A sampling-based approach to wideband interference cancellation

Classical adaptive array schemes which use only complex spatial weights are inherently narrowband and consequently perform poorly when attempting to suppress wideband interference. The common solution to this problem is the use of tapped delay line filters in each spatial channel to facilitate space-time adaptive processing (STAP). The higher performance provided by the STAP architecture comes at the cost of a considerable increase in complexity. This paper presents a simpler technique based on programmable time adjustable sampling (TAS) that provides a limited number of wideband degrees of freedom. Two TAS methods are introduced: TAS-sidelobe canceler (TAS-SLC) is based on the sidelobe canceler, while TAS-minimum variance beamformer (TAS-MVB) is derived from the minimum variance beamformer. TAS is implemented by adjusting the sampling instant at selected array channels. TAS-SLC consists of controlling the sampling in the main channel of the sidelobe canceler With TAS-MVB array complex weights are substituted with TAS time delays. The performance of TAS methods with wideband interference is compared to the conventional sidelobe canceler and minimum variance beamformers. It is shown that TAS-SLC provides better performance than the sidelobe canceler, while TAS-MVB outperforms the minimum variance beamformer     

Theory of partially adaptive radar

This work extends the recently introduced cross-spectral metric for subspace selection and dimensionality reduction to partially adaptive space-time sensor array processing. A general methodology is developed for the analysis of reduced-dimension detection tests with known and unknown covariance. It is demonstrated that the cross-spectral metric results in a low-dimensional detector which provides nearly optimal performance when the noise covariance is known. It is also shown that this metric allows the dimensionality of the detector to be reduced below the dimension of the noise subspace eigenstructure without significant loss. This attribute provides robustness in the subspace selection process to achieve reduced-dimensional target detection. Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detector can outperform the full-dimension detector when the noise covariance is unknown, closely approximating the performance of the matched filter.     

Cyclostationarity-exploiting direction finding algorithms

Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. By exploiting cyclostationarity, the signal direction of arrival (DOA) estimation can be significantly improved. We propose two new direction finding beamformer algorithms that exploit cyclostationarity. These algorithms show very attractive estimation performance over conventional beamforming methods, as depicted by simulation results.     

Low-Error Bearing Estimation in Shallow Water

The multipath effect due to sound propagation in shallow water causes bearing errors in horizontal line arrays for all angles different from broadside when conventional resolution methods like beamformer or maximum entropy method are used. Error-free bearing estimates can be achieved only by means of generalized power estimators which use a priori knowledge about the spatial channel statistics. This a priori knowledge is usually unknown. The method described makes use of a pilot source which transmits training signals in order to achieve an estimate for a suboptimum spatial signal covariance matrix which is used for field matching.     

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     

鲁棒成形极化敏感阵列波束的方法及极化估计

基于极化敏感阵列,提出了一种鲁棒成形阵列波束的方法。该方法首先将阵列的数据模型进行了重新描述,从而获得了信号波达角(DOA)和极化解耦的模型。借助于该模型并对信号的两个极化方向分别进行鲁棒约束,设计出了一个新的鲁棒空域波束空间成形矩阵,利用该矩阵可以获得信号两个极化分量的鲁棒估计。基于特征值分解的方法,最后给出了估计信号极化参数的方法。分析和数值仿真实验均表明:提出的方法,在对DOA估计误差以及阵列位置误差等造成的阵列失配具有较强鲁棒性的同时,也能有效抑制干扰和噪声,进而提升了极化参数估计的性能。     

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     

Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree of freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beam forming techniques are demonstrated using high-fidelity simulated X-band radar data     

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.     

Optimal weight extraction for adaptive beamforming using systolicarrays

Systolic algorithms and architectures for parallel and fully pipelined instantaneous optimal weight extraction for multiple sidelobe canceller (MSC) and minimum variance distortionless response (MVDR) beamformer are presented The proposed systolic parallelogram array processors are parallel and fully pipelined, and they can extract the optimal weights instantaneously without the need for forward or backward substitution. We also show that the square-root-free Givens method can be easily incorporated to improve the throughput rate and speed up the system. As a result these MSC and MVDR systolic array weight extraction system are suitable for real-time very large scale integration (VLSI) implementation in practical radar/sonar system     

An intelligent algorithm for coherent sound source localization based on a strong tracking filter

Background noise is inevitable when sensor arrays are used for aeroacoustic measurements in wind tunnels. The direct removal of background noise, however, would affect the measurement accuracy. In particular, the existing array signal processing algorithms are either invalid or inefficient for removing the noise that is coherent with the signal of interest. In this paper, an intelligent algorithm is developed to localize the coherent sound sources and background noise in real time by iteratively checking the collected data from the array. The proposed method can automatically adjust the suboptimal fading factor to extract useful information as much as possible in residual sequence. This algorithm is tested in simulations and then demonstrated in an experiment.Compared to the two existing methods, the results indicate that the new method has a good phase-shift tracking ability and rapid estimation-error convergence speed, and can achieve an acceptable performance even for low-cost acoustic sensors. Overall, the proposed method should assist array beamforming and hence benefit aeroacoustic measurement.     

