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Building layout reconstruction (BLR) is an important topic in the field of through-the-wall imaging. Traditionally, reflective signals are commonly used to generate an accurate building map. However, due to the inherent features of electromagnetic (EM) waves, the reconstructed walls will inevitably suffer from problems such as deviation and cavities. Alternatively, as an extension of computed tomography, the transmissive signals can be exploited for BLR with high efficiency, but its performance degrades seriously when the sampling views are sparse. In this article, to fully combine the superiority of the two types of implementations of BLR, we proposed a hybrid imaging framework by jointly exploiting the reflective and transmissive signals to retrieve the unknown layout. Specifically, first, the time delay of the transmissive signal will be estimated and used to reconstruct the spatial tomographic map. Then, a series of reflected echoes sampled by different routes will be compensated iteratively and used to generate the back-projection image. Finally, we fuse the two images generated by different types of signals using the feature-level detector. Both simulated and experimental results reveal that the proposed imaging framework can yield better performance compared with the image derived from single-type signals. [ABSTRACT FROM AUTHOR]

Shape variance of target images, image overlapping for adjacent targets, and weak scattering target detection are critical challenges of human target detection for through-the-wall radar imaging. In this letter, an adaptive target detection method is proposed based on fully convolutional network (FCN). The downsampling–upsampling structure is employed to extract multiscale features. The attention mechanism is integrated with the FCN for weak scattering target detection. Exploiting both the intensity and geometrical features of the target image, the proposed algorithm could overcome the abovementioned challenges and achieve better detection performance compared with the state-of-the-art methods. The proposed algorithm is evaluated via simulation and experimental tests. [ABSTRACT FROM AUTHOR]

Multiframe track-before-detect (MF-TBD) usually uses sliding-window-based batch processing, where a number $N$ of the latest data frames are jointly processed at each measurement time. The sliding window mechanism compromises the operating efficiency of MF-TBD by increasing both computational costs and memory requirements, thus, heavily restricting its application in practical problems. In this paper, an improved recursive implementation for MF-TBD is proposed. Unlike the sliding-window-based implementation, the proposed method calculates the merit function, a measure of the possibility that a state is target originated, of the current batch based on an approximated recursive relationship between the merit functions of consecutive batches. As a result, instead having to process the whole batch, at any given time only the latest frame needs to be processed. The recursive relationship is first derived for any arbitrary merit function, and then explored further with several typical merit functions that are used in MF-TBD. Both the theoretical analysis and simulation results demonstrate that the proposed method can achieve almost $N$ times reduction in computational complexity and memory requirements with negligible performance loss. [ABSTRACT FROM AUTHOR]

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection. [ABSTRACT FROM AUTHOR]

We address the performance prediction of the noncoherent detector (i.e., squared-law plus integrator) for independent but possibly nonidentically distributed (non-id) noncentral Gamma (NCG) fluctuating target model, which is more general and can describe more complex targets. Initially, we obtain the probability density function (pdf) of the sum of independent and non-id NCG random variables in terms of an infinite sum of Gamma pdfs. Furthermore, we develop an analytic expression of the detection probability in terms of converging series based on the generalized Marcum Q function. Finally, at the analysis stage, we first study the truncation error of the derived expression and then evaluate the impacts on the detection performance of the target fluctuating parameters via numerical simulations. [ABSTRACT FROM AUTHOR]

