Eigenanalysis-Based Adaptive Interference Suppression and Its Application in Acoustic Source Range Estimation

Ren, Suiling; Ge, Feng-Xiang; Guo, Xin; Guo, Long

doi:10.1109/joe.2014.2359378

Cited by 25 publications

(17 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The remainder of the paper is outlined as follows. In the Section II, the IS module based on eigenanalysis-based adaptive interference suppression (EAAIS) proposed by Ren [27] and the BOT module based on KF are built firstly, and the following ISKF joint algorithm is first presented, too. The Section III shows the sea trial results of applying the proposed algorithm for weak target tracking under two conditions, namely uncrossed and crossed conditions.…”

Section: B the Effective Bot In Complex Conditionsmentioning

confidence: 99%

“…Therefore, underwater IS is one of most important part in ocean exploration. The robustness and other merits of EAAIS have been introduced in [27], and it will be employed in the IS module as the core algorithm.…”

Section: A Eigen-decomposition Based Interference Suppression Algorithmmentioning

confidence: 99%

“…Furthermore, this novel algorithm is able to efficiently eliminate the ill effects of interferers and achieve the robust real-time tracking of passive sonar, which may be a challenge for the traditional KF tracker. Most importantly, the ISKF joint algorithm makes two contributions as follows towards addressing the inherent limitations of IS algorithm and traditional KF tracker [27]- [35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Chen

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Aiming at the problems in the bearing-only tracking (BOT) of underwater weak target in the presence of interferers, such as discontinuity of tracking trajectory and target loss, a novel joint algorithm based on interference suppression (IS) and Kalman filter (ISKF) is proposed in this paper. In the proposed algorithm, in order to smooth the corrupted trajectory of weak target and improve the precision of tracking, a feedback structure which connects IS module and Kalman filter (KF) module is built. Furthermore, the feedback structure renders the measured results of bearing trajectory shareable mutually between IS module and KF module in real-time. In ISKF joint algorithm, IS module is employed as a preprocessing module to eliminate the power of interferers, and KF module is employed as tracker to obtain the bearing trajectory of weak target. In the initial step, the coarse target bearing and interference bearing are estimated using the conventional beamforming (CBF) method. In the updated step, the precise BOT trajectory of weak target is jointly updated using IS and KF. Moreover, when the target intersects with the interference, the ISKF joint algorithm can still track the weak target effectively with the aid of prediction model of KF. Finally, the sea trial results show that the proposed algorithm can achieve more accurate and robust real-time tracking than KF only, which benefits from the feedback between the IS module and the KF module.INDEX TERMS Interference suppression (IS), Kalman filter (KF), deep integration, BOT, ISKF joint algorithm.

show abstract

Section: B the Effective Bot In Complex Conditionsmentioning

confidence: 99%

Section: A Eigen-decomposition Based Interference Suppression Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Chen

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It can be interpreted as the ratio between the maximum of the CBF power spectra of ( ) m l ω υ in angular sector SOI Θ where the desired signal is located and the maximum in the whole direction region [6].…”

Section: Eigenanalysis Based Interference-plus-noise Covariance Matrimentioning

confidence: 99%

“…However, this kind of pure period is unavailable in many acoustic applications due to strong background noise. In [6], the SOI covariance matrix was reconstructed using received signal samples by building robust power ratios to identify all the eigenvectors not dominated by the SOI and subtracting them from the matrix. It shows robustness, but cannot produce the enhanced waveform.…”

Section: Introductionmentioning

confidence: 99%

Robust Generalized Sidelobe Canceller with an Eigenanalysis-Based Blocking Matrix

WANG¹,

REN²

2017

dtetr

View full text Add to dashboard Cite

Abstract. In acoustic environments with strong background noise and interferences, the generalized sidelobe canceller (GSC) suffers from signal distortion and reduced amount of noise suppression since the blocking matrix cannot produce pure noise reference due to unavoidable direction of arrival (DOA) mismatch. In this paper, a generalized sidelobe canceller robust to DOA mismatch is proposed. It employs eigenanalysis to reconstruct the interference-plus-noise covariance matrix with the possible DOA region of signal of interest (SOI). The reconstructed matrix is provided for the MaxSINR beamforming and subsequent blocking matrix construction. The designed blocking matrix can minimize the signal leakage meanwhile maximize the noise and interference power for further noise and interference suppression. Numerical simulation results show that compared with the alternatives, the proposed GSC has better interference suppression capability and lower SOI distortion in situations without precise DOA of SOI.

show abstract

Tow ship interference mitigation in shallow ocean using correlated mode decomposition technique

Remadevi

Suneera

Sureshkumar

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Passive towed arrays assume great importance in shallow ocean surveillance, owing to their capability to detect long range underwater acoustic targets. The detection and classification performance is severely impacted by the tow ship interference manifestation in the pressure sensor array towed behind the ship. In this work, a robust tow ship multipath interference mitigation algorithm intended for shallow ocean operation is developed, based on the dynamic mode decomposition (DMD) technique, which in turn exploits the coherent structure of the multimode acoustic wave propagation inherent in the shallow ocean scenario. The proposed algorithm effectively identifies the coherent normal mode interference subspace and eliminates its components from the received array data vector. Further, this approach compliments the shallow ocean normal mode acoustic field propagation and ensures simultaneous mitigation of all the multipath generated interferences, making it a superior method to reduce the azimuth spread of the interference and tow ship spectral leakage into the panoramic detection beams. This greatly enhances the detection and classification capability of sonar, especially for distant stealthy targets. The efficacy of the algorithm is validated using Monte Carlo simulations and further ratified by experimental data captured during the field trials in the Arabian Sea.

show abstract

Eigenanalysis-Based Adaptive Interference Suppression and Its Application in Acoustic Source Range Estimation

Cited by 25 publications

References 34 publications

Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Robust Generalized Sidelobe Canceller with an Eigenanalysis-Based Blocking Matrix

Tow ship interference mitigation in shallow ocean using correlated mode decomposition technique

Contact Info

Product

Resources

About