Search Results (19)

The conventional Capon beamforming algorithm can achieve a high gain in the direction of desired signals and zero-trapping in the direction of interfering signals, providing a high output signal-to-interference-plus-noise ratio (SINR). However, when the steering vector of the desired signal is mismatched, the performance of the Capon beamforming algorithm degrades. In addressing this challenge, the present research introduces a refined algorithm. The core of the proposed robust Capon beamforming technique lies in leveraging the orthogonality between the steering vector and the noise space, the estimated expected signal steering vector is corrected. Based on this feature, the proposed algorithm meticulously optimizes the predicted steering vector of the desired signal, which can mitigate the problem of performance degradation caused by the mismatch in the steering vector. Moreover, the covariance matrix is corrected using the desired signal elimination method, which can overcome the problem of signal self-cancelation. Furthermore, through the optimization process, the proposed algorithm can maintain high robustness in complex environments and under the condition of different input signals, its beam pattern performance is more excellent. The results of simulation experiments show that the proposed algorithm demonstrates greater robustness compared to the currently available algorithms, can achieve a higher output SINR, and is insensitive to steering vector mismatch. Full article

Aiming at the poor warning effect and slightly low precision of signal positioning in the traditional lightning detection and warning system, the research utilizes the artificial intelligence approach to signal positioning in the lightning detection and warning system for performance improvement. The study first digitized the lightning signal using the arrival direction algorithm and then used the Capon algorithm based on the digitized processing to reduce the interference and improve the accuracy of lightning positioning. The results indicated that the root mean square error value and positioning angle error of lightning warning signal positioning data processing by hybrid algorithm were 6.72% and 5.93%, respectively. Meanwhile, the percentage of detection efficiency and real time was 96.36% and 95.16%, respectively, and the anti-interference ability was 94.02%. Moreover, the average value of time-consuming lightning warning positioning and the positioning error were 2.39 s and 2.69%, respectively. Moreover, the performance of all the comparison indexes was better than that of the comparison methods. This indicates that the method not only improves the precision of lightning signal positioning but also enhances the stability and real-time performance of the system. It has significant application potential in the field of lightning detection and warning and can effectively improve the precision and timeliness of lightning warning. Full article

A reduced-dimension robust Capon beamforming method using Krylov subspace techniques (RDRCB) is a diagonal loading algorithm with low complexity, fast convergence and strong anti-interference ability. The diagonal loading level of RDRCB is known to become invalid if the initial value of the Newton iteration method is incorrect and the Hessel matrix is non-positive definite. To improve the robustness of RDRCB, an improved RDRCB (IRDRCB) was proposed in this study. We analyzed the variation in the loading factor with the eigenvalues of the reduced-dimensional covariance matrix and derived the upper and lower boundaries of the diagonal loading level; the diagonal loading level of the IRDRCB was kept within the bounds mentioned above. The computer simulation results show that the IRDRCB can effectively solve the problems of a sharp decline in the signal-to-noise ratio gain and an invalid diagonal loading level. The experimental results demonstrate that the interference noise of the IRDRCB is 3~5 dB higher than that of conventional adaptive beamforming, and the computational complexity is reduced by 15% to 20% compared with that of the RCB method. Full article

The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS. Full article

To overcome the problems of the high sidelobe levels and low computational efficiency of traditional Capon-based beamformers in optimizing the two-dimensional (elevation–azimuth) beampatterns of conformal arrays, in this paper, we propose a robust Capon beamforming method with sparse group constraints that is solved using the alternating-direction method of multipliers (ADMM). A robustness constraint based on worst-case performance optimization (WCPO) is imposed on the standard Capon beamformer (SCB) and then the sparse group constraints are applied to reduce the sidelobe level. The constraints are two sparsity constraints: the group one and the individual one. The former was developed to exploit the sparsity between groups based on the fact that the sidelobe can be divided into several different groups according to spatial regions in two-dimensional beampatterns, rather than different individual points in one-dimensional (azimuth-only) beampatterns. The latter is considered to emphasize the sparsity within groups. To solve the optimization problem, we introduce the ADMM to obtain the closed-form solution iteratively, which requires less computational complexity than the existing methods, such as second-order cone programming (SOCP). Numerical examples show that the proposed method can achieve flexible sidelobe-level control, and it is still effective in the case of steering vector mismatch. Full article

Polarimetric phased array radar (PAR) can achieve high temporal resolutions for improved meteorological observations with digital beamforming (DBF). The Fourier method performs DBF deterministically, and produces antenna radiation patterns with fixed sidelobe levels and angular resolution by pre-computing the beamforming weights based on the geometry of receivers. In contrast, the Capon method performs DBF adaptively in response to the changing environment by computing the beamforming weights from the received signals at multiple channels. However, it becomes computationally expensive as the number of receivers grows. This paper presents computationally efficient adaptive beamforming with an application of convolutional neural networks, named ABCNN. ABCNN is trained with the phase and amplitude of complex-valued time-series IQ signals and the Capon beamforming weights as input and output. ABCNN is tested and evaluated using simulated time-series data from both point targets and weather scatterers for a planar of fully digital PAR architecture. The preliminary results show that ABCNN lowers computation time by a factor of three, compared to the Capon method, for a phased array antenna with 1024 elements, while mitigating the contamination from sidelobes by placing nulls at the location of the clutter. Furthermore, ABCNN produces antenna patterns similar to those from the Capon method, which shows that it has successfully learned the data. Full article

