Adaptive Radar

Smith, Steven T.

doi:10.1002/047134608x.w7222

Cited by 11 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…that is typically referred to as the optimum radar receiver filter [2], [3] whose schematic representation is given in Figure 1. As it can be clearly observed from (7), the optimum receiving filter for STAP essentially assumes the knowledge of the steering vector v and requires the computation of the disturbance covariance matrix.…”

Section: Optimum Space-time Adaptive Processingmentioning

confidence: 99%

Phase-Only Space-Time Adaptive Processing

Pallotta¹,

Farina

Smith

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling, derives the optimum phase-only STAP; namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the N -torus of phase-only complex vectors, where N is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phaseonly STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.INDEX TERMS radar signal processing, space time adaptive processing (STAP), adaptive radar receiver, phase-only adaptive nulling, phase-only STAP, gradient descent method.

show abstract

Section: Optimum Space-time Adaptive Processingmentioning

confidence: 99%

Phase-Only Space-Time Adaptive Processing

Pallotta¹,

Farina

Smith

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Each cycle requires one q ⇥ q and two r ⇥ r matrix inversions. Hence, this method is computationally efficient in applications with the signal and interference ranks q and r much smaller than m, such as STAP [6]- [8], [18] where m is the product of the numbers of sensors and pulse returns, see also [4] and [5]. Our ICM algorithm also provides estimates of the interference signals H, see (3.7c).…”

Section: Bayesian Estimation Of Complex Signal Amplitudes and Apmentioning

confidence: 99%

“…where the loading factors have been selected as 2 2 e 2 , 3 e 2 , corresponding to 0 and 5 dB above the estimated noise level (3.9c) [8]. In Fig.…”

Section: Numerical Examplesmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference

Dogandzic

Zhang

2007

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We propose a Bayesian method for complex amplitude estimation in low-rank interference. We assume that the received signal follows the generalized multivariate analysis of variance (GMANOVA) patterned-mean structure and is corrupted by low-rank spatially correlated interference and white noise. An iterated conditional modes (ICM) algorithm is developed for estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (nonBayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods Abstract We propose a Bayesian method for complex amplitude estimation in low-rank interference. We assume that the received signal follows the generalized multivariate analysis of variance (GMANOVA) patterned-mean structure and is corrupted by low-rank spatially correlated interference and white noise. An iterated conditional modes (ICM) algorithm is developed for estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (non-Bayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods.

show abstract

“…By construction, these approaches describe a high-dimensional object (a symmetric positive semidefinite matrix with O(n 2 ) degrees of freedom) using only a few generic parameters that are not problem-specific; more broadly, these parametric families may not be rich enough to capture the phenomena of interest. Nonparametric methods (e.g., sparse precision matrix estimation [11,20,12], tapering [24,21], diagonal loading [42], and shrinkage [26,27,41]) can be much more flexible. However, neither approach easily allows prior knowledge---for instance, known covariance matrices at related conditions---to be incorporated.…”

mentioning

confidence: 99%

Geodesically Parameterized Covariance Estimation

Musolas¹,

Smith²,

Marzouk³

2021

SIAM J. Matrix Anal. Appl.

Self Cite

View full text Add to dashboard Cite

Statistical modeling of spatiotemporal phenomena often requires selecting a covariance matrix from a covariance class. Yet standard parametric covariance families can be insufficiently flexible for practical applications, while nonparametric approaches may not easily allow certain kinds of prior knowledge to be incorporated. We propose instead to build covariance families out of geodesic curves. These covariances offer more flexibility for problem-specific tailoring than classical parametric families and are preferable to simple convex combinations. Once the covariance family has been chosen, one typically needs to select a representative member by solving an optimization problem, e.g., by maximizing the likelihood of a data set. We consider instead a differential geometric interpretation of this problem: minimizing the geodesic distance to a sample covariance matrix (``natural projection""). Our approach is consistent with the notion of distance employed to build the covariance family and does not require assuming a particular probability distribution for the data. We show that natural projection and maximum likelihood estimation within the covariance family are locally equivalent up to second order. We also demonstrate that natural projection may yield more accurate estimates with noise-corrupted data.

show abstract

Adaptive Radar

Abstract: The sections in this article are Adaptive Radar Fundamentals Statistical Model for Adaptive Radar Optimum Adaptive Processing Space–Time Adaptive Processing Adaptive Detection Adaptive Weight Computation Adaptive Radar Estimation Conclusions

Cited by 11 publications

References 46 publications

Phase-Only Space-Time Adaptive Processing

Phase-Only Space-Time Adaptive Processing

Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference

Geodesically Parameterized Covariance Estimation

Contact Info

Product

Resources

About