Improved CFAR performance in the littoral

Littoral operation of radars poses severe signal processing difficulties due to the highly stressing, inhomogeneous clutter. This report describes an initial investigation into the feasibility of utilising site-specific radar modelling to provide a localised estimate of the clutter statistics which can then be used to predict the required threshold to maintain a given false alarm rate. The technique has been applied to littoral clutter recordings obtained from the experimental S-band phased array radar, MESAR2. Results are presented for the technique in comparison with a conventional, non-adaptive, cell averaging CFAR. This paper concludes that significant performance enhancements are possible through the use of this new technique.     

Analysis of CFAR performance in Weibull clutter

Recent interest has focused on order statistic-based (OS-based) algorithms for calculating radar detection thresholds. Previous analyses of these algorithms are extended, to determine closed-form approximations for the signal-to-clutter ratio required to achieve a particular probability of detection in clutter environments whose amplitude statistics are modeled by the Weibull distribution, and where the clutter dominates receiver noise. Performance is evaluated in both homogeneous and inhomogenous clutter. The analysis shows that the OS-based algorithm is quite robust against both interference and clutter edges. A method is suggested for improving performance at clutter inhomogeneities for short-range targets     

The Detection Performance of the Siebert and Dicke-Fix CFAR Radar Detectors

The Siebert and the Dicke-fix CFAR radar detectors, used to maintain a constant false alarm rate (CFAR) in radar receivers under very similar circumstances, are considered. The Siebert detector represents the maximum-likelihood detection procedure for a signal in Gaussian noise of unknown power level, whereas the Dicke-fix makes use of a bandpass limiter to normalize the input and thus ensure a constant false alarm rate. The detection performance of the two detectors is determined and a comparison shows that over a wide range of parameters, the Dicke-fix introduces a loss which is approximately 1 B larger than for the Siebert detector.     

Optimal CFAR detection in Weibull clutter

Optimal, in the maximum likelihood sense, constant false-alarm rate (CFAR) detection for Weibull clutter statistics, is investigated. The proposed OW (optimal Weibull) estimator is proved to be an asymptotically efficient estimator of the mean power of the Weibull clutter. Theoretical analysis of the OW-CFAR detector is provided, while detection performance analysis is carried out using the Monte Carlo simulation method. The operation of the median and morphological (MEMO)-CFAR detector in Weibull clutter statistics is also explained. It performs almost optimally in uniform clutter and, simultaneously, it is robust in multitarget situations. The performance of the proposed OW-CFAR detector in uniformal Weibull clutter is used as a yardstick in the analysis of the MEMO cell-averager (CA) and ordered statistic (OS) CFAR detectors. Nonfluctuating and fluctuating (Swerling II) targets are considered in detection analysis. The performance of the detectors is also examined at clutter edges     

CFAR integration processors in randomly arriving impulseinterference

Time diversity transmission is often used to circumvent the high probability of a deep fade on a single transmission which may result in loss of the signal. One way to combat deep fades is to postdetection integrate the received observations from each range resolution cell. The false alarm rate of the postdetection integrator (PI) is extremely sensitive to randomly arriving impulse interference. Such interfering pulses may be unintentionally generated by nearby radars or intentionally generated by pulse jammers seeking to destroy the visibility of the radar. The binary integrator (PI) which uses an M-out-of-L decision rule is insensitive to at most M-1 interfering pulses. We consider the adaptive implementation of the PI and BI detectors for constant false alarm rate (CFAR) operation. We show that the CFAR BI detector when the “AND” (L-out-of-L) decision rule is used exhibits more robust false alarm control properties in the presence of impulse interference at the expense of severe detection loss when no interference is present. The CFAR adaptive PI (API) detector is proposed to alleviate this problem. The CFAR API detector implements an adaptive censoring algorithm which determines and censors with high probability the interference samples thereby achieving robust false alarm control in the presence of interference and optimum detection performance in the absence of interference     

