Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review

Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes.AKey words: spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging

Determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator of a DC Signal in AWGN Using Laplace Transform

This paper presents an alternative approach for the determination of Cramer-Rao Lower Bound (CRLB) and Minimum Variance Unbiased Estimator (MVUE) using Laplace transformation. In this work, a DC signal in Additive White Gaussian Noise (AWGN) was considered. During the investigation, a number of experiments were conducted to analyze different possible outputs under different conditions, and then the patterns of the outcomes were studied. Finally closed-form expressions for the CRLB and MVUE were deduced employing the Laplace transformation. The resulting expressions showed that the proposed method has almost the same number of steps as the existing method. However, the latter requires only the knowledge of algebra to arrive at the CRLB expressions contrary to the existing approach where a strong mathematical background is required and hence making it superior over the existing method, in that sense. Keywords: Additive White Gaussian Noise, Cramer-Rao Lower Bound, DC-Value, Laplace transform, and Minimum Variance Unbiased Estimator (MVUE)

Multipath Exploitation-Based Indoor Target Localization Model Using Single Marginal Antenna

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)

Possibilities and Challenges of Radio Frequency Energy Harvesting in Dar es Salaam, Tanzania

This paper presents investigation on the possibilities and challenges of harvesting ambient Radio Frequency Energy (RFE) at Dar es Salaam region in Tanzania. The Radio Frequency (RF) signals were measured using a Rohde and Schwarz FSC 3 spectrum analyzer observing available frequencies with their respective power. Among several RF signals received, the most powerful signals observed were; 800 MHz, 950 MHz, 2100 MHz and 2400 MHz, having average signal strengths of about -30.29 dBm, -35.94 dBm, -42.90 dBm and -30.42 dBm respectively. The power possessed within these frequencies were suitable to be harvested due to their signal strengths, an overall power average of -34.89dBm was obtained and a multi narrowband harvester was designed and simulated using real-time values on Keysight’s Advanced Design System (ADS) 2019. The simulation results confirm a promising possibility of harvesting RF energy to power ultra-low-power devices in the Internet of Things (IoT) and beyond

Target-to-Target Interaction in Through-the-Wall Radars under Path Loss Compensated Multipath Exploitation-Based Signal Model for Sparse Image Reconstruction

Multipath caused by reflections from interior walls of buildings has been a long-standing challenge that affects Through-the-Wall Radar Imaging. Multipath creates ghost images that introduce confusion when detecting desired targets. Traditionally, multipath exploitation techniques under the compressive sensing framework have widely been applied to address the challenge. However, the multipath component emanating from target-to-target interactions has not been considered–a consequence that may, under multiple target scenarios, lead to incorrect image interpretation. Besides, far targets experience more attenuation due to free space path-loss, hence resulting into target undetectability. This study proposes a signal model, based on multipath exploitation techniques, by designing a sensing matrix that incorporates multipath returns due to target-to-target interaction and path loss compensation. The study, in addition, proposes the path-loss compensator that, if integrated into the proposed signal model, reduces path loss effects. Simulation results show that the Signal to Clutter Ratio and Relative Clutter Peak improved by 4.9 dB and 1.9 dB, respectively compared to the existing model.Keywords: Compressive sensing, multipath ghost, multipath exploitation, pathloss, path-loss compensator, through-the-wall-radar imaging

Efficient Load Balancing Algorithm in Long Term Evolution (LTE) Heterogeneous Network Based on Dynamic Cell Range Expansion Bias

The traditional scheme for load balancing in a homogeneous Long Term Evolution (LTE) Network where User Equipment (UEs) associate to a node with the strongest received signal strength is not practical for LTE Heterogeneous Network (LTE HetNet) due to power disparity between the nodes. Therefore, dynamic Cell Range Expansion (CRE) based load-balancing schemes were employed by several scholars to address the challenges in the LTE HetNet. However, the fairness index in achieving the desired average user throughput and UE offloading effect is relatively low. In this work, an efficient load-balancing algorithm for LTE HetNet based on dynamic Cell Range Expansion (CRE) was developed to improve the fairness of the network for the desired throughput and UE offloading effect. The simulation results achieve a throughput gain improvement of up to 11%, while the fairness index improves by 6% compared to the existing algorithm. Further, the UEs offloading effect shows a significant improvement of 3% relative to the existing algorithm. Keywords: Fairness Index; Cell Range Expansion; Load Balancing; LTE Heterogeneous Network; Throughpu

