STAP for clutter suppression with sum and difference beams

Brown, Russell D.; Schneible, Richard A.; Wicks, Michael C.; Wang, Hong; Zhang, Yuhong

doi:10.1109/7.845254

Cited by 83 publications

(46 citation statements)

References 10 publications

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“…Proof: Our proof proceeds in parallel with the one in [8]. Let (6) denote the unknown parameter vector, where is the vector made up with the elements of , i.e., , , , for , where denotes the imaginary-part operator. Then, the Fisher information matrix for the parameter vector is given by (7) where denotes the trace operator.…”

Section: Cramér-rao Bound For Bs Measurementmentioning

confidence: 99%

“…To alleviate the difficulty, a dimension reduction matrix , , may be used to transform an element space (ES) measurement vector to a beamspace (BS) measurement vector , where superscript denotes the conjugate transpose. The BS transformation matrix, may be designed under different criteria, for example, to cover a given spatial sector [1], to maximize the signal to noise ratio (SNR) in the sector [2], to minimize the interfering power [3], to minimize the Cramér-Rao bound (CRB) [4], or simply to ease the implementation by employing the discrete Fourier transformation (DFT) [5], [6], [7]. Apart from the DFT-based transformation, the above-mentioned will have arbitrary complex-numbered elements, and therefore, they will incur higher hardware cost than the case with unit-modulus complex numbers, if is to be implemented with analog parts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Stochastic CRB for Non-unitary Beam-space Transformations and its Application to Optimal Steering Angle Design

Choi

Chun

Paek

et al. 2015

IEEE Signal Process. Lett.

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Many of modern radar systems employ antenna arrays with thousands of anetnnas. These antennas are usually grouped into a few subarrays or beams through the process of beamspace transformation, to estimate the angle of a target. In this letter, we consider a simple but pragmatic beamspace transformation that only allows beam steering, and present a technique of finding optimal steering angles that minimize the Cramér-Rao bound (CRB) for angle estimation. The CRB and mean squared error (MSE) of the angle estimate using the resulting optimal steering angles are compared with those using the widely used DFT-based beam steering.

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Section: Cramér-rao Bound For Bs Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Stochastic CRB for Non-unitary Beam-space Transformations and its Application to Optimal Steering Angle Design

Choi

Chun

Paek

et al. 2015

IEEE Signal Process. Lett.

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“…In addition, the STAP approach is developed for clutter suppression, which involves adaptively adjusting the space-time filter response in attempt at maximizing output signal-to-interference-plus-noise ratio and improving radar detection performance [33], [34]. In [35], the STAP approach for clutter suppression with sum and difference beams is discussed, where the performance for airborne clutter rejection is shown. In [36], an adaptive filtering method is utilized to calibrate the monostatic and bistatic radars of a monopulse SAR system, where the blind calibration of two channels enables to null stationary scene and detect moving targets.…”

Section: Introductionmentioning

confidence: 99%

Robust Clutter Suppression and Moving Target Imaging Approach for Multichannel in Azimuth High-Resolution and Wide-Swath Synthetic Aperture Radar

Zhang

Xing

Xia

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches. Index Terms-Chirp Fourier transform (CFT), digital beamforming, Doppler ambiguity, Doppler centroid estimation, ground moving target indication (GMTI), high resolution and wide swathManuscript

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“…This idea is further investigated to estimate the angles and ranges of multiple unresolved extended targets in [7]. In [8], the authors focus on space-time adaptive processing [9][10][11][12] and devise decision schemes for pointlike targets, which suitably exploit the spillover of target energy to provide accurate estimates of the target position within the CUT (subbin accuracy). The range gates are formed by sampling the output of a filter matched to the transmitted pulse, such as p(t), every T p seconds, with T p as the duration of p(t).…”

Section: Introductionmentioning

confidence: 99%

Radar detection and range estimation using oversampled data

Aubry

Maio

Foglia

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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In this work, we propose two adaptive receivers achieving enhanced range estimation capability through a joint exploitation of the oversampling and the spillover of target energy in adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT are derived and assessed. The performance analysis, conducted using both simulated data and real recorded datasets, is aimed at assessing the effectiveness of proposed solutions, also in comparison with existing detectors sharing range estimation capabilities. The illustrative examples highlight that better detection performance and increased range estimation accuracy can be achieved by exploiting the oversampling at the price of an additional processing cost.

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STAP for clutter suppression with sum and difference beams

Cited by 83 publications

References 10 publications

A Stochastic CRB for Non-unitary Beam-space Transformations and its Application to Optimal Steering Angle Design

A Stochastic CRB for Non-unitary Beam-space Transformations and its Application to Optimal Steering Angle Design

Robust Clutter Suppression and Moving Target Imaging Approach for Multichannel in Azimuth High-Resolution and Wide-Swath Synthetic Aperture Radar

Radar detection and range estimation using oversampled data

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