Cell-averaging Constant False Alarm Rate Research Articles

Automatic dual censoring cell-averaging CFAR detector in non-homogenous environments

Constant false alarm rate (CFAR) detectors are designed to regulate the false alarm rate against the noise and clutter background whose mean power is unknown and (or) varying. This is accomplished using local threshold estimate of the background observations. When background observations contain irrelevant samples, censoring is warranted to improve detection performance and to regulate the false alarm rate. In this paper, a new CFAR detector composed of an automatic dual censoring algorithm and cell-averaging CFAR detector is proposed. This new detector is named automatic dual censoring cell-averaging (ADCCA)-CFAR. The proposed detector does not require any prior knowledge about the background observation. It uses fuzzy membership function to determine and censor the unwanted samples in the reference window. The performance of the proposed detectors is evaluated and compared with those of the cell-averaging (CA), order statistic (OS) and automatic censoring cell-averaging based on ordered data variability (ACCA-OVD) CFAR detectors. The simulation results show that the ADCCA-CFAR detector not only provides low CFAR loss in homogenous background but also performs robustly in the presence of interfering targets and clutter edge situations.

Network Intrusion Detection Using CFAR Abrupt-Change Detectors

In this paper, the constant false alarm rate (CFAR) detectors are proposed for network intrusion detection. By using an autoregressive system to model the network traffic, predictor error is shown to closely follow a Gaussian distribution. CFAR detector approaches are then developed on the prediction error distribution. In the present study, we consider the optimal CFAR, the cell-averaging CFAR, and the order statistics CFAR. The use of these CFAR techniques can significantly improve the detection performance. In addition, we propose the use of fusion of these CFAR detectors by using Dempster-Shafer and Bayesian techniques. Computer simulations based on the DARPA traffic data show that the proposed approach achieves higher detection probabilities than the conventional detection method. Even under different types of attacks, the intrusion detection performances based on the proposed CFAR detectors shows consistent improvement.

Adaptive pseudo-noise code acquisition scheme using automatic censoring for DS/SS communication in frequency-selective Rayleigh fading channel

An adaptive pseudo-noise code acquisition scheme based on automatic multipath signal cancellation for mobile communication systems in frequency-selective Rayleigh fading channel is proposed. The proposed system combines a serial strategy and an automatic censored cell-averaging constant false alarm rate (CFAR) detector based on ordered data variability. This system does not require any a priori information about the number of interferences caused by the presence of multipath signals in the reference channel. The mean acquisition time and the detection performances of the proposed processor are evaluated and compared with those of the conventional adaptive acquisition scheme based on fixed-censoring point detector. It is shown that the considered scheme outperforms significantly the conventional one. The effects of various channel parameters on the acquisition performance, namely the number of resolvable paths, the partial correlation length and the signal-to-noise ratio are also investigated.

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

This paper deals with the constant false alarm rate (CFAR) radar detection of targets embedded in Pearson distributed clutter. We develop new CFAR detection algorithms-notably cell averaging (CA), greatest of selection (GO) and smallest of selection SO-CFAR operating in Pearson measurements based on a non-linear compression method for spiky clutter reduction. The technique is similar to that used in non uniform quantization where a different law is used. It consists of compressing the output square law detector noisy signal with respect to a non-linear law in order to reduce the effect of impulsive noise level. Thus, it can be used as a pre-processing step to improve the performance of automatic target detection especially in lower generalised signal-to-noise ratio (GSNR). The performance characteristics of the proposed CFAR detectors are presented for different values of the compression parameter. We demonstrate, via simulation results, that the pre-processed compression procedure is computationally efficient and can significantly enhance detection performance.

Noncoherent Constant False-Alarm Rate Schemes With Receive Diversity for Code Acquisition Under Homogeneous and Nonhomogeneous Fading Circumstances

In this paper, we address the cell averaging (CA), greatest of (GO), and smallest of (SO) constant false-alarm rate (CFAR) processors for code acquisition in a homogeneous and a nonhomogeneous environment. The performance characteristics of the CA, GO, and SO processors are analyzed and compared when receiving antenna diversity is employed in the pseudonoise code acquisition of direct-sequence code-division multiple-access systems. From the simulation results, it is observed that the GO CFAR scheme has the best performance in a nonhomogeneous environment and has almost the same performance as the CA CFAR scheme in a homogeneous environment.