Local SNR considerations in decentralized CFAR detection

We consider the decentralized detection problem, involving N sensors and a central processor, in which the sensors transmit unquantized data to the fusion center. Assuming a homogeneous background for constant false-alarm rate (CFAR) analysis, we obtain the performances of the system for the Swerling I and Swerling III target models. We demonstrate that a simple nonparametric fusion rule at the central processor is sufficient for nearly optimum performance. The effect of the local signal-to-noise ratios (SNRs) on the performances of the optimum detector and two suboptimum detectors is also examined. Finally, we obtain a set of conditions, related to the SNRs, under which better performance may be obtained by using decentralized detection as compared with centralized detection     

Adaptive cell-averaging CFAR detection in distributed sensornetworks

The problem of adaptive cell-averaging constant false-alarm rate (CFAR) detection is considered for two distributed sensor network topologies, namely the parallel and the tandem topologies. The compressed data transmitted amongst the detectors is assumed to be in the form of decisions. The overall systems are optimized to yield the maximum probability of detection for a fixed probability of false alarm. The performance of the systems is also analyzed     

改善彩色多功能显示器抗振性能的几条途径

基于某机载彩色多功能显示器的功能振动试验,提出了几种在改善抗振性能方面行之有效的、有针对性的对策,探索了信号抗干扰的原理及设计方法.     

Analysis of CFAR processors in homogeneous background

Five different constant false alarm rate (CFAR) radar processing schemes are considered and their performances analyzed in homogeneous and nonhomogeneous backgrounds, the latter specifically being the multiple target environment and regions of clutter transitions. The average detection threshold for each of the CFAR schemes was computed to measure and compare the detection performance in homogeneous noise background. The exponential noise model was used for clear and clutter backgrounds to get closed-form expressions. The processor types compared are: the cell-averaging CFAR, the `greatest of' CFAR, the `smallest of' CFAR, the ordered-statistics CFAR, and a modified ordered-statistics processor called the trimmed-mean CFAR     

Data quantization effects in CFAR signal detection

In this paper, we investigate data quantization effects in constant false alarm rate (CFAR) signal detection. Exponential distribution for the input data and uniform quantization are assumed for the CFAR detector analysis. Such assumptions are valid in the case of radar for a Swerling I target in Gaussian clutter plus noise and a receiver with analog square-law detection followed by analog-to-digital (A/D) conversion. False alarm and detection probabilities of the cell averaging (CA) and order statistic (OS) CFAR detectors operating on quantized observations are analytically determined. In homogeneous backgrounds with 15 dB clutter power fluctuations, we show analytically that a 12-bit uniform quantizer is sufficient to achieve false alarm rate invariance. Detector performance characteristics in nonhomogeneous backgrounds, due to regions of clutter power transitions and multiple interfering targets, are also presented and detailed comparisons are given     

Detectability Loss Due to "Greatest Of" Selection in a Cell-Averaging CFAR

Curves are presented showing the additional constant false-alarm rate (CFAR) loss which results when a "greatest of" logic is imple mented between the leading and lagging sets of reference cells. Thee analytical results for a square law detector and a Swerling case 1 fluctuating target are supplemented by simulation results for a nonfluctuating target, and envelope and logarithmic detector laws.     

CFAR detection in clutter with unknown correlation properties

We develop a constant false-alarm rate (CFAR) approach for detecting a random N-dimensional complex vector in the presence of clutter or interference modeled as a zero mean complex Gaussian vector whose correlation properties are not known to the receiver. It is assumed that estimates of the correlation properties of the clutter/interference may be obtained independently by processing the received vectors from a set of reference cells. We characterize the detection performance of this algorithm when the signal to be detected is modeled as a zero-mean complex Gaussian random vector with unknown correlation matrix. Results show that for a prescribed false alarm probability and a given signal-to-clutter ratio (to be defined in the text), the detectability of Gaussian random signals depends on the eigenvalues of the matrix R_c^-1R_s. The nonsingular matrix R_c and the matrix R_s are the correlation matrices of clutter-plus-noise and signal vectors respectively. It is shown that the “effective” fluctuation statistics of the signal to be detected is determined completely by the eigenvalues of the matrix R_c^-1R_s. For example the signal to be detected has an effective Swerling II fluctuation statistics when all eigenvalues of the above matrix are equal. Swerling I fluctuation statistics results effectively when all eigenvalues except one are equal to zero. Eigenvalue distributions between these two limiting cases correspond to fluctuation statistics that lie between Swerling I and II models     

