Tracking Swerling fluctuating targets at low altitude over the sea

Bossé, Éloi; Turner, R.M.; Lecours, M.

doi:10.1109/7.97326

Cited by 44 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By the WSSUS assumption and the Wiener-Khintchine theorem [24] (13) where is the power spectral density (PSD) of , i.e., the Fourier transform of the covariance function (14) (15) Hence, by (13) (16) In general, pulse wave propagation and scattering in a random medium can be characterized by the correlation function (see chapter §5 in [22]) (17) The function is the correlation function between the output fields due to the time-harmonic inputs at two different frequencies and . We should note that the time-varying frequency response of the random channel in (17) shows explicitly the time index that is omitted in (12). If we let and send two waves at different frequencies and , and observe the fluctuation fields at the same time, as we separate the frequencies, the correlation of these two fluctuation fields decreases.…”

Section: B Multipath Mimo Radar Modelmentioning

confidence: 99%

See 1 more Smart Citation

Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Abstract-Time reversal explores the rich scattering in a multipath environment to achieve high target detectability. Multiple-input multiple-output (MIMO) radar is an emerging active sensing technology that uses diverse waveforms transmitted from widely spaced antennas to achieve increased target sensitivity when compared to standard phased arrays. In this paper, we combine MIMO radar with time reversal to automatically match waveforms to a scattering channel and further improve the performance of radar detection. We establish a radar target model in multipath rich environments and develop likelihood ratio tests for the proposed time-reversal MIMO radar (TR-MIMO). Numerical simulations demonstrate improved target detectability compared with the commonly used statistical MIMO strategy.

show abstract

Section: B Multipath Mimo Radar Modelmentioning

confidence: 99%

“…There are many mechanisms that cause multipath in radar detection, for example, the presence of a large number of scatterers in the vicinity of the target of interest, or tracking and detection of low-angle targets over a flat surface [16], [17]. Multipath affects the level of the energy return from the target due to coherent combining of the return signals.…”

mentioning

confidence: 99%

Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

show abstract

“…3. Basic assumptions, signal model and special cases 5 3 Basic assumptions, signal model and special cases…”

Section: Contributionsmentioning

confidence: 99%

Low Angle Estimation: Models, Methods, and Bounds

Boman

Stoica

2001

Digital Signal Processing

View full text Add to dashboard Cite

In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum-Likelihood estimators exploiting di erent a priori information can be found.The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1 two unknown target locations 2 the target and its corresponding sea-re ection are related via simple geometry 3 the sea re ection coe cient is known as a function of grazing angle.A compact expression for the Cram er-Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cram er-Rao bound is highly dependent on the multipath model, while it is the same for the di erent signal parameterizations and that it is independent of the noise covariance. However, the Cram er-Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cram er-Rao bound. Simulations show that the Maximum Likelihood methods are statistically e cient and achieve the theoretical lower bound on error variance, in case of high enough SNR.The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The in uence of the number of sensors and the number of snapshots on the error variariance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.

show abstract

“…On the other hand, a parametric model takes the height of the antenna, reflection coefficients and so on as a priori information, which works with a few snapshots and correlated sources. A refined maximum likelihood (RML) technique has been proposed based on a refined model [10]. This model combines the geometric information and physical parameters, such as the refractivity gradient, the reflection coefficient, and the divergence factor [11].…”

Section: Introductionmentioning

confidence: 99%

Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar

Huan

Zhang

Dai

et al. 2020

Sensors

View full text Add to dashboard Cite

Height detection of a low elevation angle target is crucial in radar applications. Due to the presence of the multiple path reflections, elevation angle estimation is difficult with conventional narrowband radar waveforms. The reflection ground area parameters are especially hard to obtain for modeling. In this paper, we proposed a wideband, low elevation angle estimator based on range super-resolution, achieving a high robustness to variations in reflection coefficients. A relaxation (RELAX) algorithm was applied as the range super-resolution algorithm to separate the direct target echo and the reflected signal thanks to the relatively abundant frequency diversity. The grazing angle was obtained by synthesizing the steering vector of the direct signal and the range structure relationship between the two signal components. Theoretical analysis and simulation results confirmed the improved behavior of the proposed robust estimator in contrast to other conventional algorithms.

show abstract

Tracking Swerling fluctuating targets at low altitude over the sea

Cited by 44 publications

References 25 publications

Time Reversal in Multiple-Input Multiple-Output Radar

Time Reversal in Multiple-Input Multiple-Output Radar

Low Angle Estimation: Models, Methods, and Bounds

Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar

Contact Info

Product

Resources

About