Optimal waveform design for multi-antenna monostatic through-the-wall radar imaging

To achieve high resolution through-the-wall radar imaging (TWRI), long wideband antenna arrays need to be considered, thus resulting in massive amounts of data. Compressive sensing (CS) techniques resolve this issue by allowing image reconstruction using much fewer measurements. The performance of different CS algorithms, when applied to TWRI, has not been investigated in a comprehensive and comparative manner. In this paper, popular CS algorithms are evaluated, to see which are most suitable for TWRI applications. As for the evaluation criteria, the notion of F1-score is adopted and used in the context of TWRI; thus emphasising the algorithms ability to reconstruct an image with correctly detected targets. Algorithms responses to different levels of signal-to-noise ratio (SNR) and compression rate are evaluated. Numerical results show that for systems with low SNR, alternating direction based algorithms work better than others. When the SNR is high, algorithms depending on spectral gradient-projection methods give good results even with high compression rates.

In this paper, measured results for complex permittivity of some commonly used building walls under difierent hydration (wetness) levels are presented and a simple hybrid measurement and electromagnetic modeling approach for the estimation of power returns from targets located behind walls in various through-the-wall radar imaging (TWRI) scenarios is discussed. The radar cross section (RCS) of some typical targets of interest, such as an AK47 assault ri∞e and human, are flrst investigated in free-space using numerical electromagnetic modeling. A modifled radar range equation, which analytically accounts for the wall efiects, including multiple re∞ections within a given homogeneous or layered wall, is then employed in conjunction with wideband measured parameters of various common wall types, to estimate the received power versus frequency from modeled targets of interest. The proposed technique, which can be helpful in TWRI dynamic-range system design consideration, is, in principle, applicable to both bistatic and monostatic operations. The results for various wall types, including drywall, brick, solid concrete, and cinder block, the latter two under both wet and dry conditions, are presented.

Through-the-wall radar imaging (TWRI) is usually associated with the use of ultra wideband (UWB) signals to allow for wall penetration and high resolution. It is difficult to keep the antenna pattern fixed at all frequencies. Due to the large bandwidth and associated big data, compressive sensing (CS) is used for TWRI. We propose to embed the directivity of the antenna in the designed sparsity dictionary. The results show improved image reconstruction.

In this paper, we provide a review of the different approaches used to detect activities inside a building using through-the-wall radar. We classify two main types of radar that are used in detection activities behind the wall. Stepped frequency and pulse compression radar are the most frequently used types of radar in through-the-wall radar imaging (TWRI). In addition, a preference has been shown for using frequencies in the range of 1–3 GHz. Though a high volume of research is being done in this area, there are still many open issues. Detectability of the human being, wall modeling, and target differentiation have been identified as the main open issues in the field of TWRI.

Through-the-wall radar imaging (TWRI) provides the capability to see inside buildings using electromagnetic waves for both defence and civilian applications. Unlike traditional free-space radar operation, the presence of walls introduces unique challenges that need to be addressed to render TWRI a viable technology. In this chapter, we present a review of various approaches for building layout determination and imaging of stationary indoor scenes. These approaches effectively address the associated challenges to provide effective and reliable radar operation. Specifically, we consider both ground-based and airborne radar operation and discuss both non-adaptive time-domain and frequency-domain beamforming approaches for through-the-wall image formation of stationary scenes and feature extraction-based building layout determination methods.

Polarimetric through-the-wall radar imaging (TWRI) system has the enhancing performance in the detection, imaging, and classification of concealed targets behind the wall. We propose a group sparse basis pursuit denoising- (BPDN-) based imaging approach for polarimetric TWRI system in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with the nonuniform fast Fourier transform (NUFFT) technique to implement the imaging reconstruction of observed scene. The experimental results have demonstrated that compared to the existing compressive sensing- (CS-) based imaging algorithms, the proposed NUFFT-based SPGL1 algorithm can significantly reduce the required computer memory and achieve the improved imaging reconstruction performance with the high computational efficiency.

