Numerical optimization method for array signal processing

This paper presents a novel and efficient extrema-mapping algorithm, which we call the roller-coaster algorithm. Two versions of the algorithm, the one-dimensional (1-D) and the two-dimensional (2-D) roller-coaster, are developed. Its applicability to array signal processing is demonstrated. We use it to solve a multiple source direction finding problem using multiple signal classification (MUSIC), beamformer, and minimum variance methods, and for antenna array design. The algorithm is based on heuristic assumptions and its properties are not proved. Yet, its performance was tested in many simulated experiments, yielding favorable results     

Median cascaded canceller for robust adaptive array processing

A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics; such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.     

Instrumental variable adaptive array processing

An instrumental variable (IV) approach is presented for estimating the weights of an adaptive antenna array. Theoretical analysis of the IV method shows that the antenna gain weights are independent of finitely correlated noise, so that unbiased estimation of signal arrival angles is possible. Only matrix inversions are required to compute the weight estimates. In this sense, the IV method provides performance comparable with eigenvector techniques but with lower computational burden. Both minimal and overdetermined IV estimators are derived. The overdetermined estimators give the same theoretical array weights as minimal estimators, but yield more accurate weight estimates in real data situations. Simulation results are presented to compare these IV methods with one another and with conventional matrix inversion weight estimators. In these examples it is seen that IV methods are able to resolve closely spaced interference sources when conventional matrix inversion techniques cannot. It is also shown that overdetermined methods are capable of providing weight estimates with lower variances than those of minimal methods     

Reiterative median cascaded canceler for robust adaptive array processing

A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.     

Dimension reduction for array processing with robust interference cancellation

We present a robust solution for data reduction in array processing. The purpose is to reduce the computation and improve the performance of applied signal processing algorithms by mapping the data into a lower dimension beamspace (BS) through a transformation. Nulls steering to interference are incorporated into a transformation using the subspace projection technique, and the BS spatial spectrum estimation accuracy is evaluated and maximized with a measure. The derived transformation tries to preserve the full-dimension Cramer-Rao bounds (CRBs) for the parameters of interest while rejecting undesired signals effectively. When compared with an optimal method and an adaptive approach, simulation results show that significant improvements are obtained in terms of BS direction-of-arrival (DOA) estimation root-mean-squared error (RMSE), bias, and resolution probability.     

Optimum array detector for a weak signal in unknown noise

We study the design of constant false-alarm rate (CFAR) tests for detecting a rank-one signal in the presence of background Gaussian noise with unknown spatial covariance. We look at invariant tests, i.e., those tests whose performance is independent of the nuisance parameters, like the background noise covariance. Such tests are shown to have the desirable CFAR property. We characterize the class of all such tests by showing that any invariant decision statistic can be written as a function of two basic statistics which are in fact the adaptive matched filter (AMF) statistic and Kelly's generalized likelihood ratio statistic. Further, we establish an optimum test in the limit of low signal-to-noise ratio (SNR), the locally most powerful invariant (LMPI) test. We also derive the bound for the probability of detection of any invariant detector, at a fixed false-alarm rate, and compare the LMPI and the published detectors (Kelly and AMF) to it     

A hardware signal processing platform for sensor systems

A field programmable gate array-based computing platform for high-speed sensors such as short-range radars is presented. The circuit performs necessary A/D conversions, raw data stream compression and target detection, and constructs a message structure suitable for external displays. In the prototype, USB is the connecting path to a laptop computer. An elementary pulse radar is utilized as an application example. An application interest would be in collision avoidance systems. Observed data transfer rates with real radar input signals were 36 Mbytes/s when typical target detection algorithms were applied.     

