Spatial Smoothing Preprocessing Research Articles

Simplified Spatial Smoothing for DOA Estimation of Coherent Signals

In coherent direction of arrival (DOA) estimation, subspace-based methods suffer from performance deterioration because of the rank loss of the signal covariance matrix. A variety of spatial smoothing preprocessing techniques have been proposed for decorrelation, among which the enhanced spatial smoothing preprocessing (ESS) technique shows outstanding performance by exploiting the signal subspace. However, ESS is applied by squaring the matrix whose columns span the signal subspace (called signal matrix), which involves unnecessary computational loads. Besides, the smoothed covariance matrix after ESS is a linear combination of the sub-matrices of signal matrix, where the coefficients of the combination may undermine the decorrelation performance. In this context, a simplified spatial smoothing (SSS) technique is proposed for decorrelation by averaging the sub-matrices of the signal matrix directly, and avoids redundant operations in squared signal matrix. The proposed method is tested numerically in terms of the signal-to-noise ratio (SNR), the number of snapshots, angle separation, and the execution time. Simulation results show the improvement of the decorrelation performance, efficiency, and robustness with the proposed method in coherent scenarios, compared with the other spatial smoothing preprocessing based methods.

An Enhanced Spatial Smoothing Technique With ESPRIT Algorithm for Direction of Arrival Estimation in Coherent Scenarios

Subspace-based methods suffer from the rank loss of the noise free data covariance matrix in the context of direction of arrival (DOA) estimation of coherent sources. The well-known spatial smoothing techniques are then widely employed to create a rank restored data covariance matrix. However, conventional spatial smoothing techniques, such as the spatial smoothing pre-processing (SSP), modified spatial smoothing pre-processing (MSSP), and improved spatial smoothing (ISS), do not make full use of the available information in the data covariance matrix. In this paper, an enhanced spatial smoothing (ESS) technique is proposed to exploit both the covariance matrices of individual subarrays and the cross-covariance matrices of different subarrays. Besides, the proposed method can work directly on the signal subspace (ESS-SS), since the signal subspace contains all the information of the DOAs of incoming signals. After de-correlation, the subspace method ESPRIT is adopted to estimate the DOAs. Compared with conventional approaches, the proposed method is more powerful to de-correlate the correlation between signals, and also more robust to the noise impact. The proposed method is tested on numerical data in coherent scenarios, and compared with conventional approaches. Simulation results show that the proposed method has an enhanced resolving capability and a lower signal-to-noise ratio threshold.

Neural network model for 2D DOA estimation of two coherent sources

This paper presents an artificial neural network (ANN)-based model for two-dimensional direction of arrival (DOA) estimation of two coherent sources. The proposed model is composed of two neural networks, one for each radiating source. Datasets for training and testing of the neural model are formed assuming narrowband signal model and a 4 × 4 rectangular antenna array at the receiver. Unlike MUSIC algorithm with the spatial smoothing pre-processing (SSP) scheme, the ANN model is available to separate two closely spaced coherent sources. Comparison results are presented to show advantages of the neural model both in terms of accuracy and speed of calculation.

Accurate Estimation of the Number of Weak Coherent Signals

Estimating the number of signals (NIS) is an important goal in array signal processing, such as direction-of-arrival (DOA) estimation. A common approach for solving this problem is to use an eigenvalue of the array covariance matrix and information criterion, such as the Akaike information criterion (AIC) and minimum description length (MDL). However they suffer serious degradation, when the incoming signals are coherent. To estimate the NIS of the coherent signals impinging on a uniform linear array (ULA), a method for estimating the number of signals without eigendecomposition (MENSE) is proposed. The accuracy of the NIS estimation performance of MENSE is superior to the other algorithms equipped with preprocessing such as the spatial smoothing preprocessing (SSP) and forward/backward spatial smoothing techniques (FBSS) to decorrelate the coherency of signals. Instead of using SSP or FBSS preprocessing, MENSE uses the Hankel correlation matrices. The Hankel correlation matrices can not only decorrelate the coherency of signals but also suppress the influence of noise. However, in severe conditions like low signal-to-noise ratio (SNR) or a closely spaced signals impinging on a ULA, the NIS estimation metric of MENSE has some bias which causes estimation error. In this paper, we pay attention to the multiplicity defined by the ratio of the geometric mean to the arithmetic mean. Accordingly, we propose a new estimation metric that has less bias than that in MENSE. The Computer simulation results show that the proposed method is superior to MENSE in the above severe conditions.

