Improved compressive sensing with antenna directivity for TWRI

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.

With recent advances in both algorithm and component technologies, through-the-wall radar imaging (TWRI) is emerging as an affordable sensor technology in civil and military settings. Design of appropriate waveforms is important for improving the performance of TWRI systems. We present optimal waveforms, based on the matched illumination concept, for detection of targets inside enclosed structures using a monostatic multi-antenna radar system. We evaluate the effect of these optimal detection waveforms on the performance of through-the-wall radar imaging. The design flexibility for transmit signal duration and its role in the ultimate range resolution of the TWRI system is investigated. Supporting examples based on numerical electromagnetic modeling are also provided.

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.

Ultra wideband (UWB) is a promising technology in delivering high data rate for short range wireless communication systems. Because of their large bandwidth, UWB signals may encounter some problems especially with high sampling rate requirements. Moreover, coherence existence with other narrowband systems is a major concern which needs to be addressed through proper mechanisms. The problem becomes so complex if multiple users exist. Since narrowband interference (NBI) signals have sparse representation in the discrete cosine transform (DCT) domain, they can be estimated and suppressed using Compressive Sensing (CS). CS also has the ability to reduce the high sampling rate requirements. For training based NBI mitigation with CS, three groups of pilot symbols are used to estimate the NBI signal subspace, the UWB signal subspace, and to provide information about the channel. In this paper, the distribution of pilot symbols among the three groups is investigated in the presence of strong NBI. The investigation is based on the bit error rate performance and throughput. The influence of each pilot symbols group is studied. The performance is also evaluated in the presence of multiuser interference in addition to the NBI. Simulation results show that the size of the third group of pilot symbols which is used to estimate the channel is the most dominant one.

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.

For precise synchronization of ultra wide band (UWB) signals in wireless channels, narrow band interference (NBI) suppression is a challenging issue. This is more relevant for real time channels which are non-linear in nature and are coexisted by narrow band wireless systems. An interference suppression scheme coupled to an energy detection based synchronization approach designed using second-order complex adaptive notch filter (ANF) is reported in this paper. This ANF uses a gradient descent algorithm for tracking the filter coefficients. The use of the same exploits the correlation difference property of signals received and suppresses NBIs in UWB signals. Consequently, it significantly reduces the computational complexity compared to the existing adaptive filters and requires lower power for an efficient hardware implementation. The data rate of UWB is usually high, so a combined pipelined-parallelism (CPP) approach is proposed which effectively simplifies the hardware design unlike direct and cascade forms. Detailed analysis suggest that the proposed scheme provides better performance in terms of power, speed, convergence and stability. Moreover, this scheme when used in conjunction with energy detection receivers significantly improves the interference tolerance margin, thereby raising the performance levels of synchronization with energy detection approach in non-coherent energy detection receivers.

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.

The interference of ultra wideband (UWB) signals on conventional radio systems, called victims, is still an open issue. In this paper we show that not only the interference power but also the amplitude statistics have significant influence on the bit error rate (BER) of the victim. The amplitude probability distribution (APD) of UWB signals at the channel and inside the victim's receiver is studied analytically and verified by simulation. It is shown under which boundary conditions the UWB signal looses its pulsed behavior inside the victim's receiver. From this an interference model for the additional BER caused by UWB is proposed. The theoretical model is evaluated by simulations with a WLAN and a UMTS victim

Received ultra wideband (UWB) signals in most indoor environments are stochastic in nature, typically composed of abundant multi-path components with random attenuation and arrival time. A commonly adopted detection strategy is to use a chosen deterministic template to correlate the received signal segment by segment, and only those segments having significant cross-correlation with the template are selected, temporally synchronized and co-phased in their projections for coherent combining. Correlating a deterministic template with a random signal usually results in only partial energy captured by the receiver since a random signal is of multidimensions. In this paper, we construct a receiver with multiple orthogonal branches to fully capture the signal energy, in much the same way as a quadrature receiver does in conventional digital communications. The local reference signals are derived from the orthogonal expansion of UWB signals. The random projections of a UWB signal onto different orthogonal basis functions are non-coherently combined, leading to the concept of dimension diversity. Dimension diversity can be either used alone, or alongside a Rake receiver to further exploit path diversity. The error performance of proposed receivers is analyzed, and their superiority over the conventional ones is examined by numerical results based on real UWB data.

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) 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.