Circular array STAP

Traditionally, space-time adaptive processing (STAP) for airborne early warning (AEW) radar has been applied to uniform linear arrays (ULAs). However, when considering the overall radar system, electronically scanned circular arrays have advantages: a better combination of even and continual angular and temporal coverage, and mechanical simplicity because it does not need to rotate. This paper answers the question “How well does STAP perform when applied to a circular array?” This paper shows that for the AEW mission, circular arrays are indeed STAP compatible. However, when conventional STAP algorithms are used there may be a small loss in performance when compared with a ULA. With some care in the choice and implementation of the STAP algorithm, the majority of the degradation is at close ranges, where the target returns are relatively strong. At long ranges performance is barely affected. A STAP algorithm which compensates for the circular array environment and provides better performance than existing algorithms is presented     

Optimum Spatial Processing of Passive Sonar Signals

The spatial structure of the likelihood ratio array processor for detecting a monochromatic plane wave signal in Gaussian noise is compared with a conventional beam-forming detector. Conditions are determined under which the optimum detector performs significantly better than the conventional detector. Conditions are also found under which the beamformer is itself near optimal.     

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     

Integrated track maintenance for the PMHT via the hysteresis model

Unlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor data     

Eigenstructure-based algorithms for direction finding withtime-varying arrays

This paper considers the problem of finding the directions of narrowband signals using a time-varying array whose elements move during the observation interval in an arbitrary but known way. We derive two eigenstructure-based algorithms for this problem, which are modifications of techniques developed originally for time-invariant arrays. The first uses array interpolation, and the second uses focusing matrices. Like other eigenstructure-based methods, these algorithms require a modest amount of computations in comparison with the maximum likelihood (ML) estimator. The performance of the algorithms is evaluated by Monte-Carlo simulations, and is compared with the Cramer Rao Bound (CRB). Although both techniques were successful for wideband array processing with time-invariant arrays, we found that only the interpolated array algorithm is useful for direction finding (DF) with time-varying arrays     

增益幅度不同时信号二维方向角和多普勒频率的盲估计

 在各阵元增益幅度不一致的条件下,提出了一种起伏目标的二维方向角和多普勒频率盲估计的新方法。此方法在各阵元增益幅度不一致的条件下,仍可获得很好的估计性能,并能应用于各个信号的频率相同的场合。且具有对噪声不敏感,不需进行谱峰搜索,适用范围广等特点。仿真结果表明了此算法的有效性。     

Analysis of Min-Norm and MUSIC with arbitrary array geometry

A nonasymptotic performance comparison is presented between the Min-Norm and MUSIC algorithms for estimating the directions of arrival of narrowband plane waves impinging on an array of sensors. The analysis is based on a finite amount of sensor data. The analysis makes the assumption of high signal-to-noise ratio (SNR), and it applies to arrays of arbitrary geometry. It is shown that Min-Norm can be expressed as a certain data-dependent weighted MUSIC algorithm, and that this relationship allows a unified performance comparison. It is also shown that the variances of the estimated directions-of-arrival from the MUSIC algorithm are always smaller than those of the Min-Norm algorithm at high SNR when both algorithms employ a numerical search procedure to obtain the estimates     

FDI Performance of Two Redundant Sensor Configurations

A geometrical interpretation of the generalized likelihood test (GLT) approach to sensor failue detection and isolation (FDI) is provided, and analytical expressions are derived to evaluate FDI performance parameters of interest. These results are used to determine the FDI performance which can be achieved when various FDI decision strategies are applied to two redundant sensor configurations: a conical array of five single degree-of-freedom sensors and a dodecahedron array of six single degree-of-freedom sensors.     

Propagation and System Accuracy Impact of Major Sensor Errors on a Strapdown Aircraft Navigator

A strapdown system is considered as an unaided inertial navigator aboard an aircraft. Presented here are simulation results detailing the propagation of navigation errors (in nautical miles) due to strapdown sensor errors for four trajectories. They indicate the type of performance that can be expected from a strapdown system utilizing good ?off the shelf? gyros and accelerometers, and dramatically illustrate the improvements necessary in these components to obtain navigation performance comparable to that available from a good gimballed inertial system. The total navigation error for each trajectory is broken down to show the contribution from each of the various error sources. This breakdown quickly reveals which are the critical error sources for a given trajectory class, and also points up the relationship that exists between each individual error source, aircraft maneuvers, and the resulting navigation error. Several of the error mechanisms are discussed at length and a set of linearized differential equations which can be used to analyze error propagations is presented. These results should be of particular interest to the system designer who is faced with the problem of specifying the sensor error parameters necessary to meet mission performance requirements. With an analysis similar to the one presented here, but structured around his own expected mission trajectories, the designer should be able to confidently predict system accuracies and intelligently perform tradeoffs on the critical system parameters.