Efficient technique for two‐dimensional scattering center extraction and ISAR image formation

AbstractThis article presents efficient technique of two‐dimensional (2D) scattering center extraction and inverse synthetic aperture radar (ISAR) image formation of a target. The proposed technique uses a sparse scanning order conception and modified spatial smoothing preprocessing (MSSP). The MSSP is used to construct autocorrelation matrix. In general, 2D IFFT (inverse fast Fourier transform) is widely used to obtain 2D scattering center and ISAR image. This method is computationally efficient, but has drawbacks that are poor in terms of resolution. To overcome these shortcomings with the IFFT‐based method, various high‐resolution techniques were developed. The proposed algorithm enables us to provide exact scattering centers and ISAR image of a target. In order to show the performances of the proposed technique, we use backscattered field data of the ideal point scatterers and the F‐18 aircraft to estimate 2D scattering centers and obtain 2D ISAR image. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2173–2178, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23619

레이다 표적 인식에서 3D MEMP 기법을 이용한 표적의 3차원 산란점 예측

본 논문은 레이다 표적 인식에서 레이다 산란신호에 대한 3차원 산란점 추출을 위한 고해상도 기법에 대해 기술하고 있다. 또한, 3차원 산란점 추출에서 신호의 극점을 획득하기 위해 3차원 짝 맞춤 절차를 소개하고 있다. 짝 맞춤 절차는 기존의 일반적인 방법보다 더 정확하고 견고한 특징을 가지고 있다. 3차원 산란점을 추출하기 위해서는 우선 주어진 3차원 레이다 산란 데이터로부터 상호 분산 행렬을 생성해야 한다. 그리고 matrix pencil 기법을 이용하여 3차원 산란점을 추출한다. 본 논문에서 MSSP를 이용하여 상호 분산 행렬을 생성하였으며, 관측 행렬은 sparse scanning order conception 방법을 이용하여 만들었다. 제시한 기법의 성능을 보여주기 위해서 본 논문에서는 이상적인 점 산란체를 생성하여 이에 대한 결과를 보여주고 있다. This paper presents high resolution techniques of three-dimensional(3D) scattering center extraction for a radar backscattered signal in radar target recognition. We propose a 3D pairing procedure, a new approach to estimate 3D scattering centers. This pairing procedure is more accurate and robust than the general criterion. 3D MEMP(Matrix Enhancement and Matrix Pencil) with the 3D pairing procedure first creates an autocorrelation matrix from radar backscattered field data samples. A matrix pencil method is then used to extract 3D scattering centers from the principal eigenvectors of the autocorrelation matrix. An autocorrelation matrix is constructed by the MSSP(modified spatial smoothing preprocessing) method. The observation matrix required for estimation of 3D scattering center locations is built using the sparse scanning order conception. In order to demonstrate the performance of the proposed technique, we use backscattered field data generated by ideal point scatterers.

Accurate Angle-of-Arrival Estimation Method in Real System by Applying Calibration and Spatial Smoothing

Although subspace-based methods for estimating the Angle of Arrival (AOA) require a precise array response to achieve highly accurate results, it is difficult to obtain this response in practice even though the antennas are calibrated. Therefore, a method of compensating for errors in calibration is required. This paper proposes a procedure to enable precise AOA estimates to be obtained in a real system by applying array calibration and spatial smoothing preprocessing (SSP). Measured data were collected from experiments using two scenarios, i.e., in an anechoic chamber and at an open site, where a single source signal arrived at the array antenna. All measured data were then calibrated by using data obtained at 0 deg in an anechoic chamber before the AOAs were estimated. Nevertheless, errors in the array response remained after calibration because errors in the AOA estimates could still be observed. SSP was then applied to the calibrated data to obtain more accurate AOA estimates. We found that SSP can reduce the random error in an array response obtained in a real system, leading to reduced errors in AOA estimates in the observed data. To generalize the problem that SSP can reduce random perturbation in the array response, simple expressions are illustrated and verified by Monte-Carlo simulation. Random gain and phase errors in the array response are only considered in this paper and ESPRIT was used to estimate the AOAs.

