The CFAR adaptive subspace detector is a scale-invariant GLRT

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986)     

A NEW CFAR DETECTOR WITH GREATEST OPTION

This paper presents a new CFAR detector based on Ordered Statistics (OS) and Cell-Averaging (CA) forming local estimates, and using Greatest-Of selection (GO) to form clutter power level estimate Z in test cell(OSCAGO). Under the Swerling II assumption, the analytic expressions of Pfa,Pd and ADT of this detector are derived, its detection performance in homogeneous background and in strong interfering targets environment are analyzed and compared it with OS, GOSGO detectors. The results show that the detection performance of OSCAGO in homogeneous background and in multiple-target situations are obviously better than those of OS and GOSGO. When the number of interfering targets is equal to certain value, the CFAR loss of OSCAGO is about 3dB less than that of GOSGO.     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.     

Automatic censoring CFAR detector for heterogeneous environments

In radar detection, many constant false alarm rate (CFAR) processors have been proposed in the literature. It is well known that a processor is optimal only for one type of environment and that its detection performances are seriously degraded in presence of unknown irregularities. In such situations, the main difficulty resides in the estimation of the background configuration. That is, depending upon the non-homogeneity of the environment, one would choose the adequate optimal detection algorithm among a variety of known conventional ones that offer the best detection probability. Based on unknown transitions; i.e., in the presence of a priori unknown numbers of interfering targets and/or clutter edge, we propose an automatic censoring CFAR (AC-CFAR) detector for heterogeneous Gaussian clutter. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The proposed detector dynamically switches to the optimal conventional detector CA-, CMLD- or TM-CFAR. The performances of the proposed detector is evaluated and compared to existing detectors in various background situations. Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed detector exhibits considerable robustness in the presence of interfering targets and/or clutter-edge situations.     

A subspace method for space time adaptive processing

The problem of space-time adaptive processing (STAP) using a nonlinear array is considered. A key part of STAP is the estimation of the space-time covariance matrix of the received data. The conventional method of doing this causes significant performance degradation at short ranges because of the nonstationarity of the data. We present an alternative algorithm which circumvents this problem by projecting the data on the subspace orthogonal to the clutter and jammer subspaces. The clutter subspace is computed from the known array manifold, while the jammer subspace is estimated from clutter-free measurements. Numerical examples illustrate the performance improvement achieved at short ranges.     

一种基于拟合优度检验的恒虚警检测方法

针对传统的基于滑窗自适应门限恒虚警检测方法在非高斯环境下和多目标干扰环境下,性能下降的问题,提出了一种基于拟合优度检验的恒虚警检测方案,该方法利用了目标回波与背景杂波统计特性的差异,通过检验待检单元的回波样本是否服从背景分布来检测目标:如果待检单元样本服从背景分布,则有理由相信待检单元回波源于背景杂波,从而判断没有目标存在;否则,将判断有目标存在.和传统的基于自适应门限的检测方法相比,该方法受背景分布特性和干扰目标的影响很小.仿真实验表明,在尖锐的非高斯杂波环境下以及多目标干扰环境下,都能保持更优的检测性能.     

A fast CFAR detection space-time adaptive processing algorithm

All of the conventional CFAR detection algorithms that use space-time processing involve a time-consuming matrix-inversion operation. Based on today's technology, this computational complexity sometimes makes the full-rank solution difficult to realize. In this correspondence, a CFAR detection algorithm, which does not need a matrix inversion, is developed by an adaptation and extension of Hotelling's principal-component method studied recently by Kirsteins and Tufts (1994). Finally, the performance of the new CFAR test statistic is analyzed, and the effect of the rank reduction on performance is evaluated for an example scenario     

Robustness of a modified greatest-of CFAR detector

Robust detectors must be designed to obtain a constant probability of false alarm in an unknown clutter environment. A change to a standard `greatest-of? detector is proposed to adapt the threshold according to an estimated clutter shape factor. Results are presented for both Weibull and log-normal assumptions.     