Through-the-wall radar imaging (TWRI) is currently an active and growing area of research and development because the detection and localization of targets in enclosed structures has many civilian and military applications such as rescue operations in case of fire or after earthquakes. However, contrary to free-space imaging, methods needed for TWRI are not a straight-forward process and face many challenges, all imposed by the presence of the building walls [1]. One problem pertains to frequency dependent attenuation and phase dispersion caused by exterior walls. This limits the useful frequency range to the UHF band with its lower frequency limited by the antenna size. Consequently, the bandwidth and image resolution of TWR systems is limited. The second issue is related to the scattering from the building walls and the multipath signals they create. The specular reflection and multiple bounces within the wall create a very strong signal much larger than that of the target behind the wall which normally has a small radar cross section (e.g. human). This leads to two problems: If the target is close to the wall, it's return can be completely covered by the wall return or if the target is further away from the wall, it can still be covered by the strong multiple bounce response or the point spread function sidelobes of the wall return. It is possible to reduce the sidelobe level but at the expense of range resolution by windowing the frequency response of the radar return.

A three-dimensional (3D) beamformer for through-the-wall radar imaging (TWI) for a general multilayered wall case is presented. The far field layered medium Green's function is incorporated in the 3D TWI algorithm for the compensation of the wall effect. Numerical results show that the 3D beamformer can provide high quality focused image of the target behind the walls.

This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for com- pressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behind- the-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.

Through-the-wall radar imaging (TWRI) applications allow accurate target localization and high-resolution imaging. However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.

Through-the-wall radar imaging (TWRI) enables critical tasks such as search and rescue, surveillance, and health monitoring. However, strong wall reflections often obscure weak human signatures. This work presents a simulation-driven framework for detecting stationary and breathing individuals at low frequencies behind walls. Synthetic radar datasets were generated in MATLAB, modeling multiple wall types and clutter conditions. A convolutional neural network (CNN)-based U-Net was developed, trained, and validated to suppress wall clutter and enhance target visibility. The model effectively distinguishes human responses from dominant wall echoes, yielding over 30 dB improvement in signal-to-clutter ratio. Tests on synthetic and emulated radar data further confirm its ability to recover weak target echoes while maintaining real-time throughput within FPGA power limits. Results demonstrate the robustness and efficiency of the proposed deep learning approach, establishing a strong foundation for embedded implementation and practical field deployment in complex through-wall scenarios.

This article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

Animals that navigate and hunt by echolocation, such as some bats and marine mammals, have been observed to change their sonar pulse depending on the environment, as well as during hunting. It has become of interest to incorporate these strategies into man-made sonar waveform and receiver design. We examine the benefits of optimal waveform design versus transmitting a linear FM waveform for detecting elastic objects. Performance loss suffered by assuming a point target is also examined. Our approach utilizes a method recently proposed by Kay to design the optimal power spectrum of the transmit waveform. Because there is an unlimited number of waveforms with the same power spectrum, we further impose a time domain constraint, in terms of the signal duration, to obtain a unique optimal waveform. [Work supported by ONR 321US.]

In this paper we describe methodologies for joint design and exploitation of heterogeneous RF Intelligence Surveillance Reconnaissance (ISR) sensor networks. In this work, we provide an algorithm that simultaneously optimizes a waveform set that overtly incorporates the aspect variant nature of the target and clutter signatures along with the mutual interference of the waveforms. The basis of the optimization is a maximization of the composite signal-to-interference-plus-noise ratio. The work reported here generalizes the single optimal waveform design approach of Pillai et. al. to the case of J simultaneous waveforms. Knowledge of the target spectral support for each aspect is assumed and the waveform set is jointly optimized. Simulation results are given that show the waveform characteristics and the increase in signal-to-interference-plus-noise ratio over sets of chirp waveforms and independently optimized waveforms.