Target detection in clutter background: Null or whiten the clutter

Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR). The traditional fractional domain transform-based detection method requires parameters match searching, which costs heavy computational burden in case of a large amount of data. Sparse FT and sparse fractional FT can obtain high-resolution sparse representation of the target, but the signal sparsity needs to be known before, and the sparse representation performance is poor in clutter background. In this article, adaptive filtering method is introduced into the sparse fractional ambiguity function (SFRAF) method, and a SFRAF domain adaptive clutter suppression and highly maneuvering target detection algorithm is proposed, which is named as adaptive SFRAF (ASFRAF). The ASFRAF domain iterative filtering operation can suppress the clutter while retaining the signal energy as much as possible. Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.

Low observable moving target-detection technology under clutter background is the key factor affecting the radar performance. Here, frequency diverse array (FDA) MIMO radar is investigated for low-observable moving target detection. FDA-MIMO radar not only has all the advantages of phased array and MIMO radar, but also can make a two-dimensional joint estimation of the target distance and azimuth. In order to achieve coherent integration for Doppler processing, a novel three-dimensional processing method is proposed, i.e. space-range-Doppler focus (SRDF) processing. It utilises the property of FDA and high-resolution Doppler processing of MIMO. After discussing signal model of FDA-MIMO radar, the flowchart of SRDF-based low-observable moving target detection and estimation is provided. Finally, simulation of moving target detection in clutter background verifies that proposed method has better ability for joint angle-range-Doppler processing with higher resolutions.

Joint transform correlator is one of the most successful optical pattern recognizers which is composed of Ar⁺ laser, spatial filter, Fourier transform lenses, EALCD (electrically addressed liquid crystal displays) and CCD (Charge Coupled Device). It has many applications in industry and military. The bottle technologies are how to recognize the target in clutter scene and how to increase the brightness of the correlation peak which represents the detected target. In this paper, we researched joint transform correlator (JTC) for infrared target detection, and used wavelet transform technology to increase the contrast of input image which contains target and reference objects, reduced the high frequency noise and realized target detection in clutter scene. According to our requirement, we selected the two-order spline wavelet, and made input image have discrete binary wavelet transformed. Because edge always contains much information of image, we adopted the method of modular maximum to extract the edge of different scales after wavelet transform mentioned above. In addition, combined with threshold division which could eliminate some of the clutter, we could get the remarkable edge. As experiment result, we detected the infrared image, both with computer correlation matched method and optical JTC method. The experiments show that the wavelet transform technology is one of the best data processing methods for the target detection in clutter background.

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 our previous papers, the FPGA-based processing package and the co-processor board have been introduced for numerous commercial and military applications including motion detection, optical flow, background velocimetry, and target tracking. The processing package is being continually upgraded by new point- and area-applied algorithms for a variety of real-time digital video camera systems including foveal sensors based on Nova's <b>V</b>ariable <b>A</b>cuity <b>S</b>uperpixel <b>I</b>mager (VASI^TM) and <b>L</b>arge <b>F</b>ormat VASI^TM (LVASI^TM) technologies. This paper demonstrates the FPGA-based processor for high frame-rate target detection in a cluttered background using variable acuity sensors. For the 1024 x 1024 pixel LVASI^TM Focal Plane Array (FPA), the proposed target-detection algorithm increases the frame rate from 4 Hz for the full resolution mode up to 450 Hz for the foveal mode while maintaining full field of view and target-detection performances on cluttered backgrounds that are comparable with detection performances at the full resolution mode.

The paper proposes a method to improve the performance of target detection on the background clutter using the standard deviation of polarization entropy H parameter. The performance of target detection is analyzed with different types of target, on the background clutter models Rayleigh, Weibull and Laplace. Also examined the special case when the target has the same H parameter as the background clutter. The results showed that the performance of the method using the standard deviation of H in the target detection on the background clutter has increased significantly compared to the case where only the H parameter is used.

This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance. When the clutter return follows a stationary Gaussian distribution with slowly varying power spectral density, the optimal selection is derived. When the clutter is composed of reflected versions of the transmitted waveforms, a greedy-based method for selecting the frequency snapshots is presented. Numerical experiments show that a receiver with reduced DOF can lead to detection performance which is very close to that of the receiver with full DOF.

