DOA and polarization estimation via signal reconstruction with linear polarization-sensitive arrays

This paper addresses the problem of direction-of-arrival(DOA) and polarization estimation with polarization sensitive arrays(PSA), which has been a hot topic in the area of array signal processing during the past two or three decades. The sparse Bayesian learning(SBL) technique is introduced to exploit the sparsity of the incident signals in space to solve this problem and a new method is proposed by reconstructing the signals from the array outputs first and then exploiting the reconstructed signals to realize parameter estimation. Only 1-D searching and numerical calculations are contained in the proposed method, which makes the proposed method computationally much efficient. Based on a linear array consisting of identically structured sensors, the proposed method can be used with slight modifications in PSA with different polarization structures. It also performs well in the presence of coherent signals or signals with different degrees of polarization. Simulation results are given to demonstrate the parameter estimation precision of the proposed method.     

Direction-of-arrival estimation using signal subspace modeling

A computationally viable algorithm for estimating the direction-of-arrival (DOA) of multiple wavefields that are incident on an array of sensors is developed. The geometry of the array is unrestricted and the incident wavefields may be generated by mixtures of incoherent and coherent emitting sources. In the approach taken, the sensor signals are modeled as a noise-contaminated linear combination of steering vectors which are functionally dependent on a set of DOA parameters. These parameters are to be chosen so that this sensor signal model is most compatible with empirically measured data (i.e., snapshot data). An iterative procedure is developed for selecting the most data compatible set of DOA parameters in both the snapshot domain and the array covariance matrix domain. Critical to the success of such iterative solution procedures is the generation of quality DOA parameters to initialize the algorithm. A sequential beamforming method for this initialization is presented. Numerical examples to illustrate the effectiveness of the proposed algorithmic approach are given     

Efficient wideband signal parameter estimation using a radon-ambiguity transform slice

A novel efficient technique based on a single slice Radon-ambiguity transform (RAT) for time-delay and time-scale estimation is proposed. The proposed approach combines the narrowband cross-ambiguity function (NBCAF), the wideband cross-ambiguity function (WBCAF), and a single slice RAT to estimate multiple target parameters in noisy environments. The square modulus of Gaussian-enveloped linear frequency modulated (GLFM) signals has high-energy centrality in the ambiguity plane. Its peaks in the NBCAF fall along nearly straight lines whose slopes depend on the Doppler rates of the moving targets. These lines could be effectively detected by computing the entire Radon transform of the NBCAF for all possible angles; however, it is a computationally intensive procedure. It is shown that without calculating the entire RAT, it is possible to estimate target parameters using only a single slice of the RAT, i.e., using an appropriate projection of the NBCAF. It is demonstrated that the proposed method can successfully separate overlapping targets efficiently. The efficiency is achieved due to fast Fourier transform (FFT)-bascd processing, use of a single slice of RAT, and the use of only one-dimensional (1-D) searches.     

Covariance matrix estimation errors and diagonal loading inadaptive arrays

Simulations were used to investigate the effect of covariance matrix sample size on the system performance of adaptive arrays using the sample matrix inversion (SMI) algorithm. Inadequate estimation of the covariance matrix results in adapted antenna patterns with high sidelobes and distorted mainbeams. A technique to reduce these effects by modifying the covariance matrix estimate is described from the point of view of eigenvector decomposition. This diagonal loading technique reduces the system nulling capability against low-level interference, but parametric studies show that it is an effective approach in many situations     

A novel multistage estimation of signal parameters

A multistage estimation scheme is presented for estimating the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc. Such a situation arises, for example, in the case of the Global Positioning Systems (GPS). In the proposed scheme, the first-stage estimator operates as a coarse estimator of the frequency and its derivatives, resulting in higher RMS estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency (an event termed cycle slip). The second stage of the estimator operates on the error signal available from the first stage, refining the overall estimates, and in the process also reduces the number of cycle slips. The first-stage algorithm is a modified least-squares algorithm operating on the differential signal model and referred to as differential least squares (DLS). The second-stage algorithm is an extended Kalman filter, which yields the estimate of the phase as well as refining the frequency estimate. A major advantage of the proposed algorithm is a reduction in the threshold for the received carrier power-to-noise power spectral density ratio (CNR) as compared with the threshold achievable by either of the algorithms alone     

