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Detection Paradigms for Radar
Published in Alexander D. Poularikas, Stergios Stergiopoulos, Advanced Signal Processing, 2017
Bhashyam Balaji, Michael K. McDonald, Anthony Damini
Over the past two decades, there has been a proliferation of research within the international community to extend the detection capabilities of radar beyond that achieved by the traditional noncoherent and simple coherent processing strategies. In particular, surveillance with air and space-borne multiple aperture radar systems utilizing space-time adaptive processing (STAP) has resulted in a decreased minimum velocity at which a target can be detected in a clutter background, thus increasing the robustness of the radar system. Furthermore, these multiple channel radar systems offer the opportunity for not only detection, but also accurate location of slow-moving targets within the clutter band. STAP signal processing techniques operate by not only analysing the time series of data received from each aperture, as is done in traditional coherent processing, but also by further capitalizing on the spatial diversity afforded through the use of multiple apertures which allows the targets and clutter to be viewed from a diversity of aspect angles. This added degree of freedom provides the capability to simultaneously suppress both clutter and jammer interference, thus increasing the probability of target detection. Similarly, in the case of fixed surface platforms, one and two-dimensional array processing techniques introduce great gains in the area of target detection in the presence of spatially localized interference.
A Novel Compressed Sensing–Based Algorithm for Space–Time Signal Processing Using Airborne Radars
Published in C.H. Chen, Compressive Sensing of Earth Observations, 2017
Jing Liu, Mahendra Mallick, Feng Lian, Kaiyu Huang
Space–time adaptive processing (STAP) is a signal processing technique that was originally developed for detecting slowly moving targets using airborne radars [10–13]. It represents the simultaneous adaptive application of both Doppler filtering and spatial beamforming [14,15] and allows the suppression of clutter that neither technique can address individually. While much of the early work in STAP focuses on the simplest case of side-looking monostatic radars with uniform linear arrays (ULAs), STAP techniques have also been applied to bistatic radars, conformal arrays, space-based radars, and other applications [16]. However, the traditional STAP algorithm uses a large number of training cells to estimate the space–time covariance matrix, which requires a large computer memory and is time-consuming.
High-Resolution Space-Time Signal Processing for Radar
Published in Yingbo Hua, Alex B. Gershman, Qi Cheng, High-Resolution and Robust Signal Processing, 2017
Fredrik Athley, Mats Viberg, Jonny Eriksson
A few comments about the noise model are in order. There are a number of noise sources in a radar system, e.g. thermal noise generated in the receivers and external noise such as sky noise. Another effect that could be accounted for in the noise term is unwanted echoes from the ground, sea, rain, buildings, etc. This is called clutter. The noise will also include any other unmodeled signals, such as intentional jamming. Whereas the thermal noise can be assumed temporally and spatially white, this is not the case for clutter and jamming If the radar platform is not moving, the clutter retina signal will be centered around zero Doppler frequency. It can then be removed relatively easily by a bandstop filter, often called MTI (Moving Target Indication) filter. If the radar platform is moving, the situation is considerably more difficult. The Doppler frequency of the ground echoes will then be different in different directions. One way to deal with this problem is to apply a space-time filter, that removes the clutter. The technique of adaptively suppressing clutter in moving radar systems is often called STAP (Space-Time Adaptive Processing), and has been an intense research area the last decade, see e.g. [25], [53] and the references therein. In this chapter we will adopt a structured space-time model for the noise correlation. While this model is reasonable for jamming, it is at most only approximately true for clutter. Furthermore, clutter is often modeled with more heavy tailed distributions than the Gaussian, such as the Weibull, log-normal and K-distributions (see e.g. [36]). The main reason for using the Gaussian model with structured space-time correlation is simplicity. However, as we will see, the performance of the various estimators only depends on the second order properties of the noise.
A novel method of dynamic monitoring and parameter estimation for rock-fall based on multichannel SAR
Published in Geomatics, Natural Hazards and Risk, 2020
Qian Zhang, Jing Wang, Lili Hou, Peng Lin, Shuguang Song
Currently, the most commonly used methods of rock-fall target detection include Displaced Phase Center Antenna (DPCA) (Muehe and Labitt 2000), Along-track Interferometry (ATI) (Suchandt et al. 2010), and Space-Time Adaptive Processing (STAP) (Ward 1998) methods. STAP uses a space-time adaptive processing method that can restrain clutter and interfere; however, the computational complexity is extremely large (Ender 1999). Re-STAP can overcomes the computational complexity issue of STAP, but the algorithm is vulnerable to the effects of non-uniform environments (Baumgartner and Krieger 2012; Rosenberg and Gray 2013). Additionally, neither DPCA nor ATI can eliminate the influence of poor detection performance associated with the along-track velocity of rock-fall targets (Yan et al. 2013; Zheng et al. 2014). Parameter estimation is crucial for rock-fall target relocation, focused imaging and false target suppression (Gao et al. 2015). In recent years, several methods have been proposed. In (Yang and Wang 2016), estimating the radial velocity is transformed to DOA estimation of the echoes. By constructing the spatial spectrum of the moving target, the radial velocity can be estimated by maximizing the spectrum. In (Baumgartner and Krieger 2016), the imagery quality of the moving target is weighed by some criterion, and the radial velocity is estimated by searching for the value which optimizes the imagery quality. Wang et al. (2016) transform the velocity estimation problem to measuring the azimuth offset, which is proportional to its radial velocity.