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MTI and Pulse Doppler Radars
Published in Habibur Rahman, Fundamental Principles of Radar, 2019
Relative velocity between the target and the radar creates a Doppler shift of the transmitted frequency. It was shown previously that this Doppler shift is proportional to the radial speed of the target. Thus a measurement of Doppler frequency affords a means of measuring radial speed, which is more accurate than other methods. In addition, the Doppler shift can be used in radar system applications for several advantages that include separating desired target returns from those of fixed targets, and extracting information concerning radial velocity of the target. A pulse radar that utilizes such advantages is called a moving target indication (MTI) or pulse Doppler radar. The physical principles of both these radars are the same but they differ in their mode of operation. For instance, the MTI radar operates on low pulse repetition frequencies, and uses a delay line canceller filter to isolate moving targets from stationary targets, thus causing ambiguous Doppler velocity but unambiguous range measurements. On the other hand, the pulse Doppler radar operates on high pulse repetition frequency, and is the one in which the Doppler measurement is unambiguous but the range measurement can be either ambiguous or unambiguous, and the Doppler data are extracted by the range gates and Doppler filters.
Development of a Real-Time Electro-Optical Reconnaissance System
Published in David R. Martinez, Robert A. Bond, Vai M. Michael, High Performance Embedded Computing Handbook, 2018
In addition, moving targets also may appear as outlier points. This fact is illustrated in Figure 23-10. Since there is no range information in the video information, there is no way to discriminate between a static target at one range and a moving target at different range. The fact that moving targets show up as outliers provides one mechanism for performing moving-target indication (MTI). If the amount of vertical displacement can be determined, then this may be used to estimate the velocity of the target,νT, () vT=vAΔhh+Δh,
Digital Interperiod Signal Processing Algorithms
Published in Vyacheslav Tuzlukov, Signal Processing in Radar Systems, 2017
The main element of the digital moving-target indicator is the digital rejector filter that can guarantee a cancellation of correlated passive interference. In the simplest case, the digital rejector filter is constructed in the form of the filter with the v-fold (v < N) interperiod subtraction that corresponds to the structure of the nonrecursive filter. The recursive filters are also widely used for the cancellation of passive interferences. Consider briefly the problems associated with the analysis and synthesis of rejector filters.
A comparison of multi temporal interferometry techniques for landslide susceptibility assessment in urban area: an example on stigliano (MT), a town of Southern of Italy
Published in Geomatics, Natural Hazards and Risk, 2019
Annamaria Vicari, Nicola Angelo Famiglietti, Gerardo Colangelo, Gianpaolo Cecere
Surface deformations like landslide can be monitored with a variety of sensors (Calò et al. 2012). Current methods are, for instance, airborne and terrestrial laser scanning (ALS and TLS), (Lichun et al. 2008; Monserrat and Crosetto 2008; Mallet and Bretar 2009; Kasperski et al. 2010) prism measurements with robotic total stations (RTS) (Artese and Perrelli 2018), GNSS measurements (Bovenga et al. 2013), and ground or satellite-based differential interferometric synthetic aperture radar (DInSAR) (Tarchi et al. 2003; Cascini et al. 2009; Lowry et al. 2013). All these techniques are classified as ‘Remote Sensing’, which refers to the science aimed at collecting Earth Observations (EO) by using non-contact methods. Remote sensing for landslide investigation is widely documented in the recent literature, especially Advanced Synthetic Aperture Radar Differential Interferometry (A-DInSAR) (Bovenga et al. 2006; Farin et al. 2006; Cascini et al. 2010; Notti 2010; Bovenga et al. 2012; Righini et al. 2012; Herrera et al. 2013; Tofani et al. 2013). We refer to these methods as multi temporal interferometry (MTI). Many MTI application opportunities are emerging thanks to greater data availability from radar satellites and improved capabilities of the new space radar sensors (C-bandSentinel-1) in terms of resolution (from 20 to 5 m) and revisit time (about 12 days for C-band acquisitions). This implies greater quantity and quality information about ground surface displacements and hence improved monitoring capabilities of slow kinematic movements (Prati et al., 2010). MTI users can rely on the following strengths of the technique: Large area coverage (thousands of km2) together with high spatial resolution (1–3 m) of the new generation radar sensors, and multi-scale investigation option (from regional to site-specific);Very high precision (typically mm) of surface displacement measurements marginally influenced by bad weather;Regular, high frequency (days–weeks) measurements over long periods (years);Retrospective studies using long-period (20 years) archived radar imagery.