Iterative Adaptive Approach Algorithm Research Articles

Fast Iterative Adaptive Approach for Indoor Localization With Distributed 5G Small Cells

This letter addresses Time-Of-Arrival estimation for an indoor positioning system (IPS) made up of distributed 5G small cells by focusing on the Iterative Adaptive Approach (IAA) with Orthogonal Frequency Division Multiplexing (OFDM) signals. While IAA has been successfully applied to many other areas, its application to 5G IPS is not straightforward. In fact, even the current fast implementation of IAA is hardly compatible with 5G chips having limited computational capability, due to large number of sub-carriers of OFDM signals. To address this problem, we pursue the idea of averaging over a number of successive sub-carriers sequentially. Since our analysis shows that the direct Sub-carrier Average (SA) operation exhibits the asinc modulation effect on channel response, possibly leading to miss-detection of Direct Path (DP), we further propose a channel compensation-based SA method and provide a quantitative analysis of the signal-to-noise ratio of the DP to demonstrate its robustness. The resulting SA-based fast IAA (SA-FIAA) algorithm can achieve comparable performance as the standard IAA, while reducing computational cost by over 2 − 3 orders of magnitude, making it practically tractable in 5G IPSs. The effectiveness and computational efficiency of the proposed approach are well demonstrated through real-world experiments.

Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band

Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.

FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.

Iterative adaptive approach STAP algorithm based on arc antenna array

The radar with arc antenna array has the advantages of all-orientation space scanning, simultaneous estimation of pitch-azimuth angle and good antenna pattern. However, due to the special geometry of the antenna, the echoes from different range cells are not independently and identically distributed (IID) samples, resulting in performance degradation in space-time adaptive processing technology. In this paper, the model of airborne radar with arc antenna array is established, and the clutter distribution is analyzed. The conclusion is that the nonlinear variation of spatial angular frequency with array element number results in the non-uniform clutter distribution in different range cells. Therefore, the traditional space-time adaptive processing is not fit for the target detection of arc array radar. Hence, the arc array-based iterative adaptive approach (IAA) algorithm is studied. The IAA algorithm is utilized to estimate the space-time spectrum, which can distinguish clutter and targets. Simulation experiments verify the effectiveness of the IAA algorithm in arc array radar.

DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging

Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets' areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets' areas according to the coarsely possible targets' areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets' areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.

Robust adaptive beamforming using iterative adaptive approach

ABSTRACTThe performance of traditional reconstruction-based robust adaptive beamformers may degrade when the array is not well calibrated. This performance degradation is mainly caused by the Capon spectrum estimator which can not estimate the spatial power spectrum accurately. In contrast to existing approaches, we propose two new reconstruction-based robust adaptive beamformers by using the accurate iterative adaptive approach (IAA) spectrum to combat the covariance matrix uncertainties and the steering vector mismatches. The first one employs the low-complexity IAA (IAA-LC) algorithm to obtain the interference power estimates and reconstruct the interference-plus-noise covariance matrix (INCM) with the computational complexity further reduced. The second one reconstructs the INCM by updating the power estimates corresponding to each interference steering vector with the use of knowledge-aided IAA (IAA-KA) algorithm. The desired signal steering vector is corrected by searching for the direction corresponding to the maximum power, which circumvents the use of optimization program. Simulation results show that our proposed beamformers outperform the others and can achieve the robust performance in the cases of array model mismatches.

MIMO High Frequency Surface Wave Radar Using Sparse Frequency FMCW Signals

The heavily congested radio frequency environment severely limits the signal bandwidth of the high frequency surface wave radar (HFSWR). Based on the concept of multiple-input multiple-output (MIMO) radar, we propose a MIMO sparse frequency HFSWR system to synthesize an equivalent large bandwidth waveform in the congested HF band. The utilized spectrum of the proposed system is discontinuous and irregularly distributed between different transmitting sensors. We investigate the sparse frequency modulated continuous wave (FMCW) signal and the corresponding deramping based receiver and signal processor specially. A general processing framework is presented for the proposed system. The crucial step is the range-azimuth processing and the sparsity of the carrier frequency causes the two-dimensional periodogram to fail when applied here. Therefore, we introduce the iterative adaptive approach (IAA) in the range-azimuth imaging. Based on the initial 1D IAA algorithm, we propose a modified 2D IAA which particularly fits the deramping processing based range-azimuth model. The proposed processing framework for MIMO sparse frequency FMCW HFSWR with the modified 2D IAA applied is shown to have a high resolution and be able to provide an accurate and clear range-azimuth image which benefits the following detection process.