Decorrelation Performance of Spatial Smoothing Preprocessing at Transmitter in the Presence of Multipath Coherent Waves

Direction of arrival estimation of coherent multipath waves by using superresolution technique often requires decorrelation preprocessings. Spatial smoothing preprocessings are the most popular schemes as the techniques. In mobile environment, position change of the target/transmitter often brings us decorrelation effect. In addition, multiple signals transmitted by an antenna array, such as a MIMO transmitter, can also cause the same effect. These effects can be categorized as the spatial smoothing preprocessing at the transmitter. In this paper, we analyze the spatial smoothing effect at the transmitter in the presence of multipath coherent waves. Theoretical and simulation results show that the spatial smoothing at the transmitter has a good feature in comparison with the conventional SSP at the receiving array. We also show that better decorrelation performance can be obtained when the SSPs at the transmitter and receiving array are applied simultaneously.

Measurement of direction-of-arrival of coherent signals using five-port reflectometers and quasi-Yagi antennas

The measurement results of detection-of-arrival (DoA) of coherent signals using five-port reflectometers and quasi Yagi antennas are presented. The receiver operating at 2.4GHz consists of eight five-port reflectometers and a uniform linear array (ULA) of eight 2.4GHz quasi-Yagi antennas. The DoA is estimated by measuring the phase difference of signals pitched by the antenna array and by using the MUSIC algorithm associated with Spatial Smoothing Pre-processing (SSP). The phase difference is determined using the five-port technique. The measurement results show that the DoA of coherent signals are well estimated with a maximum error of 2/spl deg/. In addition, we also show that it is capable of estimating the time delay and direction of arrival simultaneously using the five-port technique.

Direction-of-Arrival Estimation of Coherent Signals Using a Cylindrical Array

Mainly, a uniform linear array (ULA) has been used for DOA estimation of coherent signals because we can apply the spatial smoothing preprocessing (SSP) technique. However, estimation by a ULA has ambiguity due to the symmetry, and the estimation accuracy depends on the DOA. Although these problems can be solved by using a uniform circular array (UCA), we cannot estimate the DOA of coherent signals because the SSP technique cannot be applied directly to the UCA. In this paper, we propose to estimate 2-dimensional DOA (polar angles and azimuth angles) estimation of coherent signals using a cylindrical array which is composed of stacked UCAs.

Performance of spatial smoothingalgorithms for radar signature modelling

In the area of radar signature modelling, subspace-based methods have recently become very popular. To model radar signals using subspace-based methods, spatial-smoothing preprocessing (SSP) is essential to estimate the covariance matrix of the received signals. Here, the performances of two typical SSP techniques are compared in the context of radar signature modelling.

Prediction of the number of coherent signals for mobile communication systems using autoregressive modeling

AbstractOne effective technique for reducing multipath fading in mobile communications is introduction of directional diversity with the help of adaptive arrays. This technique requires direction of arrival (DOA) estimation of coherent signals and relative time delay estimation. Eigenvector analysis or maximum likelihood localization is an efficient technique to estimate DOA. However, for coherent signals, spatial smoothing preprocessing is required and information about the number of signals is needed.This paper proposes to use spatial autoregressive modeling and final prediction error (FPE) to estimate the number of coherent signals. Since FPE drastically decreases with p when the length of prediction filter p is less than the number of coherent signals k and FPE converges to a constant value determined by C/N when p is greater than k, the number of coherent signals can be estimated. Moreover, by appropriately defining the FPE threshold, the number of dominant coherent signals can be estimated under the C/N concept. Numerical experiment indicates that this technique can estimate the number of signals and DOA with sufficient accuracy. Further, by appropriately defining the FPE threshold, the number of dominant coherent signals can be estimated.

Superresolution techniques for time-domain measurements with a network analyzer

Superresolution techniques for time delay estimation are proposed and applied to frequency-domain data measured with a network analyzer. A MUSIC (multiple signal classification) algorithm preprocessed by spatial smoothing is used. The spatial smoothing preprocessing is performed to destroy signal coherence, and the decorrelation performance is examined in detail. The expression which gives an individual response is given. Using this expression, it is possible to eliminate unwanted signals that appear as ripples in the frequency domain. Experimental results show that the frequency bandwidth required by the MUSIC algorithm to resolve distinct time-domain responses and eliminate unwanted signals is much narrower than that required by the FFT (fast Fourier transform). Thus, the MUSIC algorithm is applicable to the time-domain measurements with the network analyzer and has much higher resolution capability than the conventional FFT techniques. The MUSIC algorithm is one of the most promising methods of enhancing the accuracy of measurement for narrowband devices such as antennas.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>