Adaptive Target Detection Research Articles

A Tunable W-ABORT-Like Detector with Improved Detection vs Rejection Capabilities Trade-Off

Adaptive radar detection of point-like targets in presence of disturbance with unknown spectral properties is addressed. By relaxing the assumptions of the W-ABORT, a tunable detector with improved detection vs rejection trade-off is proposed. To this aim, the presence of a fictitious signal under the null hypothesis is modeled probabilistically, so that an additional degree of freedom is introduced in the statistic of the detector. The resulting parametric GLRT shows a range of possible behaviors, from Kelly's detector to the plain W-ABORT. Monte Carlo simulations reveal that an improved compromise can be obtained. Simple closed-form approximations are also given with near-optimal performance, so that the ultimate computational complexity remains comparable to that of the W-ABORT.

Persymmetric adaptive target detection with distributed MIMO radar

Based on persymmetric structures in received signals, we consider the adaptive detection problem in colored Gaussian noise with unknown persymmetric covariance matrix in a multiple-input, multiple-output (MIMO) radar with spatially dispersed antennas. To this end, a set of secondary data for each transmit-receive pair is assumed to be available. A MIMO version of the persymmetric generalized likelihood ratio test (MIMO-PGLRT) detector is proposed. A closed-form expression for the probability of false alarm of this detector is derived. In addition, a MIMO version of the persymmetric sample matrix inversion (MIMO-PSMI) detector is also developed. Compared to the MIMO-PGLRT detector, MIMO-PSMI has a simpler form and is computationally more efficient. Numerical examples are provided to demonstrate that the proposed two detection algorithms can significantly alleviate the requirement of the amount of secondary data and allow for a noticeable improvement in detection performance.

Adaptive range-spread maneuvering target detection in compound-Gaussian clutter

Adaptive detection of range spread maneuvering target embedded in compound-Gaussian clutter is an important challenge for radar engineers. For the long integration, maneuvering target suffers from inevitable range walks across cells as well as unpredictable phase change at individual range cells. Therefore, the traditional adaptive normalized matched filter detectors are ineffective in long integration duration. In this paper, combining adaptive normalized matched filter with sliding high-order cross-correlation integration, a new adaptive range-spread target detector is proposed. Firstly, the long integration duration is segmented into short disjoint subintervals. In each subinterval, it is assumed that no range walking across cells happens and target's complex returns accord with the traditional rank-one parameter models. In each subinterval, the coherent integration output vector is obtained by the adaptive normalized matched filter along the slow-time dimension in different range cells. If a target is present, the coherent integration outputs of each subinterval share similar waveforms except for unknown range walks. Otherwise, when a target is absent, the coherent integration output vectors estimated from individual subintervals are uncorrelated positive random vectors without similarity. Further, the sliding high-order cross-correlation integration of these coherent integration output vectors is calculated for target detection. The new detector combines the short-time coherent integration and long-time similarity integration. The experimental results using raw radar target data and simulated clutter data show that the new detector achieves better detection performance for range-spread maneuvering targets than the existing detectors.

Modified log-ratio operator for change detection of synthetic aperture radar targets in forest concealment

Inherent speckle noise greatly limits the performance of change detection in synthetic aperture radar (SAR) images. In order to reduce the influence of such speckle noise, a modified-log-ratio (MLR) operator for change detection of targets in forest concealment is proposed. By performing a logarithm of the linear transform ratio on the pixel amplitude using multilook processing to generate a difference image, the presented MLR operator is theoretically proven to be capable of improved restraining of the negative effect of speckle noise over the conventional log-ratio operator. A Gaussian distribution of the MLR statistics is assumed, which facilitates the adaptive detection of targets using a constant false alarm rate technique. Experimental results based on the public VHF-band CARABAS-II SAR image dataset validate the effectiveness of the proposed operator.