Super-resolution Doppler beam sharpening imaging based on an iterative adaptive approach

ABSTRACTThis paper proposes a super-resolution Doppler beam sharpening (DBS) imaging method in the wide-area surveillance mode based on the iterative adaptive approach (IAA). DBS imaging is an effective technique to improve the azimuth resolution in the wide-area surveillance mode. However, the conventional DBS imaging, which is realized by fast Fourier transform (FFT) method, suffers from poor resolutions and high sidelobes. In this paper, the IAA algorithm is introduced to replace the FFT method in the DBS imaging, in order to enhance resolution and reduce sidelobe levels. Both simulation and real data processing have validated the effectiveness of the proposed method.

Adaptive clutter suppression based on iterative adaptive approach for airborne radar

To improve the performance of the recently developed weighted least-squares-based iterative adaptive approach (IAA) in space–time adaptive processing (STAP) for weak or slow targets detection, we propose a novel IAA scheme to adaptively suppress the ground clutter by using the secondary training data (STD). Especially, we use the IAA to estimate the clutter plus noise covariance matrix from a very small number of STD. The resulting clutter plus noise covariance matrix can be utilized to form the STAP filter and then suppress the clutter. To reduce the computational complexity of the IAA, we exploit the sparsity of large clutter components in the angle-Doppler image and develop a modified IAA algorithm employing a soft-thresholding to adaptively determine the entries of each iteration that should be updated. Simulation results show that our proposed scheme outperforms the conventional IAA scheme over weak or slow targets detection and the modified IAA algorithm exhibits a comparable or even a better performance than the IAA algorithm but a lower computational complexity.

Superfast Approximative Implementation of the IAA Spectral Estimate

In this correspondence, we develop superfast approximative one-dimensional algorithms for the computationally efficient implementation of the recent iterative adaptive approach (IAA) spectral estimate. The proposed methods are based on rewriting the IAA algorithm with suitable Gohberg-Semencul representations, solving the resulting linear systems of equations using the preconditioned conjugate gradient method, where a novel preconditioning is applied using an incomplete factorization of the Toeplitz matrix. Numerical simulations illustrate the efficiency of both the proposed preconditioning as well as the overall algorithm, offering a computational reduction of up to two orders of magnitude as compared to our recently proposed efficient and exact IAA implementation.

Time-Recursive IAA Spectral Estimation

This letter presents computationally efficient time-updating algorithms of the recent Iterative Adaptive Approach (IAA) spectral estimation technique. By exploiting the inherently low displacement rank, together with the development of suitable Gohberg-Semencul (GS) representations, and the use of data dependent trigonometric polynomials, the proposed time-recursive IAA algorithm offers a reduction of the necessary computational complexity with at least one order of magnitude. The resulting complexity can also be reduced further by allowing for approximate solutions. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.

ANALYSIS OF RADIAL VELOCITY DATA BY A NOVEL ADAPTIVE APPROACH

In this paper, we introduce an estimation technique for analyzing radial velocity data commonly encountered in extrasolar planet detection. We discuss the Keplerian model for radial velocity data measurements and introduce a technique named the iterative adaptive approach (IAA) to estimate the three-dimensional spectrum (power versus eccentricity, orbital period and periastron passage time) of the radial velocity data. We then discuss different ways to regularize the IAA algorithm in the presence of noise and measurement errors. We also discuss briefly the computational aspects of the method and introduce a computationally efficient version of IAA. Finally, we establish the significance of the spectral peaks by using a relaxation maximum likelihood algorithm and a generalized likelihood ratio test. Numerical experiments are carried out on both simulated and real life data sets to evaluate the performance of our method. The real life data sets discussed are radial velocity measurements of the stars HD 63454, HD 208487, and GJ 876.

Iterative Adaptive Approaches to MIMO Radar Imaging

Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.

High Resolution Angle-Doppler Imaging for MTI Radar

To reduce the need for training data or for accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we consider high resolution angle-Doppler imaging by processing each range bin of interest independently. Specifically, we use a weighted least-squares-based iterative adaptive approach (IAA) to form angle-Doppler images of both clutter and targets for each range bin of interest. The resulting angle-Doppler images can be used with localized detection approaches for moving target indication (MTI). We show via numerical examples that the robust and nonparametric IAA algorithm can be used to enhance the MTI performance significantly as compared with existing approaches.