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For the reason of target multipaths ambiguities, localization for hidden moving target due to lack of line of sight (LOS) path in urban environments is a hard task. To handle this problem we propose a localization algorithm based on the internal connection of these multipaths in this paper. First, we establish an electromagnetic (EM) propagation model based on the characters of the measured signal received by an L-band single-input single-output (SISO) ultra-wideband (UWB) radar. After that, the model-based target localization algorithm is proposed based on a times-of-arrival (TOAs) association method, which is able to match the measured TOAs with the corresponding EM propagation paths correctly. The proposed method can cope with the case while some missing detections of TOAs happen. Simulation and experimental results ultimately validate the robustness and the state-of-the-art performance of the proposed algorithm.

Multipath exploitation with compressive sensing (CS) has been successfully applied in through-the-wall radar imaging (TWRI) given prior knowledge of the room geometry. However, in most practical applications and critical missions, the geometry of the room is not known. In this paper dynamic wall pursuit algorithm is proposed to simultaneously recover the room geometry and image the scene behind the wall. We demonstrate that moving targets increase the wall reconstruction accuracy. The algorithm exploits the fact that wrong wall positions populate the reconstructed image with false targets. It makes use of the moving targets in the scene to increase the certainty of the detected wall positions. Simulated results show the effectiveness of this method even at low SNR values.

The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from the adjacent cells. The proposed GLRT is also used at the end of the second transmission for the final decision. Extensive performance evaluation of the proposed detector and adaptive waveform for various multipath scenarios is presented. The performance analysis prove that the proposed method improves the Signal-to-Clutter Ratio (SCR) of the received signal, and the detection performance with multipath exploitation.

Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years. The abundant multipath signals severely interfere with the detection performance and accuracy of parameter estimation of traditional algorithms. Under Gaussian white noise environments, this letter proposes an adaptive time reversal (TR) waveform covariance matrix (WCM) design method with multipath exploitation to improve the maximum signal-to-noise ratio (SNR) at the receiver in multipath environments. This equivalently improves the detection probability. The proposed two-stage algorithm firstly adapts the time-reversal echo to construct a multipath information matrix with a Hermitian structure. Secondly, the letter transforms the maximized SNR problem into semidefinite programming (SDP), which is constrained by a constant total transmit power. Consequently, the waveform covariance matrix is obtained by solving semidefinite programming. Simulation experiments verify the adaptability and effectiveness of the proposed algorithm.

Recently, indoor target localization became an area of interest due to its diverse applications. In indoor target localization, surrounding environment creates multipath components, which can be exploited to aid in localization process. A number of studies have been proposed to employ multipath exploitation in localizing indoor targets. However, their localization errors can still be improved. This study proposed a new localization model based on multipath exploitation techniques by using triangulation method. Ultra-wide band signals were resolved and associated using marginal antenna-based scheme. The estimate of the target location was then obtained using measured round-trip time delays. The location was determined by applying the simple trigonometry on the triangle in which real radar, virtual radars, and the target location are the vertices of the triangle in question. Simulation results show that the proposed method has improved the localization error over a wide range of timing errors, target locations and room sizes with the overall maximum localization error of 1.4 m equivalent to 22.2% improvement as compared to 1.8 m localization error obtained using the method developed by the Muqaibel et al. (2017).

25th Signal Processing and Communications Applications Conference (SIU) (2017 : Antalya; Turkey), Designing adaptive detector has gathered remarkable attention for modern radar systems in order to acquire more reliable target detection. Most of the work so far on adaptive detection have modeled multipath returns as clutter. In this work, we study the effects of multipath exploitation on adaptive target detection for point like targets. To that end, we compare performance of various adaptive detectors in terms of probability of detection in different multipath return senarios. We observe that adaptive detectors which exploit multipath returns have better detection performance when there is a significant resolvable multipath returns.

Geometrical maps of the indoor environment are vital to many applications such as indoor localization and robot navigation. In this paper, a method for three-dimensional indoor mapping using multipath delay and direction estimates is developed. Required high-resolution estimates of multipath propagation path parameters are obtained using radio frequency measurements between two antenna arrays at multiple locations. A ray-tracing algorithm is developed for detecting specular propagation paths of radio signals and corresponding reflection points. A novel method is proposed to extract walls and other planar structures from the cloud of reflection points. The empirical results show an improved precision and enhanced geometric information compared to previous experiments.