An improved SVD-based wall clutter mitigation method for through-the-wall radar imaging

This paper presents an improved SVD-based method for wall clutter mitigation in through-the-wall radar imaging. The dominant wall singular components are identified from the singular value spectrum. A subspace projection method is then applied to remove the strong wall clutter, residing in the dominant singular components, and separate the target signal from noise. The remaining wall clutter residual, which is mixed with the target signal, is suppressed by segmenting the range profile of the signal residing in the subspace orthogonal to the wall and noise subspaces. A Gaussian mixture is used to model the range profile, and the optimum segmentation threshold is found by minimizing the Bayes error. Experiments results show that the proposed method is more effective at reducing wall clutter and preserving the targets than some of the existing wall clutter mitigation methods

Modified Low Rank Approximation for Detection of Weak Target by Noise space Exploitation in Through Wall Imaging

Low dielectric materials referred as weak targets are very difficult to detect behind the wall in through wall imaging (TWI) due to strong reflections from wall. TWI Experimental data collected for low dielectric target behind the wall and transceiver on another side of the wall. Recently several researchers are using low-rank approximation (LRA) for reduction of random noise in the various data. Explore the possibilities of using LRA for TWI data for improving the detection of low dielectric material. A novel approach using modification of LRA with exploiting the noise subspace in singular value decomposition (SVD) to detect weak target behind the wall is introduced. LRA consider data has low rank in f-x domain for noisy data, local windows are implemented in LRA approach to satisfy the principle assumptions required by the LRA algorithm itself. Decomposed TWI data in the noise space of the SVD to detect the weak target adaptively. Results for modified LRA for detection of weak target behind the wall are very encouraging over LRA

Effect and Compensation of Timing Jitter in Through-Wall Human Indication via Impulse Through-Wall Radar

Impulse through-wall radar (TWR) is considered as one of preferred choices for through-wall human indication due to its good penetration and high range resolution. Large bandwidth available for impulse TWR results in high range resolution, but also brings an atypical adversity issue not substantial in narrowband radars — high timing jitter effect, caused by the non-ideal sampling clock at the receiver. The fact that impulse TWR employs very narrow pulses makes little jitter inaccuracy large enough to destroy the signal correlation property and then degrade clutter suppression performance. In this paper, we focus on the timing jitter impact on clutter suppression in through-wall human indication via impulse TWR. We setup a simple timing jitter model and propose a criterion namely average range profile (ARP) contrast is to evaluate the jitter level. To combat timing jitter, we also develop an effective compensation method based on local ARP contrast maximization. The proposed method can be implemented pulse by pulse followed by exponential average background subtraction algorithm to mitigate clutters. Through-wall experiments demonstrate that the proposed method can dramatically improve through-wall human indication performance

Sparsity-based autoencoders for denoising cluttered radar signatures

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%

Incoherent Radar Imaging For Breast Cancer Detection and Experimental Validation Against 3d Multimodal Breast Phantoms

: In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure

Wall clutter mitigation and target detection using Discrete Prolate Spheroidal Sequences

Abstract—We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS’s) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique. Keywords—radar imaging, wall clutter mitigation, target detec-tion, Compressive Sensing, Discrete Prolate Spheroidal Sequences I

Through-The-Wall Detection Using Ultra Wide Band Frequency Modulated Interrupted Continuous Wave Signals

Through-The-Wall-Detection (TTWD) techniques can improve the situational awareness of police and soldiers, and support first responders in search and rescue operations. A variety of systems for TTWD based on different waveforms have been developed and presented in the literature, e.g. radar systems based on pulses, noise or pseudo-noise waveforms, and frequency modulated continuous wave (FMCW) or stepped frequency continuous wave (SFCW) waveforms. Ultra wide band signals are normally used as they provide suitable resolution to discriminate different targets. A common problem for active radar systems for TTWD is the strong backscattered signal from the air-wall interface. This undesired signal can overshadow the reflections from actual targets, especially those with low radar cross section like human beings, and limit the dynamic range at the receiver, which could be saturated and blocked. Although several techniques have been developed to address this problem, frequency modulated interrupted continuous wave (FMICW) waveforms represent an interesting further approach to wall removal, which can be used as an alternative technique or combined with the existing ones. FMICW waveforms have been used in the past for ionospheric and ocean sensing radar systems, but their application to the wall removal problem in TTWD scenarios is novel. The validation of the effectiveness of the proposed FMICW waveforms as wall removal technique is therefore the primary objective of this thesis, focusing on comparing simulated and experimental results using normal FMCW waveforms and using the proposed FMICW waveforms. Initially, numerical simulations of realistic scenarios for TTWD have been run and FMICW waveforms have been successfully tested for different materials and internal structure of the wall separating the radar system and the targets. Then a radar system capable of generating FMICW waveforms has been designed and built to perform a measurement campaign in environments of the School of Engineering and Computing Sciences, Durham University. These tests aimed at the localization of stationary targets and at the detection of people behind walls. FMICW waveforms prove to be effective in removing/mitigating the undesired return caused by antenna cross-talk and wall reflections, thus enhancing the detection of targets