineer, and inventor. At present he is an Endowed Chair Professor of Wireless Telecommunications with the Department of Electrical and Computer Engineering, and Director of the Digital Technology Center at the University of Minnesota.

Sergio Barbarossa researcher

Sergio Barbarossa is an Italian professor, engineer and inventor. He is a professor at Sapienza University of Rome, Italy.

MIMO radar

MIMO radar is an advanced type of phased array radar employing digital receivers and waveform generators distributed across the aperture. MIMO radar signals propagate in a fashion similar to Multistatic radar. However, instead of distributing the radar elements throughout the surveillance area, antennas are closely located to obtain better spatial resolution, Doppler resolution, and dynamic range. MIMO radar may also be used to obtain Low-probability-of-intercept radar properties.

SAMV is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing. The name was coined in 2013 to emphasize its basis on the asymptotically minimum variance (AMV) criterion. It is a powerful tool for the recovery of both the amplitude and frequency characteristics of multiple highly correlated sources in challenging environment (e.g., limited number of snapshots, low signal-to-noise ratio. Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging.

Electromagnetic radio frequency convergence

Electromagnetic radio frequency (RF) convergence is a signal-processing paradigm that is utilized when several RF systems have to share a finite amount of resources among each other. RF convergence indicates the ideal operating point for the entire network of RF systems sharing resources such that the systems can efficiently share resources in a manner that’s mutually beneficial. With communications spectral congestion recently becoming an increasingly important issue for the telecommunications sector, researchers have begun studying methods of achieving RF convergence for cooperative spectrum sharing between remote sensing systems and communications systems. Consequentially, RF convergence is commonly referred to as the operating point of a remote sensing and communications network at which spectral resources are jointly shared by all nodes of the network in a mutually beneficial manner. Remote sensing and communications have conflicting requirements and functionality. Furthermore, spectrum sharing approaches between remote sensing and communications have traditionally been to separate or isolate both systems. Hence, achieving RF convergence can be an incredibly complex and difficult problem to solve. Even for a simple network consisting of one remote sensing and communications system each, there are several independent factors in the time, space, and frequency domains that have to be taken into consideration in order to determine the optimal method to share spectral resources. For a given spectrum-space-time, a real network of systems will have many more sources or systems present, making the problem of achieving RF convergence even more complex.

Jian Li (engineer) engineer

Jian Li is a researcher and educator in the field of statistical and array signal processing and its applications to radar, sonar, communications and medical imaging. She is a Professor in the Department of Electrical and Computer Engineering at the University of Florida.

References

  1. http://signal.ese.wustl.edu/MURI/publications/spm_2.pdf
  2. http://www.sal.ufl.edu/book/