Advances in signal processing technology for electronic warfare

The denial of effective communications by enemy forces during hostile military operations has been a primary concern for military commanders since the inception of radio communications on the battlefield before World War II. Since then, the electromagnetic environment has been in a constant state of evolution toward more sophisticated jam-resistant and convert forms of modulation. For example, exotic modulation techniques employing spread spectrum (SS) signaling are routinely used by our adversaries to provide their communication links an advantage over US and Allied jammers. These same spread spectrum modulation techniques are being refined to provide convert, low probability-of-intercept (LPI) features to the unintended interceptor. The thrust of this paper focuses on developments in the theory and algorithms for detection, characterization, and exploitation of advanced waveforms using new mathematical signal processing tools. Specifically, quadratic time-frequency signal representations, wavelet transforms, and cyclostationary signal processing are introduced. This overview demonstrates the importance of these advanced techniques in a clear and concise manner. Applications and future research activities are described     

Sensor systems signal processing platform

A field programmable gate array-based computing platform for high-speed sensors such as short-range radars is presented. The circuit performs necessary A/D conversions, raw data stream compression and target detection, and constructs a message structure suitable for external displays. In the prototype, USB is the connecting path to a laptop computer. An elementary pulse radar is utilized as an application example. An application interest would be in collision avoidance systems. Observed data transfer rates with real radar input signals were 36 Mbytes/s when typical target detection algorithms were applied.     

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.     

Nonuniform array processing via the polynomial approach

A polynomial approach for maximum-likelihood (ML) estimation of superimposed signals in time-series problems and array processing was recently proposed. This technique was applied successfully to linear uniform arrays and to uniformly sampled complex exponential signals. However, uniformly spaced arrays are not optimal for minimum variance estimation of bearing, range or position, and uniform sampling of signals is not always possible in practice. The authors make use of the expectation-maximization algorithm to apply the polynomial approach to sublattice arrays and to missing samples in time-series problems     

An adaptive array detector with mismatched signal rejection

An adaptive detection algorithm with a sensibility parameter for rejecting unwanted signals is presented. This algorithm is a simple modification of the generalized likelihood ratio (GLR) detector (or test) for detecting a signal in zero mean Gaussian noise with unknown correlation matrix. Specifically, the adaptive detection algorithm is obtained by introducing an arbitrary positive scalar, which is called the sensitivity parameter, into the GLR detector as a multiplier of an already existing quadratic term. The GLR detector then becomes a special case of this detector for the unity sensitivity parameter. It is shown that the sensitivity parameter controls the degree to which unwanted signals are rejected. From numerical examples, it is demonstrated how the sensitivity parameter can be chosen such that unwanted signals, can be rejected while maintaining acceptable detection loss for slightly mismatched signals. Further insight into previous work on adaptive detection is also given     

Waveform fusion in sonar signal processing

In active sonar systems, proper selection of the transmitted waveform is critical for target detection and parameter estimation, especially with the existence of clutter (reverberation). Two commonly used waveforms (constant frequency (CF) and linear frequency modulated (LFM)) are studied. Their characteristics are complementary both with respect to their accuracies and their sensitivity to the blind zero-Doppler ridge. Several fusion schemes of the two kinds of waveforms are explored and fusion results are studied both analytically and from simulation. It is concluded that fusion of the information of different waveforms can be not only more robust, but in some cases outright preferable, in term of detection probability and estimation accuracy.     

Optimal number of phased array faces and signal processors for horizon surveillance

The problem of the optimal number of phased array faces for performing 360/spl deg/ horizon surveillance is considered. Assuming the detection performance is the same in all beam positions and the total number of T/R modules is constant, it is shown that the optimal number of array faces is three. This is true whether the arrays are operating simultaneously or sequentially. A parametric analysis is performed between the number of array faces operating simultaneously and the associated cost of simultaneous operation in terms of the size of the array.     