We address the performance prediction of the ordered-statistic constant false alarm rate (OS-CFAR) detector for arbitrarily correlated and possibly nonidentically distributed target echoes in the presence of nonhomogeneous background. Accounting for the generalized Swerling-Chi fluctuating model, including the well-known Swerling models as special cases, we develop the analytic expressions of detection probability in terms of converging series in nonhomogeneous background involving clutter edges and possibly isolated point-like interference. At the analysis stage, we study the truncation error of the derived expressions and then evaluate the impacts on detection performance of the target parameters via numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider a robust vehicle license plate detection problem for intelligent transportation systems in the presence of various illumination situations. We propose a robust and fast multi-scaled license plate detection and location algorithm, which exploits a Label-Moveable Maximal MSER clique. Specifically, first, we extract the candidate character regions using the Maximally Stable Extremal Region (MSER) features. Second, we divide each candidate character region into four types and extract the suspected initial node (the top-left character) based on its neighbor MSER distribution characteristic. Third, we label each candidate character region to accomplish license detection and location based on the detected suspected initial node and the corresponding label-moveable maximal MSER clique. The robust of license plate detection, the accuracy of character labeling for license location, and the improvement of calculation efficiency are evaluated via the real data. [ABSTRACT FROM AUTHOR]

This letter considers the problem of building layout determination using through-wall-radar imaging technology, which employs multiple transmit–receive channels to implement multiview synthetic aperture imaging. In each view, the phase errors of multichannel data introduced by the unknown walls deteriorate significantly the performance of the coherently data-combined algorithm by Le Herein, we first obtain multiple single-channel building layout images for all independent channels of each view and propose a novel noncoherent fusion method named multiply–subtract–add to combine them into a single-view layout image. Then, we present an M–N–K detector plus median filtering to fuse multiple single-view layout images and reduce the existing cavities and burrs of wall images. The experimental results reveal that the presented noncoherent image fusion method gives the single-view layout images with higher signal-to-clutter-and-noise ratio than the conventional coherent algorithm based on data combination and a near-tidy panorama layout image is generated almost without the cavities and burrs. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we consider the problem of adaptive detection for range-spread targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive (AR) process. By using the generalized likelihood ratio test (GLRT) approach, we will first estimate the AR parameters and the unknown complex amplitude, and then propose an adaptive AR-based GLR detector. The performance assessments are presented too. The computer simulations show that the proposed detector, without a priori information of the covariance matrix, has the same asymptotical performances as the two-step GLR-based detector with known covariance matrix. [ABSTRACT FROM AUTHOR]

• A robust and efficient joint sensor registration and multi-object tracking method for PHD filter to address the distributed fusion in networks where the relative positions of sensors are unknown. • The pairwise registration strategy which can transform the high-dimension optimization to low-dimension optimization for computationally efficient. • The two-step optimization method which can transform the formulated non-convex problem of the proposed method to a convex one. This paper addresses the joint sensor registration and multi-object tracking in distributed sensor networks wherein the accurate positions of sensors are unknown. Each local sensor runs the probability hypothesis density filter (PHD) in its local coordinate system, having no knowledge about the relative positions of other sensors in the network. The generalized covariance intersection (GCI) fusion rule is employed to fuse the local densities. To tackle the sensor registration problem, a cost function expressing the information discrepancy between local densities is constructed based on the GCI divergence, then the relative positions of sensors are estimated by minimizing such a cost function. As the constructed cost function leads to a high-dimension optimization problem, for computational efficiency, a pairwise sensor registration strategy is proposed which is proved equally sound with the high-dimension registration method but more computationally efficient. Lastly, we demonstrate that the formulated registration method involves a non-convex problem. To solve such a non-convex problem, a two-step optimization method is proposed which can transform the non-convex problem into a convex one. The efficacy of the proposed method is demonstrated in a challenging tracking scenario via numerical experiments. [ABSTRACT FROM AUTHOR]

This paper deals with the adaptive detection of moving targets for multiple-input multiple-output (MIMO) radar in heterogeneous clutter environments. Two new detectors based on Rao and Wald criteria are developed using an ad hoc design procedure. Precisely, we first obtain the Rao and Wald tests by assuming the known target velocity and the known structure of the clutter. Then, we modify them by performing a numerical optimization with respect to the target velocity and replacing the clutter covariance matrix with a proper estimate. With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space. Finally, numerical results are presented to demonstrate the effectiveness of the proposed detectors and covariance matrix estimators. [ABSTRACT FROM AUTHOR]