Adaptive Detection of Point-Like Targets in the Presence of Homogeneous Clutter and Subspace Interference

In this letter, we devise an adaptive decision scheme for point-like targets capable of handling the joint presence of homogeneous clutter and structured interference in the primary and secondary data. To this end, we resort to a design procedure based on the method of sieves: the usual generalized likelihood ratio test (GLRT) is modified constraining the unknown parameters to belong to a suitable subset of the original space ensuring unique solutions for the involved optimizations. Remarkably, the proposed receiver possesses the constant false alarm rate (CFAR) property with respect to the unknown covariance matrix of the unstructured interference. At the analysis stage, closed-form expressions for the false alarm and detection probabilities are derived.

Persymmetric detectors with enhanced rejection capabilities

In this study, the authors deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by the space and/or time symmetry of the sensing system. In this framework, they devise and assess two selective receivers exploiting the Rao test and the generalised likelihood ratio test design criteria. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receivers can significantly outperform their unstructured counterparts and guarantee enhanced rejection performance of unwanted signals with respect to their natural competitors.

Impact of Signal Contamination on the Adaptive Detection Performance of Local Hyperspectral Anomalies

The effects of signal contamination of secondary data are investigated in the framework of adaptive target detection in remotely sensed hyperspectral images. In contrast to previous studies on signal contamination, the focus of this paper is the detection of targets with unknown spectral signatures (i.e., anomalies) and adaptive detection methods based on a local estimation of the background covariance matrix. Contamination due to the target signal is expected to have a more severe impact when the number of secondary data is limited. An analytical model for signal contamination is developed that allows variability in the extent of contamination. Several parameters, such as the contamination fraction of secondary data and the contaminating signal energy, are introduced, and a contaminating signal-to-interference-plus-noise ratio is derived as an objective measure of contamination. The proposed model is employed to experimentally evaluate signal contamination effects and the impact of its variability on the performance of adaptive detection of local anomalies. The outcomes of the experimental study are substantiated by validation with real hyperspectral data. The results obtained highlight the relevance that the impact of signal contamination, assessed with respect to different system parameters, may have for practical applications. This paper represents a starting point for the development of detection performance forecasting models that consider signal contamination.

Adaptive Polarimetric Detection of Targets in Heavy-tailed Compound-Gaussian Clutter

摘要: 该文研究极化高分辨雷达在动态变化的杂波场景中自适应检测小目标的问题。将统计特性严重拖尾的杂波建模为纹理分量为逆伽马分布的复合高斯过程，借助于广义似然比检验和辅助数据得到了自适应极化检测器，并推导了该检测器的虚警概率表达式，证明了该检测器对协方差矩阵结构具有恒虚警特性。最后，利用仿真杂波数据验证了检测器检测性能的有效性。 关键词: 目标检测 / 极化 / 复合高斯 / 逆伽马分布

방향 탐지용 전파형 대공 근접 신관센서

This paper presents the direction finding proximity fuze sensor using the clutter rejection method and the adaptive target detection algorithm for anti-air missiles. To remove effects by clutter and detect a target accurately, the clutter rejection method of Legendre sequence with BPSK(Bi phase Shift Keying) modulation has been proposed and the Doppler signal which has cross correlation characteristics is obtained from reflected target signals. Considering the change of the Doppler signal, the adaptive target detection algorithm has been developed and the direction finding algorithm has been fulfilled by comparing received powers from adjacent three receiving antennas. The encounter simulation test apparatus was made to collect and analyze reflected signal and test results showed that the -10 dBsm target was detected over 10 meters and the target with mesh clutter was detected and direction was distinguished definitely.Keywords : BPSK, Cross-Correlation(교차상관), Range Gate(거리 창), Clutter Rejection(클러터 제거), Direction Finding Fuze(방향구분 신관)

Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

We design and assess an adaptive scheme to detect a subpixel target in a sequence of images in the presence of an additive correlated Gaussian background. The presence of the subpixel target decreases the background power that hence may be different under the null and alternative hypotheses. We use the generalized likelihood ratio test (GLRT) to adapt the recently proposed modified matched subspace detector (MMSD) to unknown background variances under the null and alternative hypotheses using the secondary and primary data, respectively. We derive a modified adaptive subspace detector (MASD) that is sensitive to both energy in the target subspace and reduced energy in the orthogonal subspace. We contrast it with the MMSD and the well-known adaptive cosine estimator (ACE). Numerical simulations attest to the validity of the theoretical analysis and show that the proposed detector performance outperforms the ACE, especially in the case of dark subpixel targets. The performance-degrading effects of limited secondary data are presented for the proposed detector.