CFAR detection of distributed targets in non-Gaussian disturbance

The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels (heterogeneous environment). We also assume that a set of secondary data, free of signal components, is available to estimate the correlation properties of the disturbance The proposed detector assumes no a priori knowledge about the spatial distribution of the target scatterers and ensures the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix and the power levels. Finally, the performance assessment, conducted modeling the disturbance as a spherically invariant random process (SIRP), confirms its validity to operate in real radar scenarios     

Analysis of an adaptive CFAR detector in non-Gaussian interference

Coherent signal detection in non-Gaussian interference is presently of interest in adaptive array applications. Conventional array detection algorithms inherently model the interference with a multivariate Gaussian random vector. However, non-Gaussian interference models are also under investigation for applications where the Gaussian assumption may not be appropriate. We analyze the performance of an adaptive array receiver for signal detection in interference modeled with a non-Gaussian distribution referred to as a spherically invariant random vector (SIRV). We first motivate this interference model with results from radar clutter measurements collected in the Mountain Top Program. Then we develop analytical expressions for the probability of false alarm and the probability of detection for the adaptive array receiver. Our analysis shows that the receiver has constant false alarm rate (CFAR) performance with respect to all the interference parameters. Some illustrative examples are included that compare the detection performance of this CFAR receiver with a receiver that has prior knowledge of the interference parameters     

Computation of the Noncentral F Distribution (CFAR)Detection

A formula is given that enables accurate values of the cumulative noncentral F distribution to be calculated. Since it does not seem to be generally known that constant false alarm rate (CFAR) detection problems are concerned with evaluating the noncentral F distribution, results using the algorithm are compared with published values obtained using a CFAR algorithm. Both engineers and statisticians are therefore encouraged to consult each other's publications for useful (though maybe disguised) material.     

Analysis of Some Modified Cell-Averaging CFAR Processors in Multiple-Target Situations

A serious degradation of detection probability (Pd) in a cellaverging constant false alarm rate (CA-CFAR) detector is known to be caused by the presence of an interfering target in the set of reference cells. A technique which is often used to prevent excessive false alarms at clutter ?edges? is a ?greatest of? (GO) selection between the leading and lagging sets of cells (GO-CFAR). However, it is demonstrated for a Rayleigh target that the abovementioned suppression effect is more acute in the GO-CFAR. Practically, detection of closely separated targets is almost inhibited. Selection of the ?smallest of? (SO) the means for the adaptive threshold has been proposed to alleviate this problem. An analytic expression for Pd of this detector is also derived, and it is shown that though it does prevent the suppression effect, a large sensitivity loss is introduced unless the number of reference cells is sufficiently large. A modified CO-CFAR detector, combining a ?censoring? circuit, is proposed for automatic detection in a complex nonhomogeneous environment.     

Knowledge-based system for multi-target tracking in a littoral environment

The paper addresses how to efficiently exploit the knowledge-base (KB), e.g. environmental maps and characteristics of the targets, in order to gain improved performance in the tracking of multiple targets via measurements provided by a ship-borne radar operating in a littoral environment. In this scenario, the nonhomogeneity of the surveillance region makes the conventional tracking systems (not using the KB) very sensitive to false alarms and/or missed detections. It is demonstrated that an effective use of the KB can be exploited at various levels of the tracking algorithms so as to significantly reduce the number of false alarms, missed detections, and false tracks and improve true target track life. The KB is exploited at two different levels. First, some key parameters of the tracking system are made dependent upon the track location, e.g., sea, land, coast, meteo zones (i.e., zones affected by meteorological phenomena) etc. Second, modifications are introduced to cope with a priori identified regions nit hi high clutter density (e.g. littoral areas, roads, meteo zones etc.). To evaluate the behavior of the proposed knowledge-based tracking systems, extensive results are presented using both simulated and real radar data