A Fast Beamforming Algorithm for Large Arrays

This beamforming algorithm is written specifically for array radars in which the number of array elements K is very large compared with the number of jammers L the radar is designed to suppress. It uses a set of M noise vectors to construct a basis for the jammer component of the antenna output vectors. The component of the quiescent weight vector orthogonal to each basis vector is calculated, renormalized to unit length, and identified as the adapted weight vector. This algorithm is effective in the suppression of many types of jammers. The number of noise samples M required in the construction of the adapted weight vector is approximately equal to L. In the special case of L narrowband noise jammers, for example, a choice of M = L usually reduces the receiver output jammer power to a few dBs above the white noise background. It is permissible to have M相似文献   

Adaptive processing at the subarray level

This paper describes a class of partially adaptive arrays with adaptive processing applied to the outputs of steered subarrays. The problem is to detect a signal or estimate its direction of arrival in the presence of jammers. The advantage of applying adaptive processing to subarrays is that it requires much less CPU time than the corresponding fully adaptive processing. The subarray processing equations for the two kinds of problem are described. In this paper, we compare partially adaptive processing performance with fully adaptive processing performance in the case of the following antenna and signal sources:

• •- array with regularly spaced sensors ;
• •- between 20 and 100 elements ;
• •- between 3 and 20 subarrays ;
• •- a single jammer ;
• •- desired signal from the antenna zenith.

We suggest a method for determining the optimal subarray configurations in this case. An example is given to show that the performance of an antenna with five subarrays is comparable to that of a fully adaptive thirty-element array for eliminating a single jammer with a target at the zenith.     

The Effect of Random Steering Vector Errors in the Applebaum Adaptive Array

The effect of random errors in the steering vector of an Applebaum adaptive array is examined. Each component of the steering vector is assumed to have a random error component uncorrelated between elements. The array output signal-to-interferenceplus-noise ratio (SINR) is computed as a function of the error variance. It is shown that the array output SINR becomes more sensitive to steering vector errors as more elements are added to the array and as the received desired signal power becomes larger. The variance of the steering vector error that may be tolerated depends on the required desired signal dynamic range. The larger the dynamic range that must be accommodated, the smaller the error variance must be.     

Application of Adaptive Arrays to Suppress Strong Jammers in the Presence of Weak Signals

The purpose of this paper is to demonstrate that an adaptive array can be used to acquire weak signals, whose direction and timing are unknown, in an environment of stronger jammers. Specifically, it is shown that in an environment of one weak signal and one strong jammer, the adaptive array output suppresses the strong jammer below the weak signal by roughly the same amount that the jammer exceeded the signal before adaptation.     

Prediction of Adaptive Array Performance

A direct relationship between the conventional properties of an array and the array performance in an adaptive mode is given. Expressions are provided to obtain the output signal-to-interference-plus-noise ratio (SINR) of an adaptive array in terms of its conventinal pattern and the locations of the desired signal and jammers. These expressions permit one to evaluate the performance of an adaptive array without an exhaustive search for all possible scenarios and parametric values to ascertain that the required performance levels be met. In fact, one can predict the jammer locations for which the array will provide its best and worst performance by observing the conventional pattern. Several examples are provided to demonstrate the relationship between the conventional pattern and the adaptive array performance. The examples include both linear and planar arrays.     

Error Analysis of the Optimal Antenna Array Processors

The optimal weights of an antenna array processor, which maximizes the output signal-to-noise ratio (SNR) in the absence of errors, are computed using the noise-alone matrix inverse (NAMI) and the steering vector in the look direction or the signal-plus-noise matrix inverse (SPNMI) and the steering vector. In practice the estimated steering vector as well as the estimated optimal weights are corrupted by random errors. This paper has analyzed the effects of these errors on the performance of the NAMI processor and the SPNMI processor by deriving analytic expressions for the output signal power, output noise power, output SNR, and the array gain as a function of the error variance. The treatment is for a general array configuration and no assumption about a particular array geometry is made.     

Adaptive Arrays for Multiple Simultaneous Desired Signals

Steered beam adaptive arrays for multiple simultaneous desired signals are discussed. It is shown that the performance of a steered beam adaptive array depends upon the range of input signal strengths and the choice of the steering vector. Optimum steering vectors for various input signal strengths are given. All choices of steering vectors are equally effective in the rejection of jammers.     