Adaptive Bayesian Detection Using MIMO Radar in Spatially Heterogeneous Clutter

This letter considers adaptive target detection problem using multiple-input multiple-output (MIMO) radar in the presence of spatially heterogeneous clutter. The covariances of the primary data and secondary data for the same and different transmit-receive pairs are modeled as different random matrices with partial priori knowledge of the environment. Two-step strategy is employed to design adaptive detector. Specifically, we first obtain the generalized likelihood ratio test (GLRT) detector by assuming the known matrices. Then, we derive the maximum posteriori (MAP) estimator of the covariance matrices by exploiting the priori information, and replace the given covariance matrices in the obtained GLRT with MAP estimates. Finally, we evaluate the proposed adaptive detector via numerical simulations.

Adaptive detection of range-spread targets without secondary data in multichannel autoregressive process

Adaptive detection of range-spread targets without secondary data is addressed in a multichannel autoregressive Gaussian disturbance with unknown space–time covariance matrix, by utilizing the Rao test. The proposed Rao test without secondary data is theoretically proved to be asymptotically (large-sample in the number of temporal observations) constant false alarm rate with respect to unknown space–time covariance matrix, thanks to an asymptotic equivalence between the Rao test and the generalized likelihood ratio test. Moreover, the performance loss due to no secondary data can be remedied by appropriately increasing the temporal dimension. The performance assessment conducted by Monte Carlo simulation, also in comparison with the existing detector without secondary data, confirms the effectiveness of the proposed detectors.

A New Target Detector for Hyperspectral Data Using Cointegration Theory

This paper introduces Cointegration Theory to address the problem of adaptive target detection in hyperspectral imagery. Cointegration Theory aims at mining a long-term equilibrium relationship, which refers to the condition that an appropriate linear combination of several non-stationary series can be stationary as long as they have similar or related drift. Hyperspectral response sequences, which are highly non-stationary, have similar patterns among the same materials. To be treated as a time series, each given hyperspectral curve is matched with the reference spectrum via the Johansen Cointegration Test. The statistic of the test is then used for target detection. Experimental results indicate that our proposed method is effective and has a strong capacity to identify interesting objects from their background.

An Adaptive Target Detection Method in High-Resolution SAR Images

1 Yang Li, 2 Jianjiang Lu, 3 Jiabao Wang, Lei Bao, Wei Li 1, Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China, E-mail: solarleeon@163.com *2, Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China, E-mail: miipl606@163.com 3,4,5, Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China, E-mail: miipl606@163.com

Adaptive Detection of Wideband Radar Range Spread Targets with Range Walking in Clutter

In this paper, we address detection of range spread targets with range walking in partially homogeneous clutter, which is often encountered in wideband radar. By considering the target range walking effect, a target returns model is proposed that represents the relationship between Doppler frequency and range frequency. Based on this, a generalized matched subspace detector (GMSD) and a general adaptive subspace detector (GASD) are proposed in the frequency domain. Moreover, to eliminate excess integration loss introduced by the mismatched detection window size, two improved detectors are proposed in range domain. The theoretical analysis shows that the GMSDs are of a constant false alarm rate (CFAR) with respect to both the power and the covariance matrix of the clutter but that the GASDs are of a CFAR with respect to the clutter power and an approximate CFAR with respect to the clutter covariance matrix. Simulated experiments are carried out to verify the effectiveness of the proposed detectors.

