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This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver. [ABSTRACT FROM AUTHOR]

Through-the-wall radar imaging (TWRI) has attracted a great deal of attention in several sensitive applications, including rescue missions and military operations. Notwithstanding its broad range of applications, TWRI suffers from path-loss because distant targets experience more attenuation of signal power than those closer to the transceiver. This challenge may lead to missed targets with important information necessary for analysis and informed decision making. Responding to the challenge, we have developed a signal model with an effective path-loss compensator incorporating a free space exponent. Furthermore, multipath exploitation and compressive sensing techniques were employed to develop an effective algorithm for isolating residual clutter that may corrupt real targets. The proposed signal model integrates contributions from the front wall, multipath returns, and path-loss. Compared with the state-of-the-art model under the same experimental conditions, simulation results show that the proposed model achieves improved signal-to-clutter ratio, relative clutter peak, and probability of detection by 13.1%, 17.4% and 33.6%, respectively, suggesting that our model can represent the scene more accurately. [ABSTRACT FROM AUTHOR]

Traditionally, radar‐based localisation systems are designed to deal with targets in line‐of‐sight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non‐line‐of‐sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two‐stage localisation method using a single monostatic radar via association of multipath time‐of‐arrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray‐tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super‐resolution rather than the matched filter. Then based on the finite‐set statistics (FISST) theory, a grid‐based multi‐observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetre‐wave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) target detection utilizing multipath plays an important role in anti-terrorism, urban warfare, indoor rescue and intelligent driving. In this paper, an imaging method based on single-channel ultra-wideband (UWB) radar and dual-view observation is proposed for detecting NLOS targets. First, the cause of producing ghosts from a single view in a previous study is analyzed in detail. On this basis, with focusing on dual-view observation, the ghost problem is handled at two levels. From the level of the radar echo, path partition is conducted with the utilization of dual-view observation, solving the problem of uneven energy among multipaths. After that, two images are generated by multipath imaging, respectively, which reduces the accumulation of ghosts. From the level of the image, owing to the distinct distribution of ghosts between the imaging result, the ghosts at different locations are eliminated by image fusion. Experimental results demonstrate that compared with single view and direct multipath imaging, the approach based on dual-view observation successfully eliminates most ghosts while retaining the targets in both single-target and double-targets scenarios, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Detection and localization in urban environments is a very recent radar problem. In this paper, we investigate the possibility of detecting and locating targets not in direct line of sight (NLOS) areas with a single portable radar by exploiting multipath returns. We propose two algorithms, which handle the information provided by multipath returns in different ways to detect and estimate the NLOS target position. We also present an original method to select the number of paths to take into account in the algorithms in order to maximize detection probabilities. Numerical results show good efficiency of the proposed algorithms for problems of both detection and localization. We show that applying these algorithms improves detection performance compared to a classic matched filter in a typical urban scenario. Experimental results on a real dataset allow us to validate our multipath model in urban environments, and in particular to show that it is possible to retrieve the target location even with rough knowledge of the scene geometry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]