基于约束最小冗余线阵与干扰对消的测向方法

针对有源干扰背景下信号源和干扰源的个数超过线阵的自由度而产生线阵饱和现象,提出一种将约束最小冗余线阵与干扰对消技术相结合的测向方法。通过将无源状态和有源状态下线阵输出数据的协方差矩阵进行对消运算去除有源干扰和噪声分量,并对约束最小冗余线阵的波达方向(DOA)估计算法进行改进,构造了新的协方差Toeplitz矩阵,有效抑制了由阵列非均匀性导致的伪峰,提高了阵列的DOA估计性能。仿真结果表明:该算法在低信噪比背景下具有抗有源干扰能力,扩展了阵列孔径,并具有较高的测向精度和鲁棒性。     

Regenerative hybrid arrays for interference suppression

The authors propose two regenerative hybrid adaptive arrays in which a self-generated reference signal is obtained through a detection-modulation procedure from the array output. The proposed arrays do not depend on spectrum spreading and are therefore applicable when this feature is not available. It is shown that at steady state the performance of these regenerative hybrid arrays is approximately the same as that of a hybrid array with a perfect reference signal. As to transient behavior, these arrays are shown to converge if the available imperfect steering vector can result in a few dB output signal-to-interference-plus-noise ratio with the self-generated reference disabled     

Cancellation of jammers and jammer multipath in a GPS receiver

It is demonstrated by simulation that jammers and jammer multipath can be cancelled using an adaptive space-time array, leading to nearly ideal GPS performance     

CFAR detection and estimation for STAP radar

The algorithm presented here provides both a constant false-alarm rate (CFAR) detection and a maximum likelihood (ML) Doppler-bearing estimator of a target in a background of unknown Gaussian noise. A target is detected, and its parameters estimated within each range gate by evaluating a statistical test for each Doppler-angle cell and by selecting the cell with maximum output and finally comparing it with a threshold. Its CFAR performance is analyzed by the use of the sample matrix inversion (SMI) method and is evaluated in the cases of a fully adaptive space-time adaptive processing (STAP) and two partially adaptive STAPs. The performances of these criteria show that the probability of detection is a function only of the sample size K used to estimate the covariance matrix and a generalized signal-to-noise ratio. The choice of the number K is a tradeoff between performance and computational complexity. The performance curves demonstrate that the finer the resolution is, the poorer the detection capability. That means that one can trade off the accuracy of ML estimation with the performance of the CFAR detection criterion     

Performance of Digital Implementations of an Adaptive Processor

The performance of a digital implementation of an Applebaum-Howells type adaptive processor is analyzed for both a limiter and nonlimiter configuration. The performance is evaluated in terms of steady-state residue power, using either a single-pole filter or a perfect integrator to smooth the output of the correlation mixer. The latter filter is the more commonly used for digital implementations. It is shown that when using the perfect integrator filter for both the limiter and linear digital implementations, the steady-state average weight vector equals the optimum weight vector. Thus, for this filter, the steady-state residue power is the minimum possible for either implementation. When using the single-pole filter, neither implementation achieves the minimum possible steady-state residue power. The relative performance of the two implementations depends upon the relative gain settings. When the gains are adjusted to give comparable servo stability for the design maximum jammer power, a reasonable criterion for digital implementations because of analog to digital saturation, the limiter configuration always has smaller steady-state residue power.     

基于FPGA的自适应阵列协方差矩阵特征分解的实现

卫星导航抗干扰的过程中,对空间信号波达方向估计、干扰个数检测、最优权矢量的求解直接影响着导航接收机的抗干扰性能,而协方差矩阵的特征分解是这些算法实现的核心部分。根据自适应阵列天线获得的协方差矩阵的特性,基于双边并行Jacobi算法,实现了基于FPGA的协方差矩阵特征值和特征向量的求解,并通过在信号波达方向估计的应用进行了验证。另外,在实现的过程中对直接调用CORDIC IP核的方式进行了精度误差分析,并用一种双精度浮点的方式进行修正,提高了矩阵特征分解FPGA的实现精度,为导航抗干扰接收机性能的提升提供了有效的工程基础。     

The Steady-State Antenna Patterns of Adaptive Arrays

The antenna pattern of a receiving adaptive array of arbitrary three-dimensional geometry operating in an environment of K sources, one desired signal and (K - 1) jammers, is considered. It is shown that the adapted (voltage) antenna pattern of the array is a linear combination of K (or less) basis patterns, each of which is a function of one source only. We find that these basis patterns have a simple physical meaning, namely, the kth basis pattern is the pattern realized by the array when the transmission of source k is considered a desired signal and all other sources are turned off. When the array elements are isotropic, these basis patterns are retrodirective (that is, the mainlobe of the kth basis pattern points at source k). It had been shown that this property is also exhibited by a different decomposition of the adapted pattern in the special case of a single jammer (K = 2). In contrast, our decomposition which is simpler than the earlier one, yields retrodirective beams for all K. The simple, physically meaningful, pattern decomposition developed here is quite significant in the insight it provides regarding the basic underlying principles of adaptive arrays. It is also instrumental in elucidating their capabilities and limitations.     

