Multistage partially adaptive STAP CFAR detection algorithm

With oceanic reverberation and a large amount of data being the main sources of interference for underwater acoustic target detection, it is difficult to obtain a more robust detection performance by relying on the traditional constant false alarm rate (CFAR) detection method. An adaptive sonar CFAR detection method based on a back propagation (BP) neural network is proposed. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. This method uses a BP neural network to train the target echo signal to complete the clutter background classification and establish the clutter background recognition classification set. According to the output result of each classification, the best CFAR detector is selected from four CA/SO/GO/OS‐CFAR detectors to detect the target. The simulation results show the detection performance of the proposed method in a uniform environment, a multi‐target environment, and a clutter edge environment. The results show that the environment adaptability is strong for different clutter backgrounds, which further improves the control ability of false alarms under a non‐uniform background.

In this paper, we focus on the performance improvement of constant false alarm rate (CFAR) detector in heterogeneous Compound-Gaussian background. This paper is motivated by the fact that the detectors' performance degradation when an unknown located clutter edge exists in the reference window that divide the data samples into two different independent and identically distributed (IID) Compound-Gaussian distribution. To account for this issue, we propose an automatic clutter edge estimation algorithm based on goodness of fit (GoF) which can select IID data with the cell under test (CUT), and we also suggest a CFAR detector (CFARD) uses this clutter edge estimation algorithm as preprocessing to enhance the detection performance around clutter edges. Simulations are provided to demonstrate the performance of the proposed CFARD in comparison with Ordered-Statistic-CFARD (OS-CFARD).

Linearly combined order statistic (LCOS) constant false-alarm rate (CFAR) detectors are examined for efficient and robust threshold estimation applied to exponentially distributed background observations for improved detection. Two optimization philosophies have been employed to determine the weighting coefficients of the order statistics. The first method optimizes the coefficients to obtain efficient estimates of clutter referred to the censored maximum likelihood (CML) and best linear unbiased (BLU) CFAR detectors. The second optimization involves maximizing the probability of detection under Swerling II targets and is referred to as the most powerful linear (MPL) CFAR detector. The BLU-CFAR detector assumes no knowledge of the target distribution in contrast to the MPL-CFAR detector which requires partial knowledge of the target distribution. The design of these CFAR detectors and the probability of detection performance are mathematically analyzed for background observations having homogeneous and heterogeneous distributions wherein the trade-offs between robustness and detection performance are illustrated. >

Two novel constant false alarm rate (CFAR) detectors, the And-CFAR and Or-CFAR, are proposed, The two new CFAR detectors improve the conventional cell averaging CFAR (CA-CFAR) and order statistics CFAR (OS-CFAR) by making full use of the cell information. The novel CFAR processors combine the result of the CA-CFAR and OS-CFAR to get a better detection performance. In a homogeneous background, the mathematical medals of the two new CFAR detectors are derived and their performance has been evaluated and compared with that of CA-CFAR and OS-CFAR.

This paper develops a method to model the two parameter Constant False Alarm Rate (CFAR) detector as an intensity test. For optimality we show that the shape of the stencil should be matched to the radial intensity profile of the target. Using principal component analysis (PCA) we show experimentally that the first gamma kernel is a good approximation to the target profile, which explains the good results of the (gamma) -CFAR detector, and may lead to configurable stencils for better detectability.

The constant false alarm rate (CFAR) detector is a representative detection method for radar systems. Its detection performance is severely depending on clutter environments such as homogeneous and non-homogeneous. Depending on the environment, we have to adopt different CFAR algorithms. Thus, in this paper, we propose the environment detection algorithm using the Kalman filter. By using this algorithm, we can select the optimal CFAR detector for each environment.

This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a-priori information provided by a geographic information system (GIS), chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes are analysed in the presence of real radar data, collected by the McMaster IPIX radar, also in comparison with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a-priori information available about the sensed environment.