In radar detection, in order to make the beam have variable directivity, a Capon beamformer is usually used. Although this traditional beamformer enjoys both high resolution and good interference suppression, it usually leads to high sidelobe and is sensitive to array steering vector (ASV) mismatch. To overcome such problems, this study devises a novel, robust adaptive beamformer that is robust to ASV mismatch under the constraint where the sidelobe is oriented to the ground. Moreover, to make full use of the transmit power, the constraint of a low peak-to-average power ratio (PAPR) is also taken into consideration. Accordingly, this robust adaptive beamformer is developed by optimizing a transmitting beamformer constrained by ASV mismatch and low PAPR. This optimization problem is transformed into a second-order cone programming (SOCP) problem which can be efficiently and exactly solved. The proposed transmit beamformer possesses not only adaptive interference rejection ability and robustness against ASV mismatch, but also direct sidelobe control and a low PAPR. Simulation results are presented to demonstrate the superiority of the proposed approach. The proposed method can make the peak sidelobe level (PSL) level on the ground side below −30 dB. Full article

In order to flexibly and completely suppress dynamic interference, a flexible and robust beamforming based on JADE is proposed in this paper. In addition, it is insensitive to the gain–phase errors of the array. Firstly, the actual steering vector with gain–phase errors is separated from the received snapshot data by the joint approximate diagonalization of eigenmatrix (JADE) algorithm. Secondly, the direction of arrival (DOA) of interference can be estimated from the separated actual steering vector by the correlation coefficient method. Thus, the actual interference steering vector with gain–phase errors can be selected by the correlation coefficient with the nominal steering vector constructed by the estimated DOA. Then, the interference covariance matrix can be reconstructed by the actual interference steering vector, and the interference power estimated by the Capon power spectral. Finally, according to the prior information of the interference, only the dynamic interference covariance matrix is tapered by the novel covariance matrix trap (CMT), which can flexibly broaden and deepen the null. Simulation results show that the depth of the proposed beamformer is more than 10 dB deeper than that of the traditional algorithm in the non-stationary interference. In addition, it can save at least 2 degrees of freedom compared to the traditional method. Full article

Ground-based oblique incidence sounding (OIS) is an important means to investigate the ionosphere. As the OIS ionogram is a visual representation of the OIS parameters, such as group distance and maximum usable frequency (MUF), it is of great significance for improving the quality and the range resolution. This will facilitate the automatic interpretation and inversion of OIS ionograms to obtain the fine structure and spatial–temporal evolutions of the ionosphere. In this paper, a novel OIS signal processing scheme is proposed based on the Eigenspace-based (ESB) beamforming technology and Capon high-resolution range profile (HRRP). First, by applying the ESB beamformer to a compact L-shaped antenna array, the energy of the OIS signals received by multiple antennas can be added, while the interference and noise will be suppressed. Subsequently, the Capon HRRP algorithm is used to improve the range resolution. This is achieved due to the slow variation in the characteristics of the ionosphere resulting in good short-term coherence of the narrowband signals. The experimental results show that the two-stage signal-processing method significantly improves the imaging quality of OIS ionograms. In particular, the structure inside the ionosphere and its temporal and spatial evolution can be observed more precisely after the range resolution of the OIS ionogram is improved; therefore, it has great application potential. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect. Full article

Underlying topography plays an important role in the national economic construction, military security, resource exploration and investigation. Since synthetic aperture radar tomography (TomoSAR) can achieve the three-dimensional imaging of forests, it has been widely used in underlying topography estimation. At present, there are two kinds of TomoSAR based on the applied datasets: single polarimetric TomoSAR (SP-TomoSAR) and fully polarimetric TomoSAR (FP-TomoSAR). However, SP-TomoSAR cannot obtain the underlying topography accurately due to the lack of enough observations. FP-TomoSAR can improve the estimation accuracy of underlying topography. However, it requires high-cost data acquisition for the large-scale application. Thus, this paper proposes the dual polarimetric TomoSAR (DP-TomoSAR) as another suitable candidate to estimate the underlying topography because of its wide swath and multiple polarimetric observations. Moreover, three frequently used spectral estimation algorithms, namely, Beamforming, Capon and MUSIC, are used in DP-TomoSAR. For validation, a series of simulated experiments was carried out, and the airborne P-band multiple polarimetric SAR data over the Lope, Gabon was also acquired to estimate the underlying topography. The results suggest that DP-TomoSAR in HH & HV combination is more suitable to estimate underlying topography over forest areas than other DP combinations. Moreover, the estimation accuracy of DP-TomoSAR is slightly lower than that of FP-TomoSAR but is higher than that of SP-TomoSAR. Full article

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics. Full article

The key point of forest height and underlying topography inversion using synthetic aperture radar tomography (TomoSAR) depends on the accurate positioning of the phase centers of different scattering mechanisms. The traditional nonparametric spectrum analysis methods (such as beamforming and Capon) have limited vertical resolution and cannot accurately distinguish closely spaced scatterers. In addition, it is very difficult to accurately estimate the ground or canopy heights with single polarimetric SAR images because there is no guarantee that the vertical profile will generate two clear and separate peaks for all resolution cells. A polarimetric TomoSAR method based on SKP (sum of Kronecker products) decomposition and iterative maximum likelihood estimation is proposed in this paper. On the one hand, the iterative maximum likelihood TomoSAR method has a higher vertical resolution than that of the traditional methods. On the other hand, the separation of the canopy scattering mechanism and the ground scattering mechanism is conducive to the positioning of the phase centers. This method was applied to the inversion of forest height and underlying topography in a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana with six passes of polarimetric SAR images. The inversion accuracy of underlying topography of the proposed method was up to 1.489 m and the inversion accuracy of forest height was up to 1.765 m. Compared with the traditional polarimetric beamforming and polarimetric capon methods, the proposed method greatly improved the inversion accuracy of forest height and underlying topography. Full article

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice. Full article

Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed. Full article