Exploiting waveguide invariant reverberation striations for improved active sonar detection and classification

Active sonar reverberation level (RL) uncertainty is exacerbated in shallow water by multipath propagation and multiple interactions with the bottom. This affects, for example, the statistics of split‐window RL estimates used for cell‐averaging constant false alarm rate (CA‐CFAR) normalization of A‐scan returns. For larger signal bandwidths, target detection is impacted by more incoherent reverberation components causing higher background variance while, alternatively, using smaller bandwidth signals results in higher range variability of the mean due to coherent multipath modal interference. In this paper, we present an alternative to CA‐CFAR normalization that accounts for the frequency‐selective fading characteristics of the multipath channel. The idea is to use the waveguide invariant property to estimate the frequency‐dependent reverberation level at the range cell of interest using neighboring range cells at frequencies along striations in the time‐frequency distribution of the sonar return. An approximate generalized likelihood ratio test (GLRT) detector is derived and demonstrated on acoustic tank data as well as Mediterranean active sonar data collected during the SCARAB‐98 experiment [C. W. Holland, J. Acoust. Soc. Am. (2006)]. Detection and classification results indicate the considerable potential of a waveguide invariant‐based approach. [Work supported by ONR321US.]

Application of neural networks to radar signal detection in K-distributed clutter

Radar signal detection is a complex task that is generally based on conventional statistical methods. In real applications, these methods require a lot of computing to estimate the clutter parameters and that they are optimal only for one type of clutter distribution. Recently, artificial neural networks (ANNs) have been used as a means of signal detection. Following on from this work, the authors consider the problem of radar signal detection using ANNs in a K-distributed environment. Two training algorithms are tested, namely, the back-propagation algorithm, and genetic algorithms for a multi-layer perceptron (MLP) architecture and also for the radial basis function architecture. The simulation results show that the MLP architecture outperforms the classical cell-averaging constant false alarm rate and order statistics constant false alarm rate detectors.

Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments

The authors propose an automatic censored cell averaging (ACCA) CFAR detector based on ordered data variability (ODV) for nonhomogeneous background environments. The ACCA-ODV detector selects dynamically, by doing successive hypothesis tests, a suitable set of ranked cells to estimate the unknown background level. The proposed detector does not require any prior information about the background environment and uses the variability index statistic as a shape parameter to reject or accept the ordered cells under investigation. For implementation purposes, the authors suggest a two-level architecture in which both the successive ODV-based statistics and the corresponding hypothesis tests are processed simultaneously. The performance of the proposed detector is evaluated and compared with those of the OS-CFAR and the variability index-CFAR (VI-CFAR) detectors in various background environments. The results show that the ACCA-ODV detector acts like the CA-CFAR in a homogeneous background and performs robustly in nonhomogeneous environments.

Distributed IVI-CFAR detection in non-homogeneous environments

The performance of distributed constant false alarm rate (CFAR) detection using an improved version of the variability index (VI) CFAR as local detectors is analysed. The VI-CFAR which is a combination of the cell-averaging (CA) CFAR, smallest-of (SO) CFAR and the greatest-of (GO) CFAR is robust in non-homogeneous environments. In this work, we improve its robustness by making the VI-CFAR switch to the ordered statistics (OS) CFAR in multiple target situations where the interfering targets are present in both the halves of the reference window. Simulations for the “AND”, “OR” and the “MAJORITY” fusion rules are conducted to assess the detection performance and the results are presented and discussed.

