Channel Mismatching in Sidelobe Canceler: Limitations and Remedies

After a brief recall of the Sidelobe Canceler (SLC) working principle, including the derivation of a general formula for the Cancellation Ratio (CR), the effects of channel mismatching are investigated. In particular, curves providing CRvalues as a function of amplitude and phase channel mismatching, radar bandwidth, and jammer direction of arrival (JDOA) are provided for the cases of one and two auxiliary antennas. Subsequently, a time -space processor for performance restoration is analyzed in detail. In addition to the above mentioned quantities, the attainable CR value is expressed as a function of the space-time processor parameters. The contribution of the paper is related to the derivation of a number of mathematical equations of CR for several cases of practical interest for the radar engineer. In addition, several curves are presented to assist design of SLC systems.

Beamforming with the Generalized Sidelobe Cancellerfor Coherent Noise Attenuation in 3D Seismic Data

This paper presents a new approach for attenuating coherent noise in 3D seismic data. An adaptive beamforming with generalized sidelobe canceller (GSC) design methodology is utilized here as a general form of linearly constrained adaptive beamforming structure. It consists of a fixed beamformer, and a signal-blocking matrix in front of an unconstrained adaptive beamformer.Considerationf of the complexity of the geometry for 3D seismic survey, the 3D beamforming with GSC technique is developed with two key points: (1) sorting along azimuth sections to simplify the relationship between traveltime and offset from 3D to 2D, and (2) dynamic binning scheme to avoid the possible poor folding in some azimuth sections. Both simulation result and real data example show that the newly developed 3D beamforming with GSC yields more credible results at a relative low cost, sufficient stability and good resolution.

Analysis of Effects of System Mismatches on the Performance of Adaptive Generalized Sidelobe Canceller and Compensation Methods

The effect of system mismatches on an adaptive linear constrained generalized sidelobe canceller （LC-GSC） is discussed in this paper. Based on the array gain index, two classic system mismatches, the direction of arrival （DOA） mismatch and the mismatches arising from array disturbance, are studied, respectively. To obtain the effective methods for compensating for the system mismatches, we analyze the performance of the improved LC-GSC with the diagonal loading and additional constraints （such as the directional constraints and derivative constraints）. The computer simulations show that the techniques of diagonal loading and additional constraints can effectively compensate for the system mismatches. The loss of array gains can be controlled within 3 dB in the presence of 20% of array disturbances or DOA mismatch when the signal-to-noise ratio is less than 10 dB. The analysis illustrates that the proposed compensation methods are valid and feasible.

Antenna Sidelobe Canceller of Kalman Filter

The adaptive array antenna may be considered as a general sidelobe canceller. Directional interference suppression is based on a recursive state estimation of Kalman filter. For the stationary filter,this leads to an iterative solution of Wiener Hops matrix equation. The performance of sidelobe canceller are studied by computer simulation. The result of simulation shows that the sidelobe canceller may be regarded as a special case of an adaptive array atenna.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Speech enhancement based on leakage constraints DF-GSC

In order to improve the performance of general sidelobe canceller （GSC） based speech enhancement, a leakage constraints decision feedback generalized sidelobe canceller（LCDF-GSC） algorithm is proposed. The method adopts DF-GSC against signal mismatch, and introduces a leakage factor in the cost function to deal with the speech leakage problem which is caused by the part of the speech signal in the noise reference signal. Simulation results show that although the signal-to-noise ratio （SNR） of the speech signal through LCDF-GSC is slightly less than that of DF-GSC, the IS measurements show that the distortion of the former is less than that of the latter. MOS （mean opinion score） scores also indicate that the LCDF-GSC algorithm is better than DF- GSC and the Weiner filter algorithm,

Efficient parallel adaptive array beamforming algorithm

For a large-scale adaptive array, the heavy computational load and the high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. An efficient parallel digital beamforming （DBF） algorithm based on the least mean square algorithm （PLMS） is proposed. An appropriate method is found to partition the least mean square （LMS） algorithm into a number of operational modules, which can be easily executed in a distributed-parallel-processing fashion. As a result, the proposed PLMS algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. PLMS requires less computational load than that of the conventional parallel algorithms based on the recursive least square （RLS） algorithm, as well as it is easier to be implemented to do real time adaptive array processing. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. Finally, a scheme of the PLMS algorithm using distributed-parallel-processing system is also proposed. The simulation results demonstrate that the PLMS algorithm has the same interference cancellation performance as that of the conventional LMS algorithm. Moreover, the PLMS algorithm can obtain the same good beamforming performance, regardless how the algorithm is partitioned. It is expected that the proposed algorithm will be used in a large-scale adaptive array system to deal with real time adaptive digital beamforming processing.

Robust generalized sidelobe canceller based on eigenanalysis and a MaxSINR beamformer

A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction methods usually collect a large number of segments where only the IPN signal is active.To avoid that collection procedure,we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples.Additionally,a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy.The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio(MaxSINR)beamformer.It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression.Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots.Compared to the first proposed method,the modified one can reduce the signal distortion even further.

Coordinated adaptive beamformer over distributed antenna network

The spatial diversity of distributed network demands the individual filter to accommodate the topology of interference environment. In this paper, a type of distributed adaptive beamformer is proposed to mitigate interference over coordinated antenna arrays network. The proposed approach is formulated as generalized sidelobe canceller （GSC） structure to facilitate the convex combination of neighboring nodes＇ weights, and then it is solved by unconstrained least mean square （LMS） algorithm due to simplicity. Numerical results show that the robustness and convergence rate of antenna arrays network can be significantly improved in strong interference scenario. And they also clearly illustrate that mixing vector is optimized adaptively and adjusted according to the spatial diversity of the distributed nodes which are placed in different power of received signals to interference ratio （SIR） environments.

Speech enhancement with a GSC-like structure employing sparse coding

Speech communication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller(GSC), which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. Sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast,the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection(VAD). Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.