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.     

Equivalent transformation for a partially adaptive array

A partially adaptive array is one in which elements of a phased array are controlled or adaptively weighted in groups or in which certain elements, called auxiliary elements, are made controllable. Mathematically, this type of array is formed by transforming all of the elements of an array by a nonsquare matrix such that the resulting output vector has a length less than the number of array elements. It is shown that there is an equivalent matrix transform that can effectively be utilized in analyzing the partially adaptive array's performance when a small number of external jammers are present. Processor implementation and convergence rate considerations lead to the desirability of reducing the dimensionality of the cancellation processor while maintaining good sidelobe interference protection. A meaningful measure of canceller performance is to compute the optimal output signal-to-noise ratio. This expression is a function of the jammer, direction-of-arrival vectors (DOAVs), jammer powers, the array steering vector, and internal noise. It is shown that if this expression is computed for the fully adaptive array then it is easily computed for the partially adaptive array by transforming the jammer DOAVs and the steering vector by the orthogonal projection matrix defined by the rows of the subarray transformation matrix and substituting these vectors back into the original expression for the fully adaptive array     

Effect of Sampling Time Jitter on Array Performance

The effect on array SNR of errors in sampling times (jitter) isan analyzed for the "Bryn processor." Jitter is modeled as a discrete-paramter er random process, and expressions for array SNR are determined as a function of signal and noise spectra and jitter characteristics. For a plane wave signal, it is found that jitter in the data used to design the processor has no effect on output SNR if the noise is independent between channels. However, jitter in the input (evaluation) n) data can cause a substantial loss in peak array SNR and in the array's frequency selectivity. These losses can be expected to increase as the high-frequency content of the data increases. The Bryn processor also loses its interpretation as a likelihood ratio processor in the presence of jitter. The effect of jitter can be reduced in many cases by increasing the sampling rate.     

Multiplier Offset Voltages in Adaptive Arrays

The effects of multiplier offset voltages in adaptive arrays are examined. Multiplier offset voltages arise when active circuits are used to implement the error-by-signal multipliers required in an array based on the LMS algorithm. These offset voltages are known from experimental work to have a strong effect on array performance. It is first shown how multiplier offset voltages may be included in the differential equations for the array weights. Then their effect on weight behavior is studied. It is found that the offset voltages affect the final values of the weights, but not the time constants. Furthermore, the effect they have is influenced by the amount of element noise in the array. An adequate amount of noise is necessary to minimize weight errors due to offset voltages. An example is treated to show the effect of offset voltages on the final array weights and the output signal-to-noise ratio (SNR). With offset voltages present, it is found that there is a maximum SNR that can be obtained from the array. A specific input SNR is required to obtain this maximum output SNR. Finally, it is shown that a finite operating range for the weights places a further restriction on the acceptable values of offset voltages and noise.     

A fast adaptive null-steering algorithm based on output powermeasurements

Presented and investigated here is a simple and fast adaptive algorithm for linear power inversion arrays whose nulls can be accurately steered by controlling the element weights. Based on measurements of the powers of the three signals derived from the array output and the output of an auxiliary beamformer, the algorithm tracks unknown jammers in the environment by steering the nulls of the array one by one in a cyclical time-multiplexed manner. When compared with the least mean square (LMS) algorithm, the proposed algorithm has about the same implementation complexity, a better convergence behavior and the advantage that nulls are directly available     

Power Inversion Array in a Rotating Source Environment

The steady state performance of the Frost power inversion array is evaluated, assuming constant rotational velocity of the external noise environment in the sin ? domain. The weight vector is solved implicitly in terms of a linear matrix equation. Approximate criteria are derived for weight vector and output power deviation from optimal values, which are then applied to determine the maximum scan rate of a radar sidelobe canceler.     

一种MIMO雷达幅相误差估计方法

 针对单基地相关多输入多输出(MIMO)雷达中存在的阵列幅相误差问题进行了研究。给出了单基地相关MIMO雷达的阵列模型,并提出了一种MIMO雷达幅相误差估计方法。利用发射正交信号对阵列接收信号进行匹配滤波,可分离得到类似传统阵列的"虚拟阵列",利用分时信源数据将该阵列中真实导向矢量中信源波达方向(DOA)引起的相位与幅相误差分离开,通过构造代价函数得到波达方向估计值,进而分别得到发射阵与接收阵的幅相误差的估计值,同时给出了误差引入量分析。最后通过仿真验证了该方法的有效性。本文介绍的方法简单可行,适用于任意构型MIMO雷达的幅相误差估计。     

Computational Precision Requirements for Optimal Weights in Adaptive Processing

The required accuracy for computing the estimated optimum weights of an adaptive processor has been analyzed by investigating the effects of errors in computing the inverse matrix. It is shown that the required precision depends upon the matrix. An equation for the general case is derived. Several special cases are considered in detail. It is shown that the case of a single interference source requires the highest precision. The least stressing case is identifi'ed and compared to the worst case. The requirements for a "typical" case are also considered. A comparison of the requirements for the covariance matrix estimation technique and for adaptive weight implementation using gradient descent techniques is given. It is shown that there is a dichotomy in that cases that do not stress one technique tend to stress the other.     