An adaptive detector for deterministic signals in noise of unknownspectra using the Rao test

An adaptive detector for a known deterministic signal of unknown amplitude in Gaussian noise of unknown spectra is described. The detector is based on the Rao test, which is asymptotically equivalent to the generalized likelihood ratio. The detector achieves constant false alarm probability in the presence of large changes in input noise bandwidth and variance while providing optimum detection performance. The results are supported by simulation     

Optimality of the cell averaging CFAR detector

The cell averaging constant false alarm rate detector has been assumed to be optimal for detecting Swerling I targets embedded in exponential clutter and noise of unknown power. This is because the detector uses a minimum variance unbiased estimate (which is also a maximum likelihood estimate) of the unknown clutter-plus-noise power to set the threshold. The authors prove, using a result concerning least favorable distributions in composite hypotheses testing, that the cell averaging detector is indeed optimal in that it is a uniformly most powerful detector     

Peak-amplitude detector for sinusoidal signals

A circuit technique is proposed for detecting the peak amplitude of a sinusoidal signal using Pythagoras's law without requiring a simultaneous equal-amplitude quadrature signal. The number of four-quadrant multipliers required is only two.     

A blind adaptive decorrelating detector for CDMA systems

The decorrelating detector is known to eliminate multiaccess interference when the signature sequences of the users are linearly independent, at the cost of enhancing the Gaussian receiver noise. We present a blind adaptive decorrelating detector which is based on the observation of readily available statistics. The algorithm recursively updates the filter coefficients of a desired user by using the output of the current filter. Due to the randomness of the information bits transmitted and the ambient Gaussian channel noise, the filter coefficients evolve stochastically. We prove the convergence of the filter coefficients to a decorrelating detector in the mean squared error (MSE) sense. We develop lower and upper bounds on the MSE of the receiver filter from the convergence point and show that with a fixed step size sequence, the MSE can be made arbitrarily small by choosing a small enough step size. With a time-varying step size sequence, the MSE converges to zero implying an exact convergence. The proposed algorithm is distributed, in the sense that no information about the interfering users such as their signature sequences or power levels is needed. The algorithm requires the knowledge of only two parameters for the construction of the receiver filter of a desired user: the desired user's signature sequence and the variance of the additive white Gaussian (AWG) receiver noise. This detector, for an asynchronous code division multiple access (CDMA) channel, converges to the one-shot decorrelating detector     

A subspace method for direction of arrival estimation ofuncorrelated emitter signals

A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results     

A fast refinement for adaptive Gaussian chirplet decomposition

The chirp function is one of the most fundamental functions in nature. Many natural events, for example, most signals encountered in seismology and the signals in radar systems, can be modeled as the superposition of short-lived chirp functions. Hence, the chirp-based signal representation, such as the Gaussian chirplet decomposition, has been an active research area in the field of signal processing. A main challenge of the Gaussian chirplet decomposition is that Gaussian chirplets do not form an orthogonal basis. A promising solution is to employ adaptive type signal decomposition schemes, such as the matching pursuit. The general underlying theory of the matching pursuit method has been well accepted, but the numerical implementation, in terms of computational speed and accuracy, of the adaptive Gaussian chirplet decomposition remains an open research topic. We present a fast refinement algorithm to search for optimal Gaussian chirplets. With a coarse dictionary, the resulting adaptive Gaussian chirplet decomposition is not only fast but is also more accurate than other known adaptive schemes. The effectiveness of the algorithm introduced is demonstrated by numerical simulations     

基于子空间对齐与自适应CSP算法的运动想象脑电信号分类

在脑机接口中,让分类器从一个用户适应到另一个用户是具有挑战性的,但对于减少新用户的训练时间是必要的.但由于每个个体的神经信号存在着差异,常用的特征提取方法训练的分类器,应用于不同的用户时,准确率很低.因此本文提出了一种新的自适应共空间模式的特征提取方法,该算法通过选择合适的候选试验更新协方差矩阵,然后对提取的特征进行子...     

