Order Statistics Constant False Alarm Research Articles

VIHCEMOS-CFAR Detector Based on Improved VI-CFAR

A variability index heterogeneous clutter estimate modified ordered statistics constant false alarm rate (VIHCEMOS-CFAR) detector based on improved VI-CFAR is proposed. First calculate the VI and MR values and then judge the clutter background: If the background is uniform, cell averaging (CA) target detection strategy is adopted; HCE detection strategy is adopted if it is clutter edge background; If it is a complex background composed of multiple targets or strong clutter, the OS detection strategy is adopted. The method of judging the interfering targets and strong clutter in the reference window by using the phase characteristics is used to calculate the accurate k. The Modified-OS algorithm with corrected k is proposed as the detection strategy in the multi-target environment to improve the target detection performance in practical projects. Through detailed experiments, it is proved that VIHCEMOS has adaptive clutter background capability and better detection performance than all other CFAR detection algorithms in multi-target environment.

A Multi-Target Detection Method Based on Improved U-Net for UWB MIMO Through-Wall Radar

Ultra-wideband (UWB) multiple-input multiple-output (MIMO) through-wall radar is widely used in through-wall human target detection for its good penetration characteristics and resolution. However, in actual detection scenarios, weak target masking and adjacent target unresolving will occur in through-wall imaging due to factors such as resolution limitations and differences in human reflectance, which will reduce the probability of target detection. An improved U-Net model is proposed in this paper to improve the detection probability of through-wall targets. In the proposed detection method, a ResNet module and a squeeze-and-excitation (SE) module are integrated in the traditional U-Net model. The ResNet module can reduce the difficulty of feature learning and improve the accuracy of detection. The SE module allows the network to perform feature recalibration and learn to use global information to emphasize useful features selectively and suppress less useful features. The effectiveness of the proposed method is verified via simulations and experiments. Compared with the order statistics constant false alarm rate (OS-CFAR), the fully convolutional networks (FCN) and the traditional U-Net, the proposed method can detect through-wall weak targets and adjacent unresolving targets effectively. The detection precision of the through-wall target is improved, and the missed detection rate is minimized.

Multi-Target CFAR Detection Method for HF Over-The-Horizon Radar Based on Target Sparse Constraint in Weibull Clutter Background

High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the range–Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows that the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the ordered statistics constant false alarm detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameters for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of the k value. Upon estimating the distribution parameters, the detection threshold is calculated, and the target’s constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method.

Robust CFAR Detector Based on KLQ Estimator for Multiple-Target Scenario

The order statistic (OS) constant false alarm rate (CFAR) detector is designed from a perspective of quantile estimation and uses the sample quantile (SQ) estimator to estimate background level. Following this idea, we find a more efficient quantile estimator, i.e. Kaigh-Lachenbruch quantile (KLQ) estimator, to construct a robust CFAR detector, which is referred to as KLQ-CFAR. We prove that the KLQ estimator is an asymptotic unbiased estimator to the quantile function and derive the asymptotic variance of KLQ estimator in Exponential case. It is shown that the KLQ estimator has more efficient estimation efficiency than SQ estimator. Subsequently, we explore the performance of KLQ-CFAR in Exponential clutter. In homogeneous background, the KLQ-CFAR conquers the shortcoming of large detection loss of OS-CFAR, and exhibits comparable performance with trimmed mean (TM) CFAR. The superiority of KLQ-CFAR is also reflected in multiple-target scenario, especially in the case where the number of interferences exceeds the preset parameters of KLQ- and TM-CFAR. In this case, the detection performance and false alarm control ability of KLQ-CFAR have significant improvement compared with TM-CFAR. Then, we extend the KLQ-CFAR from Exponential background to non-Gaussian clutter via transformation approach, and obtain the transformed KLQ (TKLQ) CFAR. We take Weibull clutter as a specific example. It is found that the behavior of TKLQ-CFAR in Weibull background is very similar to that of the KLQ-CFAR in Exponential clutter, but the difference lies in that the performance of TKLQ-CFAR is related to Weibull shape parameter. The TKLQ-CFAR tends to have better detection performance with the increase of Weibull shape parameter. Finally, the validity of TKLQ-CFAR is verified in skywave over-the-horizon radar data.