Linear FM signal parameter estimation from discrete-timeobservations

The authors consider the problem of estimating the parameters of a complex linear FM signal from a finite number of noisy discrete-time observations. An estimation algorithm is proposed, and its asymptotic (large sample) performance is analyzed. The algorithm is computationally simple, consisting of two fast Fourier transforms (FFTs) accompanied by one-dimensional searches for maxima. The variance of the estimates is shown to be close to the Cramer-Rao lower bound when the signal-to-noise ratio is 0 dB and above. The authors applied the algorithm to the problem of estimating the kinematic parameters of an accelerating target by pulse-Doppler radar. A representative test case was used to exhibit the usefulness of the algorithm for this problem, and to verify the analytical results by Monte Carlo simulations     

Angle and polarization estimation in a coherent signal environment

It is shown how a uniform linear array of crossed dipoles may be used with the ESPRIT algorithm and spatial smoothing techniques to estimate the arrival directions and polarizations of incoming coherent plane waves. Some examples showing typical performance are presented. One method of smoothing can be used where it is necessary to estimate both the arrival angles and polarizations of signals. Two other methods can be used when only the arrival angles are of interest     

Resolution of signal sources via spectral moment estimation

In different practical situations it is desired to estimate the number of signal sources and their positions in space or in frequency domain. The first problem is known as the detection or the order estimation and the second one as the resolution. For the resolution problem techniques such as nonlinear least squares (NLSM), high-order Yule-Walker method (HOYW), multiple signal classification (MUSIC), Pisarenko harmonic retrieval method, min-norm method, estimation of signal parameters by rotational invariance technique (ESPRIT), were proposed (Marple, 1987 and Stoica and Moses, 1997). All these high-resolution methods are based on the analysis of the signal covariance matrix. But the covariance matrix is not the only choice to represent the signal spectrum. In different applications (weather radars, synthetic aperture radar (SAR) signal processing, ultrasound imaging in medicine, atmospheric turbulence measurements) the signal spectrum can be modeled through its algebraic moments. Recently a number of efficient nonparametric methods have been proposed to estimate the algebraic spectral moments (Monakov, 1999). The presented paper is an attempt to solve the direction of arrival (DOA) problem via estimation of the algebraic spectral moments. A method proposed in the article is comparable in its accuracy with the MUSIC method. At the same time its computational burden is much lower. The method permits to estimate the signal power of sources easily to complete the full spectral line analysis. Additionally the method shows good robustness in situations when signal sources have noticeable spatial extend     

General direction-of-arrival estimation: a signal subspace approach

A high-resolution algorithm is presented for resolving multiple incoherent and coherent plane waves that are incident on an array of sensors. The incident sources can be a mixture of narrowband and broadband sources, and, the geometry of the array is unrestricted. The algorithm makes use of a fundamental property possessed by those eigenvectors of the array spectral density matrix that are associated with eigenvalues that are larger than the sensor noise level. Specifically, it is shown that these eigenvectors can each be represented as linear combinations of the steering vectors identifying the incident plane waves. This property is then used to solve the important special cases of incoherent sources incident on a general array and coherent sources incident on an equispaced linear array. Simulation results are presented to illustrate the high-resolution performance achieved with this approach relative to that obtained with MUSIC and spatial smoothed MUSIC in which the coherent-signal-subspace focusing method is used     

欺骗环境下GNSS信号估计与定位修正技术

欺骗信号以其极强的隐蔽性使卫星导航接收机难以察觉并迅速定位到错误位置,严重影响了卫星导航的安全性。现有抗欺骗技术需要其他导航系统辅助来修正受欺骗影响的定位解算,针对该问题,本文提出了一种GNSS欺骗信号参数估计与辨识方法,能够在欺骗干扰环境下估计并辨识出真实信号所对应的伪距,进而解算出接收机真实位置。该方法通过研究欺骗干扰下接收机相关值模型,在信号跟踪阶段建立真实与欺骗双信号状态模型与基于九路相关器输出的观测模型,利用扩展卡尔曼滤波（EKF）估计真实信号与欺骗信号的伪码延时与信号相关幅值,进而获得真实与欺骗伪距,在定位解算阶段利用改进观测量残差检测方法辨识出真实与欺骗伪距,最终使用真实伪距定位获得真实位置。仿真结果表明对相对码延时介于0.3~0.9 chip之间且欺骗/真实信号幅度比介于1~5之间的隐蔽欺骗攻击,所提方法的码延时估计误差约0.1 chip,可有效估计真实信号与欺骗信号参数,辨识出真实伪距,并使被欺骗的定位结果重新回到真实位置结果,改善GNSS接收机抗欺骗能力,提高卫星导航安全性。     