연근해 소형 어선의 레이더 정보 수록 및 해석 시스템 개발 - CFAR에 의한 레이더 잡음 억제 -

연근해 소형 어선에 널리 탑재되어 있는 소형 레이더 장치에 radar target extractor를 인터페이스하여 레이더 선호를 수록, 처리 및 해석하기 위한 PC based radar system 을 구축하고, cell averaging CFAR 처리장치를 통해 실제의 레이더 echo 신호를 처리하여 오경보 확률의 설정치 변화에 따른 echo영상의 변화패턴을 레이더 스코프상에서 직접 비교, 분석한 결과 및 레이더 영상신호의 음영구역의 발생대역폭을 추정한 결과를 요약하면 다음과 같다. 1. 레이더 선호의 해석을 통해 추출된 표적의 운동벡터 및 방위, 거리, 속력, CPA, TCPA 등과 같은 ARPA 정보를 실시간으로 이동 궤적과 함께 PC 모니터상에 구현하고 있기 때문에 소형 레이더 시스템에 이 장치를 부착하면 저가의 비용으로써 ARPA 정보의 취득이 기능하다. 2. ideal threshold 에 의한 표적검출성능을 개선시키기 위해 cell averaging CFAR processor 의 CUT전후에 각각 3 개의 guard cell과 이 cell의 좌<TEX>$.$</TEX>우측에 각각 20개씩, 총 40개의의 reference cell를 설치하여 레이더의 video 신호를 입벽한 후, 오경보 확률 10<TEX>$\^$</TEX>-0.25/∼10<TEX>$\^$</TEX>-1.0/의 범위에 대하여 설정치를 점차증가시켜 갈 때, 레이더 영상신호는 10<TEX>$\^$</TEX>-0.75/ 의 설정치에서 가장 양호한 clutter 제거효과를 나타내었다. 3. 레이더 스코프상에서 영상신호를 관찰하면서 cell averaging CFAR 의 오경보 확률을 적정하게 제어하면 지금까지의 ideal threshold level 에 의한 잡음억제기법에서 나타나는 선박영상의 과도한 레벨약화현상을 보완할 수 있을 것으로 판단한다. 4. 부산 용호만에 정박중인 예인선의 레이더 신호를 해석하여 영상의 음영패턴과 음영 대역폭을 추정한 결과, 예인선의 유효높이는 약 1.2 m 이었고, 이들 음영효과의 정량적 해석을 통한 해상표적의 형상정보는 향후 3차원 레이더 영상을 구현하는 데 그 기초자료가 될 것으로 판단된다. This paper describes on the suppression of sea clutter on marine radar display using a cell-averaging CFAR(constant false alarm rate) technique, and on the analysis of radar echo signal data in relation to the estimation of ARPA functions and the detection of the shadow effect in clutter returns. The echo signal was measured using a X -band radar, that is located on the Pukyong National University, with a horizontal beamwidth of <TEX>$$3.9^{\circ}$$</TEX>, a vertical beamwidth of <TEX>$20^{\circ}$</TEX>, pulsewidth of <TEX>$0.8 {\mu}s$</TEX> and a transmitted peak power of 4 ㎾ The suppression performance of sea clutter was investigated for the probability of false alarm between <TEX>$l0-^0.25;and; 10^-1.0$</TEX>. Also the performance of cell averaging CFAR was compared with that of ideal fixed threshold. The motion vectors and trajectory of ships was extracted and the shadow effect in clutter returns was analyzed. The results obtained are summarized as follows；1. The ARPA plotting results and motion vectors for acquired targets extracted by analyzing the echo signal data were displayed on the PC based radar system and the continuous trajectory of ships was tracked in real time. 2. To suppress the sea clutter under noisy environment, a cell averaging CFAR processor having total CFAR window of 47 samples(20+20 reference cells, 3+3 guard cells and the cell under test) was designed. On a particular data set acquired at Suyong Man, Busan, Korea, when the probability of false alarm applied to the designed cell averaging CFAR processor was 10<TEX>$^{-0}$</TEX>.75/ the suppression performance of radar clutter was significantly improved. The results obtained suggest that the designed cell averaging CFAR processor was very effective in uniform clutter environments. 3. It is concluded that the cell averaging CF AR may be able to give a considerable improvement in suppression performance of uniform sea clutter compared to the ideal fixed threshold. 4. The effective height of target, that was estimated by analyzing the shadow effect in clutter returns for a number of range bins behind the target as seen from the radar antenna, was approximately 1.2 m and the information for this height can be used to extract the shape parameter of tracked target..

Nonparametric PN code acquisition using the signed-rank statistic for DS/CDMA systems in frequency-selective Rician fading channels

In this paper, signed-rank based nonparametric detectors are used for pseudonoise (PN) code acquisition in frequency-selective Rician fading channels. We first derive the locally optimum rank detector and then propose the locally suboptimum rank (LSR) and kth order modified signed-rank (MSR/sub k/) detectors using approximate score functions. We compare the hybrid parallel double-dwell scheme using the LSR and MSR/sub k/ detectors with that using the conventional squared-sum (SS) and modified sign (MS) detectors. From the simulation results, it is shown that the LSR and MSR/sub k/ detectors perform better than the SS detector using the cell averaging constant false-alarm rate processor and the MS detector by about 2-3 dB.

Frequency-domain partially adaptive array algorithm combined with CFAR technique

A frequency-domain partially adaptive beamformer is proposed to reduce the computational complexity of conventional frequency-domain adaptive beamformers. The proposed beamformer only adapts the weights that correspond to the frequency bins expected to contain interferences using a cell-averaging constant false alarm rate (CA-CFAR) processor. The false alarm rate is also changed adaptively to achieve optimum partial adaptation.

Intelligent CFAR processor based on data variability

An intelligent constant false alarm rate (CFAR) processor to perform adaptive threshold target detection is presented. It employs a composite approach based on the well-known cell averaging CFAR (CA-CFAR), smallest of CFAR (SO-CFAR), and greatest of CFAR (GO-CFAR) processors. Data in the reference window is used to compute a second-order statistic called the variability index (VI) and the ratio of the means of the leading and lagging windows. Based on these statistics, the VI-CFAR dynamically tailors the background estimation algorithm. The VI-CFAR processor provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments including multiple targets and extended clutter edges.