Forward-looking ground penetrating radar image reconstruction using fully connected neural network

One way to identify targets buried in the ground is to use forward-looking ground penetration radars. The two components of speed and accuracy are very important in reconstructing the images obtained from this method. Distinguishing the target from the Earth clutter is one of the main challenges of this type of imaging. This article proposes a novel fast algorithm that reconstructs scattered images created by forward-looking ground-penetrating radar. The linear back-projection algorithm is used for fast image reconstruction. Because our problem is nonlinear ill-posed inverse, errors and distortions are compensated by a deep fully connected neural network. The difference between received and reconstructed signal is the input of the neural network, and output is the error between reconstructed and actual coefficients of the Interested region. Our assumption is the region of interest has a few electromagnetic scatterers. Because of types of soils and non-stationary situations, it is difficult to estimate the probability density function of noise plus clutter. Therefore, we modified order statistic constant false alarm rate to detect shallowly buried scatterers. The simulation results show that the algorithm improves signal-to-noise and clutter ratio in output and detects objects with a constant probability of false alarm in a fast way.

A Two-stage Detector for First-order Sea Clutter in HF Surface Wave Radar

The detection capabilities in vertical polarized, High-frequency SurfaceWave Radar (HFSWR) are limited by the clutter spikes in first-order sea clutter. The energy, position, and Doppler frequency of first-order sea clutter depend on the sea states, which are non-homogeneous – because of that, using a fixed expression to eliminate sea clutter gives limited results. In this paper, the authors proposed a new adaptive method using a two-stage detector based on the Order Statistic Constant False Alarm Rate (OS-CFAR) principle. The approach shows the capability to eliminate heterogeneous clutter and detect targets in the vicinities of sea clutter in the Range – Doppler map.

Range Detection on Time-Domain FMCW Radar Signals With a Deep Neural Network

This letter presents a novel system to perform range detections using an artificial neural network on the time-domain baseband signal of frequency-modulated continuous wave radar sensors. The network is trained and evaluated with synthetic signals, which are generated with a point target simulator. To evaluate the performance of the proposed approach, it is compared with an order statistics constant false alarm rate (CFAR) detector at different signal-to-noise ratios. The detection system is shown to work-in some cases even outperforming the baseline - in synthetic single-target, as well as in multiple-target scenarios. Therefore, it is capable of replacing the usual fast Fourier transform and CFAR detection procedures in radar signal processing. Furthermore, it is demonstrated that the detection system also works with real radar measurement data.

Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination

A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.

Repeated sorting on the sliding window for OS‐CFAR

An algorithm is suggested for repeated sorting of a sliding window of n consecutive reference cells, required in order statistics-constant false alarm rate radar detection. The sliding window surrounds a central cell under test with guard cells on its sides, which are excluded from the sorted reference cells. The algorithm's computational complexity is smaller by a factor of n/4 compared with performing a new independent sorting after each slide. That factor does not depend on the number of guard cells. The advantage of the new algorithm was confirmed by theoretical and numerical comparison with respect to two other recently reported approaches.

Building extraction from very high-resolution synthetic aperture radar images based on statistical and structural information fusion

ABSTRACT To investigate the limits of building detection from very high-resolution (VHR) synthetic aperture radar (SAR) images, a new method, based on statistical and structural information fusion, is proposed in this paper. The proposed method contains two stages: First, using order statistics constant false alarm rate (OS-CFAR) and power ratio (PR) detectors, a set of detections are made. These detections have different statistical properties, compared to the other objects, and these properties are selected for discriminating buildings from clutters. Second, the morphological analysis is used for increasing the precision of the detection. In this stage, segments, which have the most similarities to buildings in terms of shape and size, are extracted via various structural elements (SEs). The final result is obtained by fusing the two sets of detections. The experimental results on the four real VHR SAR images show that the proposed method has a high detection rate (DR) and low false alarm rate (FAR).