基于卷积构型的单元平均CFAR目标检测算法

提出一种基于卷积构型的单元平均恒虚警率(convolution based cell averaging constant false alarm rate, CCA-CFAR)快速检测算法.该算法首先根据背景杂波分布模型计算待检测合成孔径雷达(synthetic aperture radar, SAR)图像统计量矩阵, 然后对单元平均恒虚警率(cell averaging constant false alarm rate, CA-CFAR)检测器构建卷积模型, 利用卷积运算实现对背景杂波的矩估计, 并求出详细的背景杂波分布函数, 最后根据分布函数计算出每个像素的判定阈值, 并对所有待检测像素是否为目标点进行判定.该检测算法复杂度低, 运算效率高, 能够快速实现SAR图像实时目标检测.仿真实验证明了该方法的有效性和工程实用价值.     

An adaptive CFAR embedded system architecture for target detection

This paper presents field-programmable gate array (FPGA)-based novel forward and backward automatic censored cell algorithms using a Nios II core processor embedded on a Stratix II FPGA programmable device. These algorithms were recently presented for target detection in a nonhomogeneous environment, and they operate in a complementary manner to allow for high-resolution target detection with a time constraint fixed below 0.5 μs. The ACOSD-based constant false alarm rate detector does not require any prior information regarding the background environment and employs statistical analysis to dynamically calculate the threshold at which the ordered cells under investigation are accepted or rejected. The advantages of the proposed system lie in its simplicity and short processing time while maintaining a low development cost. For a reference window of 16 range cells, the experimental results obtained using the Stratix II development kit demonstrate that the proposed architecture works properly with a processing speed of 100 MHz and an overall detector execution time of 0.11 μs for each range cell. The designed hardware, which is an example of system-on-chip architecture, was physically realized in a Stratix II FPGA device, and the results are presented and discussed.     

Trimmed geometric mean order statistic CFAR detector for Pareto distributed clutter

Recent investigations have validated the Pareto class of models for radar backscattering from the sea surface for X-band maritime surveillance radar. As such, there has been much interest in the derivation of sliding window detectors, for operation in such clutter, with the constant false alarm rate property. A general expression is derived, allowing the determination of the probability of false alarm for such detectors, based upon a recently introduced invariant statistic. For a specific example of its application, a trimmed geometric mean order statistic constant false alarm rate detector is developed and compared with some recently derived detectors. It will be shown that this new detector can be designed to not only manage interference in the clutter range profile but can be very effective at managing range spread targets.     

A subspace approach to adaptive narrow-band interferencesuppression in DSSS

We address the problem of suppression of a digital narrow-band interferer in direct-sequence spread spectrum (DSSS) communications. We focus on the adaptive suppression method proposed by Honig, Madhow and Verdti (see IEEE Trans. Inform. Theory, vol.41, p.944-960, 1995) for wide-band interference, applying it to a narrow-band interferer. We identify the eigenspaces of the system dynamics to analyze the convergence of the adaptive version of the minimum mean square error (MMSE) algorithm for this application. Using this subspace approach we are able to: (1) significantly decrease the convergence times via a new constraint on the step size in adaptation; (2) introduce a simple parameterization of the mean output energy (MOE) and signal-to-interference ratio (SIR) to compare performance of various receivers; and (3) identify modes of operation where the algorithm will cease to effectively cancel interference. We propose a new adaptive receiver that avoids the convergence anomalies identified, while capitalizing on the new step size for faster convergence. Simulation results to support theoretical results are presented     

A quick response peak detector for variable frequency three-phasesinusoidal signals

An instantaneous peak detector for three-phase variable frequency sinusoidal signals is proposed. The three-phase characteristic is fully used in the proposed detector to achieve instantaneous response and frequency independence characteristics. A very simple hardware implementation circuit is also presented for minimizing the number of analog computational components. Moreover, the proposed detector possesses excellent linearity and low sensitivity to small voltage unbalance and harmonic distortion. Because of its promising accuracy and transient response, it can be used in many systems such as the voltage regulator of an uninterruptible power supply (UPS), an automatic line voltage regulator, or electric generators, etc., to improve the system transient performance. Theoretical analysis, hardware implementation, and some experimental results are also detailed in this paper