CFAR detector for high-resolution multi-look SARimage

Cell averaging and order statisic CFAR detection of gamma distributed targets against K-distributed clutter in multi-look SAR images are studied and formulated. Two numerical methods for evaluating the performances of the CFAR detectors are described. Numerical results in terms of detectability losses due to ‘spikiness’ of the clutter are plotted and discussed.

Cell-averaging CFAR detection in compound clutter with spatially correlated texture and speckle

The authors study the problem of a CA-CFAR, cell-averaging constant false alarm rate, detection of a Swerling II target model embedded in spatially correlated K-distributed clutter. The clutter model consists of the product of two components called `texture' and `speckle'. Two cases are analysed; in the first case, the speckle samples are correlated and the texture samples are independent while in the second one, both components of the texture and speckle are correlated. Simulations for integer and half-integer values of the shape parameter are conducted to assess the detection performance and the results are presented and discussed.

Performance of cell-averaging and order-statistic CFAR detectors processing correlated sweeps for multiple interfering targets

The performances of the cell-averaging (CA) and order-statistic (OS) CFAR detectors processing M correlated sweeps are analysed for the case where one or more interfering target returns are present in the set of reference cells used in estimating the clutter-plus-noise level. Exact expressions are derived for the detection probabilities of these schemes under Rayleigh fluctuating target model. The OS-CFAR performance for that case gives an integral equation which is evaluated numerically. Performances when sweeps are independent can be obtained easily as a special case.

Cell-averaging CFAR gain in spatially correlated K-distributed clutter

The detection performance of a cell-averaging CFAR detector is analysed for spatially correlated K-distributed clutter. In thermal noise or uniform clutter, the performance of the cell-averaging CFAR can be compared with that of the ideal fixed threshold, but with an associated ‘CFAR loss’, quantified by the increase in average signal-to-clutter ratio required to achieve a given probability of detection and probability of false alarm. In some types of clutter, knowledge of the overall amplitude distribution alone is not sufficient to assess the performance of a cell-averaging CFAR. In particular, sea clutter may exhibit significant spatial correlation, often associated with the sea swell. In such spatially varying clutter, the cell-averaging CFAR may be able to follow the local fluctuations, giving a considerable improvement in performance compared to the ‘ideal’ fixed threshold. This is quantified as a ‘CFAR gain’ with the limit of such improvement given by the performance of the ‘ideal CFAR’ detector. The CFAR gain or loss is illustrated in different clutter conditions for a range of cell-averager configurations. It is shown that significant CFAR gain, compared with the commonly accepted CFAR loss, can be achieved in some circumstances.

Threshold optimization for distributed order-statistic CFAR signal detection

Distributed signal detection schemes have received significant attention recently, but usually under the assumption of stationary observations which are independent from sensor to sensor. Here, order statistics based constant false alarm rate (OS-CFAR) detection techniques are applied to a distributed detection system with nonstationary observations where the signal observations are assumed to be dependent from sensor to sensor. Cases are considered where weak narrowband random signals are observed in additive Gaussian noise-plus-clutter of unknown power. Necessary conditions are given which specify the best sensor thresholds for some n-sensor cases. The best schemes using nonrandomized fusion rules are found for some specific two-sensor cases. The best schemes map use either AND or OR fusion rules depending on the specific false alarm probability and the number of reference observations used in the OS-CFAR scheme. Distributed OS-CFAR and cell-averaging CFAR (CA-CFAR) schemes are compared in terms of their capability to maintain false alarm probability in nonhomogeneous backgrounds. At least for the specific cases we have studied, there are OS-CFAR schemes which generally outperform the CA-CFAR schemes in this regard.

Mean-level detection in the frequency domain

Conventional mean-level detection (cell-averaging CFAR) is applied to frequency-domain data obtained by using the discrete Fourier transform (DFT) as a bank of Doppler filters. It is shown that the scalloping that results from use of the DFT not only results in a loss in SNR, but also causes a limiting, or compression, of the detection probability. This compression is due to the contamination of the mean-level estimate by the presence of significant signal components in all filters, which occurs when the Doppler frequency is not exactly the centre of a Doppler filter. Applying a window (e.g. Hamming) to the data does not alleviate the compression problem. However, it is shown that zero padding the data, which is the conventional approach to avoiding scalloping loss, also prevents compression of the detection probability when a technique called odd–even processing (OEP) is used. Results given show that using zero padding and OEP leads to only a small loss compared with optimum mean-level detection applied in the case when the Doppler frequency is assumed known.

Distributed CFAR detection in homogeneous and nonhomogeneous backgrounds

The performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds is analyzed. The ordered statistics (OS) CFAR detectors are employed as local detectors. With a Swerling type I target model, in the homogeneous background, the global probability of detection for a given fixed global probability of false alarm is maximized by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, the performance of the detection system is analyzed and its performance is compared with the performance of the distributed cell-averaging (CA) CFAR detection system.