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both adaptive detectors are assessed and the influence of mismatched steering vector on detection probability is analyzed. The experimental results show that the adaptive detectors have an acceptable loss with respect to the nonadaptive counterparts. Furthermore, the ANSDD-GLRT converges much faster than the ASDD-GLRT, however, the latter rejects the misaligned signals more effectively. Both detectors perform well for the uniformly-distributed-energy target, but degenerate for the fluctuating targets. In addition, the correlation between target scatterers results in an additional gain for low signal-to-clutter power ratio.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, ${\bf R}$ , is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, ${\bf R}_{0}$ , is assumed to be equal to $\lambda{\bf R}$ , i.e., ${\bf R}_{0}=\lambda{\bf R}$ . On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor $\lambda$ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

Drone-based target detection is an indispensable technology in numerous applications, such as surveillance, search, and rescue. The widely used single-modal RGB detector is easily affected by numerous factors, such as inadequate illumination and adverse weather. Introducing the thermal modal on top of the RGB modal can significantly enhance detection performance and robustness in complex environments. Nevertheless, drone-based RGBT target detection faces several challenges, such as the difficulty of detecting tiny targets, misaligned modality space, and redundant modality information. To address these issues, we propose a novel Adaptive Modality Selection Drone-based RGBT Detector (AMSDet), which eliminates redundant information in different modalities and enables accurate detection of tiny targets. Specifically, we first design a policy module to filter modalities before the fusion module and select the relevant modality information for input into the subsequent network. Second, we introduce a fusion module based on the attention mechanism to integrate the complementary information of the two modalities and improve the detection performance of tiny targets. Furthermore, we employ an appropriate training strategy and loss functions to jointly train the policy module and other layers in AMSDet. The superiority of our proposed method is validated through extensive experiments on the RGBTDronePerson and VTUAV-det datasets, achieving excellent detection performance for drone-based tiny targets.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

Adaptive beamformers often rely on estimates of the interference covariance structure. When minimal data is used in estimating the unknown covariance matrix, performance may be improved by modifying the eigenvalues of the sample covariance matrix. The orthogonally invariant covariance estimators proposed by Dey and Srinivasan [1985] and a constant risk, minimax estimator are applied to adaptive beamforming by considering the signal-to-interference ratio (SIR) loss factor of Reed, Mallett, and Brennan [1974] where the average SIR loss is seen to decrease, and, to adaptive detection, by considering the adaptive detectors of Kelly [1986] and Robey et. al. [1992] where detection performance is seen to improve. >

This paper studies adaptive radar detection of range distributed targets in the presence of Gaussian interference and possible diffuse multipath returns modeled as independent zero-mean complex circular Gaussian random vectors with unknown covariance matrices. For this problem, an adaptive constrained generalized likelihood ratio (ACGLR) test is devised, where in each range cell of the primary data the covariance matrix (due to both multipath and disturbance echoes) is forced to belong to a neighborhood of the secondary data sample covariance. The size of the uncertainty set is determined adaptively employing jointly a union-intersection test and an expectation likelihood (EL)-based estimate. Besides, an adaptive detector based on the complex parameter Rao test criterion is derived. Remarkably, both the two new architectures possess the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, their detection performance is assessed and validated via numerical examples.

In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets bat also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model aided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.

Many applications require precise estimates of high-dimensional covariance matrices. The standard estimator is the sample covariance matrix, which is conceptually simple, fast to compute and has favorable properties in the limit of infinitely many observations. The picture changes when the dimensionality is of the same order as the number of observations. In such cases, the eigenvalues of the sample covariance matrix are highly biased, the condition number becomes large and the inversion of the matrix gets numerically unstable.&#13;\nA number of alternative estimators are superior in the high-dimensional setting, which include as subcategories structured estimators, regularized estimators and spectrum correction methods. In this thesis I contribute to all three areas. &#13;\nIn the area of structured estimation, I focus on models with low intrinsic dimensionality. I analyze the bias in Factor Analysis, the state-of-the-art factor model and propose Directional Variance Adjustment (DVA) Factor Analysis, which reduces bias and yields improved estimates of the covariance matrix.&#13;\nAnalytical shrinkage of Ledoit and Wolf (LW-Shrinkage) is the most popular regularized estimator. I contribute in three aspects: first, I provide a theoretical analysis of the behavior of LW-Shrinkage in the presence of pronounced eigendirections, a case of great practical relevance. I show that LW-Shrinkage does not perform well in this setting and propose aoc-Shrinkage which yields significant improvements. Second, I discuss the effect of autocorrelation on LW-Shrinkage and review the Sancetta-Estimator, an extension of LW-Shrinkage to autocorrelated data. I show that the Sancetta-Estimator is biased and propose a theoretically and empirically superior estimator with reduced bias. Third, I propose an extension of shrinkage to multiple shrinkage targets. Multi-Target Shrinkage is not restricted to covariance estimation and allows for many interesting applications which go beyond regularization, including transfer learning. I provide a detailed theoretical and empirical analysis.&#13;\nSpectrum correction approaches the problem of covariance estimation by improving the estimates of the eigenvalues of the sample covariance matrix. I discuss the state-of-the-art approach, Nonlinear Shrinkage, and propose a cross-validation based covariance (CVC) estimator which yields competitive performance at increased numerical stability and greatly reduced complexity and computational cost. On all data sets considered, CVC is on par or superior in comparison to the regularized and structured estimators.&#13;\nIn the last chapter, I conclude with a discussion of the advantages and disadvantages of all covariance estimators presented in this thesis and give situation-specific recommendations. In addition, the appendix contains a systematic analysis of Linear Discriminant Analysis as a model application, which sheds light on the interdependency between the generative model of the data and various covariance estimators.