Mixed Mode Device-to-Device Communication Scheme for Congestion Reduction and Channel Usage Optimization in 5G Cellular Networks

Device-to-Device (D2D) communication schemes have gained more attention in cellular networks particularly in normalization process of the upcoming 5G networks. They have been investigated in core network offloading, congestion reduction and channel usage optimisation.  The two last cases are among the major constraints in current cellular networks and are the main concerns of this paper. The paper presents a mixed mode D2D communication scheme to decentralize data collection between devices and the base station in order to reduce the number of direct connections at the base station of ultra-dense cells characterized by different levels of channel utilizations or target data rates, as expected for 5G networks. The attachment utility is derived as the overall gain of a device for a target data rate and is used as a metric for D2D association’s decision. Results show that the attachment utility and D2D pairs increase by either increasing the D2D communication range or decreasing devices’ target data rates. A further important consideration is that the proposed mixed mode D2D communication scheme improves the throughput expectation in the cell by 14.2% compared to the regular cellular communication.Keywords:     5G Networks, Channel Usage Optimisation, Congestion Reduction, D2D Communication Scheme, Target Data Rate

Performance Evaluation of Aspect Dependent-Based Ghost Suppression Methods for Through-the-Wall Radar Imaging

There are many approaches which address multipath ghost challenges in Through-the-Wall Radar Imaging (TWRI) under Compressive Sensing (CS) framework. One of the approaches, which exploits ghosts’ locations in the images, termed as Aspect Dependent (AD), does not require prior knowledge of the reflecting geometry making it superior over multipath exploitation based approaches. However, which method is superior within the AD based category is still unknown. Therefore, their performance comparison becomes inevitable, and hence this paper presents their performance evaluation in view of target reconstruction. At first, the methods were grouped based on how the subarrays were applied: multiple subarray, hybrid subarray and sparse array. The methods were fairly evaluated on varying noise level, data volume and the number of targets in the scene. Simulation results show that, when applied in a noisy environment, the hybrid subarray-based approaches were robust than the multiple subarray and sparse array. At 15 dB signal-to-noise ratio, the hybrid subarray exhibited signal to clutter ratio of 3.9 dB and 4.5 dB above the multiple subarray and sparse array, respectively. When high data volumes or in the case of multiple targets, multiple subarrays with duo subarrays became the best candidates. Keywords: Aspect dependent; compressive sensing; point target; through-wall-radar imaging

IoT-Based Smart Fishing Gear for Sustainability of the Tanzania Blue Economy

The fishing industry engages many Tanzanians and is among the leading sectors of Blue Economy in the country. However, fishing practices are small-scale with poor and insufficient number of fishing facilities, hence limiting productivity and efficacy. Studies argue that the low level of technology currently used in the country could possibly be an impeding factor. Specifically, fishers use non-interactive gears that cannot instantaneously update status and alert them whenever the gears are ready for collection. In such scenarios, fishers not only waste their time but also scarce resources such as fuel to facilitate trips to and from the fishing sites to check status of the gears. Thus, the application of Information and Communication Technology (ICT) could improve the production and lower operational costs is hypothesized. ICT solutions can help realize the processes with minimum human interventions while adding intelligence to the systems to make informed decisions and hence improve systems’ efficiency. In this work, an IoT based smart fishing gear that counts and records the number of fishes in the gear and then displays them on a mobile application is proposed. The system can send alerts to the user when the required number of fish is attained, and provides navigation support to localize the distant filled gears. Results show that the system can send the location of a given gear and timely update the number of catches via the mobile application

Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction

Multiplicative and additive noises are often introduced in image signals during the image acquisition process and result into degradation of image features. The work done by Perona and Malik in 1990 and its modified versions revolutionized the way through which noises or speckles are removed. The Perona-Malik model requires tuning of the regularization parameter to control and prevent staircase artifacts in restored images. The current manual tuning is a challenging and time consuming practice when a long queue of images is registered for processing. Attempt to automate the regularization parameter appeared in Perona-Malik model with self-adjusting shape-defining constant. Although both multiplicative and additive noise based automated regularizations were presented, the paper stayed silent on matters concerning the automation method that fits with speckle reduction. This paper therefore, presents a comparative analysis of additive and multiplicative noise based automated regularizations. Simulation results and paired samples T-tests reveal that the multiplicative noise based automation outperforms the additive noise based automation for small speckle variances. However, the two automation methods do not significantly differ when large speckle variances are assumed