Generalised parametric Rao test for multi-channel adaptive detection of range-spread targets

This study considers the problem of detecting a multi-channel signal of range-spread targets in a homogeneous environment, where the disturbances in both test signal and training signals share the same covariance matrix. To this end, a generalised parametric Rao (GP-Rao) test is developed by modelling the disturbance as a multi-channel auto-regressive process. The GP-Rao test uses less training data and is computationally more efficient, when compared with conventional covariance matrix-based solutions. The theoretical detection performance of the GP-Rao test is characterised in terms of the asymptotic distribution under both hypotheses. Numerical results indicate that the proposed GP-Rao test attains asymptotically the constant false alarm rate property. Numerical results show that the GP-Rao test achieves better detection performance and uses significantly less training signals than the covariance matrix-based approach.

Cascaded Detector for Range-Spread Target in Non-Gaussian Clutter

The work presented here addresses adaptive range-spread target detection in spherically invariant random vector (SIRV) clutter based on two-step generalized likelihood ratio test (GLRT) design procedure. Firstly, with known normalized clutter covariance matrix (NCCM), a robust detector with constant false alarm rate (CFAR) property is proposed, which cascades a detector based on the GLRT for target scatterer in each range cell and two binary integrators. Subsequently, the optimum parameters for the cascaded detector are obtained. Furthermore, by replacing the ideal NCCM with the approximate maximum likelihood (ML) estimated matrix based on secondary data only, the corresponding adaptive detector with approximate CFAR property is also devised. Moreover, the formulas relating the probability of false alarm to the detection threshold are deduced. The performance assessment conducted by Monte Carlo simulation confirms the effectiveness and robustness of the proposed detectors.

Adaptive target detection with a polarization-sensitive optical system

We developed an adaptive polarimetric target detector (APTD) to determine the optimum combination strategy for a multichannel polarization-sensitive optical system. The proposed algorithm is based on scene-derived polarization properties of the target and background, and it seeks to find an optimum multichannel combination of linear polarizing filters that maximizes the signal-to-clutter ratio (SCR) in intensity and Stokes parameter images. The algorithm is validated by performing RX anomaly detection and a generalized likelihood ratio test on both synthetic and real imagery. The experimental results are analyzed through calculated SCR and receiver operating characteristic curves. Compared with several conventional operation methods, we find that better target detection performance is achieved with the APTD algorithm.

M-correlated sweeps performance analysis of adaptive detection of radar targets in interference-saturated environments

Our scope in this paper is to analyze the performance of a radar signal processor that consists of a nonrecursive MTI followed by a square-law integrator and a CFAR circuit detection, the operation of which is based on the double-threshold procedure. The processor performance is evaluated for the case where the background environment is assumed to be ideal (homogeneous) as well as in situation where it contains a number of interferers along with the target under test (multitarget environment). For some specified values for the first threshold, it is shown that the processor detectability loss is very low, and the performance degradation, caused by spurious targets is quite small even if their number is large. Additionally, the processor performance improves as either the number of incoherently integrated pulses increases or the correlation between consecutive sweeps decreases, as we predict.

A NEW ADAPTIVE LINEAR COMBINED CFAR DETECTOR IN PRESENCE OF INTERFERING TARGETS

In this paper, a new radar constant false alarm rate detector to perform adaptive threshold target detection in presence of interfering targets is proposed. The proposed CFAR detector, referred to as Adaptive Linear Combined CFAR, ALC-CFAR, employs an adaptive composite approach based on the well-known cell averaging CFAR, CA-CFAR, and the ordered statistics, OS-CFAR, detectors. Data in the reference window is used to compute an adaptive weighting factor employed in the fusion scheme. Based on this factor, the ALC- CFAR tailors the background estimation algorithm. The conducted Monte Carlo simulation results demonstrate that the proposed detector provides low loss CFAR performance in an homogeneous environment and also performs robustly in presence of interfering targets. The performances of the ALC-CFAR detector have been evaluated and compared with that of the CA-CFAR and the OS-CFAR detectors. The obtained results are presented and discussed in this paper.