Burst Suppression Segmentation of EEG Using Adaptive Binarization in Time and Frequency Domains

This paper proposes a method for burst suppression segmentation using adaptive EEG binarization in both time and frequency domains. Since the dynamic range of EEG amplitude is very wide and can vary from one recording to another owing to the physical conditions of the subjects and measuring factors, such as electrode types, electrode locations, impedance, and amplifiers, conventional quantitative EEG (qEEG) features for segmentation sensitively vary in proportion to the EEG amplitude and result in erroneous segmentation. EEG binarization was applied to solve the problem of a wide dynamic range. Through the restriction of all different dynamic values of EEG signals to 0 and 1, subsequent signal processing techniques are independent of the dynamic range. Additionally, binarization may reduce the computational time of processing data. We employed the ordered statistic constant false alarm rate (OS-CFAR) algorithm for adaptive binarization. For burst suppression segmentation, maximum likelihood estimation was conducted using Gaussian models for feature values of burst and suppression segments. To investigate the segmentation performance of the proposed method, we evaluated the accordance to visual segmentation and quantified the error of burst suppression ratio estimation. Generally, the proposed binarization method was shown to be beneficial for both increased segmentation accordance and decreased error of estimated burst suppression ratio.

Building Detection in Very High Resolution SAR Images Via Sparse Representation Over Learned Dictionaries

The spatial resolution of synthetic aperture radar (SAR) images has significantly increased in the past few years. As a result, very high resolution images are available for interpretation of the urban area. Buildings are one of the most important parts of the urban area. In this paper, we propose a novel method for building detection from SAR images. First, a set of primary detections, using double bounce, layover, and shadow features, are made. These features are extracted using the order statistics constant false alarm rate method and power ratio detector. The detectors parameters are adjusted in such a way that all bright and dark areas are well extracted. Second, the K-singular value decomposition method is used to learn two dictionaries for discrimination of buildings from clutters. The main idea is to utilize recognition techniques in detection algorithms in order to reduce the number of the false alarms. Gray level statistical, textural, and shape features are used to form the dictionaries. In this step, a secondary set of detections are available, and the final detections are made by the fusion of these two sets. The effectiveness of the algorithm is evaluated by two different TerraSAR-X images from a densely built-up area. The results show that the proposed method is an effective way to detect building through single SAR images.

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.

Range–Doppler‐based centralised framework for human target tracking in multistatic radar

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two-dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root-mean-square error pruning algorithm is proposed for multi-target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage-penetration environment.

Particle Swarm Optimization Aided Serial Acquisition in Distributed OSCFAR and CMLD for DS-CDMA Systems in Fading Channels

In this paper, adaptive serial acquisition schemes are proposed using threshold optimization in Multisensor data fusion for mobile communication systems in frequency selective Rayleigh fading channel. Two distributed adaptive detectors based on ordered statistics constant false alarm rate (OSCFAR) and censored mean level detector (CMLD) are used. For threshold optimization, the Particle Swarm Optimization (PSO) with linearly decreasing strategy is suggested, employing two logical fusion rules to enhance the serial-search in a multipath environment. The acquisition and detection performances are investigated under different system parameters and channel condition, namely the number of distributed sensors, the partial correlation length, the number of resolvable path and the effect of fading. Simulation results shown the robustness of the proposed distributed adaptive acquisition CMLD processor using PSO over the one based on OSCFAR. Moreover, the benefit of proposed schemes over adaptive acquisition ones using one antenna is also investigated by simulation.

ADOS-CFAR Algorithm for Multibeam Seafloor Terrain Detection

To solve the false terrain problem caused by the tunnel effect in the conventional multibeam seafloor terrain detection, a detection method of multibeam seafloor terrain based on the ADOS-CFAR (Amplitude Dichotomy Order-Statistics Constant False Alarm Rate) is developed. This paper first discusses the principles and existing problems of the OS-CFAR and K-FINDER algorithms and then suggests the ADOS-CFAR algorithm and its fast calculation method for those problems. By adopting this algorithm into the detection method of multibeam seafloor terrain and carrying out the simulation study on the computational load of the ADOS-CFAR, it is shown that this method has a lower computational load on the premise of ensuring the detection performance. Finally, the proposed algorithm is analyzed and verified through the test data in Songhua Lake. The result indicates that this method can effectively avoid the false terrain caused by tunnel effect with higher terrain detection accuracy.