In this paper, we analyze the performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds. We employ the ordered statistics (OS) CFAR detectors as local detectors. In the homogeneous background, we maximize the global probability of detection for a given fixed global probability of false alarm 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, we analytically analyze the performance of the detection system and compare its performance with the performance of the distributed cell-averaging (CA) CFAR detection system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

ABSTRACTIn this paper, the zlog(z) based estimator for constant false alarm rate (CFAR) detection in Weibull clutter is proposed. This estimation method is obtained in terms of the digamma function where the estimates of the shape parameter are determined by the interpolation tool. The non-integer order moments estimator (NIOME) is also given and coincides the zlog(z) estimation results for low values of the moment’s fractional order. Via simulated data, it is shown that the CFAR detection performances based on the zlog(z) estimator have almost similar results as well as the existing maximum likelihood (ML) CFAR detector, but with low time-consuming which is very important in real-time applications.

In the sense of likelihood ratio test (LRT), a new type of distributed constant false alarm rate (CFAR) scheme-CCAWCA(censored cell-averaging -R-weighted cell averaging) CFAR detector is presented. Its characteristic is that censored cell-averaging (CCA) CFAR algorithms are used in local processors to form the estimation of SNR of local observations, and then the estimation transmitted to the data fusion center (DFC). Finally, the fusion center makes the final decision based on the weighted cell averaging (WCA). Since the weights are adjusted according to different SNR adaptively, the proposed detector can be available in the case that the target echo and noise/clutter have different level for every sensor. In addition, it does not need a priori knowledge about the interference in order to perform well. Furthermore, unlike the OS-CFAF the tolerance of interfering targets is restricted in appointed k value’s. Under Swerling 2 assumption, the analytic expression of detection probability and false alarm probability are derived.

In this paper, a reduced-rank multistage Wiener filter based constant false alarm rate (CFAR) detector is applied for spectrum sensing in cognitive radio. Spectrum sensing is an essential component in cognitive radio. In order to enhance the performance of primary user signal detection under the multipath fading environment, a multiple antenna technique is employed at the secondary user. In addition, the general sidelode canceller (GSC) is utilized prior to the detection to suppress the effect of interference and noise. And then the adaptive matched filter (AMF) CFAR detector is employed for the secondary user. For the purpose of reduction in computational complexity and reliable performance, reduced-rank processing is a well-known technique under the condition of finite sample support in short sensing time. Numerical results show that the reduced-rank multistage Wiener filter based AMF CFAR detector outperforms the full-rank filter.

In high-resolution synthetic aperture radar (SAR) images, the target size may be much larger than the resolution of SAR and the background clutter statistics are generally not Gaussian-distributed. But traditional constant false alarm rate (CFAR) detection algorithm, which based on the Gaussian-distributed model, performs only one test cell at a time and ignores the mutuality information with other cells in the target, so the performance of the CFAR detection algorithm can be substantially degraded. In the paper, an extend target detection method based on the generalized compound model is proposed. Unlike traditional CFAR detection method which distinguish test cell from the background only on the basis of energy contrast, the extend target detection is sensitive to both the contrast and the distribution of the target. We demonstrate the improved performance using the extend target detection method in the real high-resolution SAR images.

Constant false alarm rate (CFAR) detection techniques have been of much interest in radar and sonar applications. In this paper we consider CFAR detection in a decentralized, multisensor context and show some interesting characteristics of cell averaging (CA) in this setting. We investigate cases with observations which are dependent from sensor to sensor, for which results have been lacking. The in-phase and quadrature components of the received narrowband observation at each sensor consist of a weak random signal in additive clutter and noise. Each sensor transmits a single binary decision to a fusion center which uses either an AND or an OR fusion rule to develop a final decision. The forms of the best sensor detector rules are found and a set of necessary conditions are given for their thresholds. Solutions are obtained for some representative cases and their detection probability performance is studied. Their ability to maintain constant false alarm probability in the face of clutter edges is also studied. We show that solutions which use different fusion rules may excel for each of these different criteria.

This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a priori information provided by a geographic information system, chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes is analysed in the presence of real radar data, collected by the McMaster IPIX radar, and compared with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a priori information available about the sensed environment.

MLP-CFAR for improving coherent radar detectors robustness in variable scenarios