Signal Resolution via Digital Inverse Filtering

Research in numerous areas is directed toward the resolution of multiple overlapping signals in a noisy environment. These areas include radar, sonar, speech, seismology, and electrophysiology. Sometimes matched filters are used; other times inverse filters are employed. This paper discusses one approach to the analysis of the resolution of inverse filters. Our method is to compromise the trade-off between signal resolution and the output signal-to-noise ratio (SNR). A performance measure for the inverse or deconvolution filter is defined as a quantity proportional to the harmonic mean of the resolution and the SNR. An optimum output pulse duration is obtained using this criterion, where the pulse shape has been previously selected and the input signal waveform is known. In addition, upper and lower bounds for the output pulse duration are presented. Graphs are given which allow the designer to select the optimum inverse filter output pulse duration for a desired signal resolution and an estimated SNR.     

Simplified covariance matrix measurement in adaptive arrays

A procedure to measure the covariance matrix of an antenna array directly from the array sum port is described. The procedure involves specifically chosen sets of antenna weights that will allow the matrix components to appear at the array output. A parallel method of measuring the covariance matrix is also described. The accuracy requirements of the power measurements are examined analytically and through simulation     

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   

Target Detection and Parameter Estimation for MIMO Radar Systems

We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.     

Analysis of array errors and a short-time processor in airbornephased array radars

Array errors are inherent in a realistic phased array radar system. The influence of array errors on the clutter degrees of freedom and the clutter subspace in an airborne phased array radar is analyzed. Based on the presented theoretic results, a method of short-time processing followed by coherent integration is proposed for clutter suppression in airborne phased array radars. It can approximate the two-dimensional optimal processor well even in the presence of array errors, clutter fluctuations and aircraft drift, with a considerable saving in computations     

Effects of finite weight resolution and calibration errors on theperformance of adaptive array antennas

Adaptive antennas are now used to increase the spectral efficiency in mobile telecommunication systems. A model of the received carrier-to-interference plus noise ratio (CINR) in the adaptive antenna beamformer output is derived, assuming that the weighting units are implemented in hardware, The finite resolution of weights and calibration is shown to reduce the CINR. When hardware weights are used, the phase or amplitude step size in the weights can be so large that it affects the maximum achievable CINR. It is shown how these errors makes the interfering signals “leak” through the beamformer and we show how the output CINR is dependent on power of the input signals. The derived model is extended to include the limited dynamic range of the receivers, by using a simulation model. The theoretical and simulated results are compared with measurements on an adaptive array antenna testbed receiver, designed for the GSM-1800 system. The theoretical model was used to find the performance limiting part in the testbed as the 1 dB resolution in the weight magnitude. Furthermore, the derived models are used in illustrative examples and can be used for system designers to balance the phase and magnitude resolution and the calibration requirements of future adaptive array antennas     

An Adaptive Array Signal Processing Algorithm for Communications

An algorithm is described for initial synchronization in a communication system with a digital adaptive array. This algorithm can also be used for message extraction. A set of consecutive complex video samples of the array output is processed to obtain optimum adaptive array weights, based on a least mean square (LMS) error criterion. This computation is performed for each of the possible alternative signals which may be present during an observation interval. The correct synchronization time or message symbol is selected as the one which yields the minimum LMS error. Assuming orthogonality of the alternative codes, a probability distribution for the output of this processor has been derived.     

Comparison of signal estimation using calibrated and uncalibrated arrays

We consider the problem of separating and estimating the waveform of superimposed signals received by an array of sensors. If the array is well calibrated it is possible to first estimate the directions of arrival (DOA) of the signals and then use this information to separate the signals. When the array is not calibrated, but the array elements have the same unknown gain pattern, up to an unknown multiplicative factor and the phases of the elements are arbitrary and unknown, it is possible to estimate the array steering vectors and then use this information for signal estimation. We compare the quality of the estimated signals in the calibrated case with the quality of the estimated signals in the uncalibrated case, in terms of the output signal-to-interference ratio (SIRO) and output signal-to-noise ratio (SNRO).     

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     

Reference Loop Phase Shift in Adaptive Arrays

Adaptive arrays for use in communication systems require the generation of a so-called reference signal, which is usually derived from the array output. A particular problem associated with this technique, the problem of reference loop phase shift, is discussed. It is shown that phase shift in the reference loop causes the array weights to cycle, and also causes the array to frequency-modulate the signal. In spite of this frequency change, the array maintains a maximum SNR at the output.     

A mean level adaptive detector using nonconcurrent data

Convergence results for a mean level adaptive detector (MLAD) are presented. The MLAD consists of an adaptive matched filter (for spatially correlated inputs) followed by a mean level detector (MLD). The optimal weights of the adaptive matched filter are estimated from one batch of data and applied to a statistically independent batch of nonconcurrent data. The threshold of the MLD is determined from the resultant data. Thereafter a candidate cell is compared against this threshold. Probabilities of false alarm and detection are derived as a function of the threshold factor, the order of the matched filter, the number of independent samples per channel used to calculate the adaptive matched filter weights, the number of samples used to set the MLD threshold, and the output signal-to-noise power ratio of the optimal matched filter. A number of performance curves are shown and discussed     

Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing

The optimum weights for an adaptive processor are determined by solving a particular matrix equation. When, as is usually true in practice, the covariance matrix is unknown, a matrix estimator is required. Estimating the matrix can be computationally burden some. Methods of decreasing the computational burden by exploiting persymmetric symmetries are discussed. It is shown that the number of independent vector measurements required for the estimator can be decreased by up to a factor of two.