Recent advances in analog signal processing

Analog signal processors which perform convolution, correlation, matched filtering, and Fourier transformation are considered. Specifically, the authors focus on one- and two-dimensional signal processors using charge-coupled-device, surface-acoustic-wave, acoustooptic, and superconductive components. The device performances are compared in terms of parameters such as signal bandwidth, time-bandwidth product, and dynamic range. These analog components are compared with existing and planned digital processor designs. Potential applications are highlighted in wideband spread-spectrum packet-radio communication, radar range-Doppler imaging, wideband compressive receivers, and low-bit-rate image-coding systems     

脑电信号的最新研究方法

脑电信号是脑神经细胞电生理活动在大脑皮层或头皮表面的总体反映，脑电信号的研究一直是生物医学领域难度很大且倍受人们关注的课题。在简要回顾了脑电研究的历史和现状的基础上，重点论述了混沌分析法、人工神经网络(ANN)分析法、小波变换法、Wigner分布等在脑电信号分析和处理中的应用情况。最后展望了脑电信号研究的发展应用前景。     

Subspace array processing for the suppression of FM jamming in GPS receivers

The mitigation of FM interference in GPS receivers is considered. In difference to commonly assumed wideband and narrowband interferers, the FM interferers are wideband, but instantaneously narrowband, and as such, have clear time-frequency (TF) signatures that are distinct from the GPS coarse acquisition (C/A) spread spectrum code. In the proposed technique, the estimate of the FM interference instantaneous frequency (IF) and the interference spatial signature are used to construct the spatiotemporal interference subspace. The IF estimates can be provided using existing effective linear or bilinear TF methods. The undesired signal arrival is suppressed by projecting the input data on the interference orthogonal subspace. With a multisensor receiver, the distinctions in both the spatial and TF signatures of signal arrivals allow effective interference suppressions. The deterministic nature of the signal model is considered and the known underlying structure of the GPS C/A code is utilized. We derive the receiver signal-to-interference-plus-noise ratio (SINR) under exact and perturbed IF values. The effect of IF estimation errors on both pseudorange measurements and navigation data recovery is analyzed. Simulation results comparing the receiver performances under IF errors in single and multiantenna GPS receivers are provided.     

Tracker and signal processing for the benchmark problem with unresolved targets

Radar signal processing is particularly important in tracking closely spaced targets and targets in the presence of sea-surface-induced multipath. Closely spaced targets can produce unresolved measurements when they occupy the same range cell of the radar. These issues are the salient features of the benchmark problem for tracking unresolved targets combined with radar management, for which this paper presents the only complete solution to date. In this paper a modified version of a recently developed maximum likelihood (ML) angle estimator, which can produce two measurements from a single (unresolved) detection, is presented. A modified generalized likelihood ratio test (GLRT) is also described to detect the presence of two unresolved targets. Sea-surface-induced multipath can produce a severe bias in the elevation angle measurement when the conventional monopulse ratio angle extractor method is used. A modified version of a recently developed ML angle extractor, which produces nearly unbiased elevation angle measurements and significantly improves the track accuracy, is presented. Efficient radar resource allocation algorithms for two closely spaced targets and targets flying close to the sea surface are also presented. Finally, the IMMPDAF (interacting multiple model estimator with probabilistic data association filter modules) is used to track these targets. It is found that a two-model IMMPDAF performs better than the three-model version used in the previous benchmark. Also, the IMMPDAF with a coordinated turn model works better than the one using a Wiener process acceleration model. The signal processing and tracking algorithms presented here, operating in a feedback manner, form a comprehensive solution to the most realistic tracking and radar management problem to date.     

Model-based vision for automatic alarm interpretation

The further development of a previously reported knowledge-based vision recognition system for automating the interpretation of alarm events resulting from a perimeter intrusion detection system (PIDS) is reported. Measurements extracted over a sequence of digitized images are analyzed to identify the cause of the alarm. Models are maintained for alarm causes, and the scene and the measurements are matched with the models to derive an appropriate classification of the event. An overview of the initial classification system is provided, and modifications to the system are described. The results of applying the system to a range of real alarm events are presented, and further improvements are discussed