Detection of fish near the bottom with a hull-mounted multibeam echosounder

In single-beam or split-beam fisheries echosounding, bottom echoes often mask echoes from fish hovering near the bottom. Likewise, in down-looking multibeam echosounding bottom echoes received in the main lobe of near normal incidence beams appear in the sidelobes of the other beams and obscure weaker echoes received at the same range in the mainlobe of these other beams. Some multibeam sonars use frequency division multiplexing to avoid crosstalk between beams. However, it was shown recently [de Moustier, Proc. MTS/IEEE OCEANS'13, San Diego, September 23–27, 2013] that detection of fish near the seafloor is possible with a multibeam echo-sounder operating at a single acoustic frequency. This is achieved with a signal processing technique based on the ordered statistic constant false alarm rate (OS-CFAR) detection method used in radar. Here, the OS-CFAR operator is applied in the angle domain, rather than the customary range domain, and it estimates the signal-to-clutter ratio across all angles at a given time slice or range increment at the output of the beamformer. In this context, clutter encompasses both clutter and noise against which the desired signal is to be detected. Data collected with a 160 kHz multibeam echosounder are used to demonstrate the technique.

AUTOMATIC THRESHOLD SELECTION IN OS-CFAR RADAR DETECTION USING INFORMATION THEORETIC CRITERIA

This paper proposes a new approach for e-ciently determining the unwanted interfering samples in the reference window, for the ordered statistics constant false alarm rate detector, based on the application of the information theoretic criteria principle. The proposed processor termed as Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) does not require any prior information about the number of interfering targets. The proposed design aims to improve the Ordered Statistics Constant False Alarm Rate detector performance under severe interference situations. The number of interfering targets is obtained by minimizing the information theoretic criteria. Simulation results that illustrate the performance of the proposed method versus the classical OS-CFAR, the AND-CFAR and the OR-CFAR detectors are presented and discussed.

Distributed Fuzzy CFAR Detection for Weibull Clutter

In Distributed detection systems, restricting the output of the local decision to one bit certainly implies a substantial information loss. In this paper, we consider the fuzzy detection, which uses a function called membership function for mapping the observation space of each local detector to a value between 0 and 1, indicating the degree of assurance about presence or absence of a signal. In this case, we examine the problem of distributed Maximum Likelihood (ML) and Order Statistic (OS) constant false alarm rate (CFAR) detections using fuzzy fusion rules such as “Algebraic Product” (AP), “Algebraic Sum” (AS), “Union” (Un) and “Intersection” (IS) in the fusion centre. For the Weibull clutter, the expression of the membership function based on the ML or OS CFAR processors in the local detectors is also obtained. For comparison, we consider a binary distributed detector, which uses the Maximum Likelihood and Algebraic Product (MLAP) or Order Statistic and Algebraic Product (OSAP) CFAR processors as the local detectors. In homogenous and non homogenous situations, multiple targets or clutter edge, the performances of the fuzzy and binary distributed detectors are analyzed and compared. The simulation results indicate the superior and robust performance of the distributed systems using fuzzy detection in the homogenous and non homogenous situations.

OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies

This paper proposes an improvement of the threshold optimization in distributed ordered statistics constant false alarm rate and censored mean level detector using Evolutionary Strategies (ESs). The target is assumed to be Rayleigh distributed and the observations are independent from sensor to sensor. Two fusion rules; “AND” and “OR” were considered. An ES was tested and a comparison with a genetic algorithm improved by a tournament selection was also analyzed. Among a variety of evolution strategies, the most popular proposed in the literature are the strategy (μ, λ) and the strategy (μ + λ). We proposed an (μ + λ) evolution strategy, by which a self-adaptation mutation is used. The results showed that, although the ES is more difficult to implement and is in a certain manner slower than the GA, it improves the performance of the system.