On radar detection and direction finding using sparse arrays

The potential performance of radar systems employing sparse arrays is investigated. Different sparse array geometries are evaluated with respect to detection and estimation performance. We present appropriate tools for analytical computation of the detection and estimation performance of the likelihood ratio test (LRT) and the maximum likelihood estimator (MLE), respectively. Particular attention is paid to the effect of ambiguity errors on system performance. The results show that sparse arrays have several advantages over nonsparse arrays, but that ambiguities deteriorate performance when sidelobe interference is present. Some ways to mitigate the ambiguity effects on system performance are briefly discussed.     

Distributed weather radar using X-band active arrays

Dense networks of short-range radars capable of mapping storms and detecting atmospheric and airborne hazards are described. Comprised of physically small, low-power antennas, these networks defeat the Earth curvature blockage that limits today's long-range radar networks and enable high resolution views that extend from the lower-troposphere to the tops of storms. The networks are comprised of 1-meter antennas that transmit 10's of W peak power and are capable of high-speed electronic beam-steering. A system architecture is described that maximizes the value accrued to users of radar data through utility functions that specify dynamic, optimal allocation of resources in response to the needs of multiple end-users and associated information retrieval algorithms.     

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation (CPPM) and linear frequency modulation (LFM), which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval (PRI) of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform (CHT) algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio (SNR) is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.     

基于超短基线声传感器阵的高速目标轨迹估计

针对低空高速飞行目标跟踪问题,首先研究了某典型目标噪声信号的时频特性,发现其信号呈现宽带低频特征,难以从频域对目标轨迹进行估计。在此基础上,从各路接收信号的到达时延量入手,考虑到声基阵只能布设于有限空间内的制约,提出了一种基于超短基线阵时延估计的目标跟踪方法。该方法利用各个超短基线阵接收声强极值点分别估计目标运动轨迹垂线方向,计算多个垂线的叉乘向量实现对目标运动方向的估计,再利用多面交汇的方式获估计得到目标运动轨迹。分别对目标俯仰角、方位角及运动轨迹估计的理论误差进行了推导,根据理论估计误差,为能够实现对目标运动轨迹的估计,各个超短基线阵应尽量保证与目标运动轨迹不在同一平面上。根据仿真结果,在采用4个传感器基阵时,角度估计平均误差在4°以内,位置估计相对误差在5%左右。仿真结果验证了该方法的有效性。     

Maneuver estimation using measurements of orientation

An image-based algorithm which provides an estimate of the current radial acceleration of a target is studied. Since the data in a single image frame provide information only on the orientation of the target, a sequence of frames must be processed to detect maneuvers. The estimation problem is parameterized in terms of natural groupings of measurement errors, and the influence of these errors on estimator fidelity is studied     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

False alarm control using Doppler estimation

A technique which uses maximum-likelihood estimates (MLEs) of target Doppler and target amplitude is developed for rejecting clutter residues. Multiple estimates are made and consistency checks are applied to the estimates. Simulation results indicate that for large clutter-to-noise ratios (C/N⩾55 dB) the probability of false alarm from clutter residues is reduced from 1.0 to below 0.01     

Modified input estimation technique using pseudoresiduals

An adaptive tracking filter for maneuvering targets is proposed using modified input estimation technique. Pseudoresiduals are defined using measurements and the velocity estimate at the hypothesized maneuver onset time. With the pseudoresiduals and a new target model representing transitions of nominal accelerations, a new input estimation method for tracking a maneuvering target is derived. Since the proposed detection technique is more sensitive to maneuvers than previous work, the shorter window length can be employed to detect and compensate target maneuvers. Also shown is that the tracking performance of the proposed filter is similar to that of interacting multiple model method (IMM) with 3 models, while computational loads of our method are drastically reduced