Detection Loss of the Sample Matrix Inversion Technique

The sample matrix inversion (SMI) technique is used for Doppler and/or array processing. Previous analysis of the technique has been in terms of signal-to-interference plus noise ratio (SINR). For Gaussian statistics, this performance measure gives the same loss values as does a probability of detection analysis for linear-time invariant systems. It is often somewhat less valid for nonlinear or time variant systems. As SMI is a nonlinear technique, a probability of detection analysis has been performed. It is shown that the detection loss is larger than that computed by the SINR measure. It is also shown that though the loss predicted by the SINR measure only depends upon the number of measurements used to estimate the covariance matrix, the detection loss depends upon the false alarm probability and the number of adaptable elements in addition to the number of measurements.     

Adaptive array CFAR detection

Presented here is a large class of adaptive array detection algorithms with constant false alarm rate (CFAR), so that the false alarm rate can be set to any preassigned number without knowledge of the noise covariance matrix. This class map incorporate any usual method of cell averaging and any method for array weight vector synthesis. A sufficient condition for CFAR is derived, which is easy to satisfy in practice. Basic system parameters are discussed. An example of detection performance for a simple cell-averaging detector, in which the array weight vector is synthesized by the method of diagonal loading, is provided using Monte Carlo simulations     

Outlier resistant adaptive matched filtering

Robust adaptive matched filtering (AMF) whereby outlier data vectors are censored from the covariance matrix estimate is considered in a maximum likelihood estimation (MLE) setting. It is known that outlier data vectors whose steering vector is highly correlated with the desired steering vector, can significantly degrade the performance of AMF algorithms such as sample matrix inversion (SMI) or fast maximum likelihood (FML). Four new algorithms that censor outliers are presented which are derived via approximation to the MLE solution. Two algorithms each are related to using the SMI or the FML to estimate the unknown underlying covariance matrix. Results are presented using computer simulations which demonstrate the relative effectiveness of the four algorithms versus each other and also versus the SMI and FML algorithms in the presence of outliers and no outliers. It is shown that one of the censoring algorithms, called the reiterative censored fast maximum likelihood (CFML) technique is significantly superior to the other three censoring methods in stressful outlier scenarios.     

Cancellation of specular and diffuse jammer multipath using ahybrid adaptive array

It has been demonstrated that specular or diffuse jammer multipath can be canceled to a desired level by using an adaptive array that combines bandwidth partitioning with tapped delay lines. Such hybrid systems are studied. In particular, the author studies an ideal two-element array that uses bandwidth partitioning in both the main and auxiliary channels, with an Mth-order adaptive finite impulse response filter in each subband of the auxiliary. The ability of this system to cancel specular moderately diffuse and diffuse multipath is studied. The combinations of bandwidth partitioning and filter order that can achieve a specified jammer cancellation level are discussed     

The Performance of an LMS Adaptive Array with Frequency Hopped Signals

The performance of an LMS adaptive array with a frequency hopped, spread spectrum desired signal and a CW interference signal is examined. It is shown that frequency hopping has several effects on an adaptive array. It causes the array to modulate both the amplitude and the phase of the received signal. Also, it causes the array output SINR (signal-to-interference-plus-noise ratio) to vary with time and thus increases the bit error probability for the received signal. Typical curves of the desired signal modulation and the time-varying SINR at the array output are presented. It is shown how the array performance depends on hopping frequency, frequency jump size, interference frequency, signal arrival angles, and signal powers.     

空域数据重排的后多普勒自适应处理方法

传统的后多普勒自适应处理方法,如因子法(FA)和扩展因子法(EFA)虽然能大大降低自适应处理时的运算量和独立同分布样本的需求量,但由于实际中均匀训练样本数目的限制,当天线阵元数进一步增大时,FA和EFA抑制杂波和检测动目标的能力会显著恶化。针对这一问题,提出了一种空域数据重排的后多普勒自适应处理方法。该方法将多普勒滤波后的空域数据重排为一行列数相近的矩阵,空域滤波器权系数也表示成可分离的形式,从而得到一双二次代价函数,利用循环迭代的思想求解权系数。实验表明该方法具有快速收敛,所需训练样本少的优点,尤其在大阵列、小样本条件下该方法抑制杂波的性能明显优于FA和EFA。