Moving target detection in multi-static GNSS-based passive radar based on multi-Bernoulli filter

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accuratel

Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing

In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the literature, the ability to perform inference on high-dimensional problems in a computationally efficient manner remains elusive. In response, we propose a probabilistic dynamic CS signal model that captures both amplitude and support correlation structure, and describe an approximate message passing algorithm that performs soft signal estimation and support detection with a computational complexity that is linear in all problem dimensions. The algorithm, DCS-AMP, can perform either causal filtering or non-causal smoothing, and is capable of learning model parameters adaptively from the data through an expectation-maximization learning procedure. We provide numerical evidence that DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety of operating conditions. We further describe the result of applying DCS-AMP to two real dynamic CS datasets, as well as a frequency estimation task, to bolster our claim that DCS-AMP is capable of offering state-of-the-art performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure

Bayesian Account of Perceptual Decision-Making

openBy making predictions, learning from mistakes, and updating memories to include new information, the brain enables adaptive behaviour in daily activities. For instance, in perceptual decision-making tasks, it is critical to rapidly select the best behaviours based on current sensory inputs, that are frequently ambiguous or masked by noise. Using random dot motion (RDM) tasks, previous research on perceptual decision-making emphasised the role of sensory information in directing behaviour by varying simply the stimulus coherence and analysed the data using models that more or less explicitly presuppose bottom-up processing (e.g., drift-diffusion models). However, accumulating evidence (e.g., Bayesian models and the Free Energy Principle applications) suggests that the brain approximates optimal Bayesian inference rather than simply being a passive information filter. As a result, we need to shed light on the computations involved in goal-directed decision-making, with a focus on the predictive mechanisms at work in volatile experimental contexts. Here we used a probabilistic Random Dot Kinematogram (pRDK) in which the probability of witnessing a rightward/leftward motion changes throughout the task. Furthermore, to operationalise the predictions of the left and right dot motion in each trial based on previous information, an Ideal Bayesian Observer was used. This allowed us to study top-down predictions' impact on decision-making. The behavioural analyses revealed a substantial impact on behaviour from both coherence levels and probabilistic contexts. Specifically, a significant interaction between the probability of motion and direction was found, indicating faster responses when predictions matched what was presented.By making predictions, learning from mistakes, and updating memories to include new information, the brain enables adaptive behaviour in daily activities. For instance, in perceptual decision-making tasks, it is critical to rapidly select the best behaviours based on current sensory inputs, that are frequently ambiguous or masked by noise. Using random dot motion (RDM) tasks, previous research on perceptual decision-making emphasised the role of sensory information in directing behaviour by varying simply the stimulus coherence and analysed the data using models that more or less explicitly presuppose bottom-up processing (e.g., drift-diffusion models). However, accumulating evidence (e.g., Bayesian models and the Free Energy Principle applications) suggests that the brain approximates optimal Bayesian inference rather than simply being a passive information filter. As a result, we need to shed light on the computations involved in goal-directed decision-making, with a focus on the predictive mechanisms at work in volatile experimental contexts. Here we used a probabilistic Random Dot Kinematogram (pRDK) in which the probability of witnessing a rightward/leftward motion changes throughout the task. Furthermore, to operationalise the predictions of the left and right dot motion in each trial based on previous information, an Ideal Bayesian Observer was used. This allowed us to study top-down predictions' impact on decision-making. The behavioural analyses revealed a substantial impact on behaviour from both coherence levels and probabilistic contexts. Specifically, a significant interaction between the probability of motion and direction was found, indicating faster responses when predictions matched what was presented

Random finite sets in multi-target tracking - efficient sequential MCMC implementation

Over the last few decades multi-target tracking (MTT) has proved to be a challenging and attractive research topic. MTT applications span a wide variety of disciplines, including robotics, radar/sonar surveillance, computer vision and biomedical research. The primary focus of this dissertation is to develop an effective and efficient multi-target tracking algorithm dealing with an unknown and time-varying number of targets. The emerging and promising Random Finite Set (RFS) framework provides a rigorous foundation for optimal Bayes multi-target tracking. In contrast to traditional approaches, the collection of individual targets is treated as a set-valued state. The intent of this dissertation is two-fold; first to assert that the RFS framework not only is a natural, elegant and rigorous foundation, but also leads to practical, efficient and reliable algorithms for Bayesian multi-target tracking, and second to provide several novel RFS based tracking algorithms suitable for the specific Track-Before-Detect (TBD) surveillance application. One main contribution of this dissertation is a rigorous derivation and practical implementation of a novel algorithm well suited to deal with multi-target tracking problems for a given cardinality. The proposed Interacting Population-based MCMC-PF algorithm makes use of several Metropolis-Hastings samplers running in parallel, which interact through genetic variation. Another key contribution concerns the design and implementation of two novel algorithms to handle a varying number of targets. The first approach exploits Reversible Jumps. The second approach is built upon the concepts of labeled RFSs and multiple cardinality hypotheses. The performance of the proposed algorithms is also demonstrated in practical scenarios, and shown to significantly outperform conventional multi-target PF in terms of track accuracy and consistency. The final contribution seeks to exploit external information to increase the performance of the surveillance system. In multi-target scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information is integrated by using a fixed-lag smoothing procedure, named Knowledge-Based Fixed-Lag Smoother (KB-Smoother). The proposed combination IP-MCMC-PF/KB-Smoother yields enhanced tracking

Compressed Sensing with Coherent and Redundant Dictionaries

This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or incoherent dictionary, but in a truly redundant dictionary. This work thus bridges a gap in the literature and shows not only that compressed sensing is viable in this context, but also that accurate recovery is possible via an L1-analysis optimization problem. We introduce a condition on the measurement/sensing matrix, which is a natural generalization of the now well-known restricted isometry property, and which guarantees accurate recovery of signals that are nearly sparse in (possibly) highly overcomplete and coherent dictionaries. This condition imposes no incoherence restriction on the dictionary and our results may be the first of this kind. We discuss practical examples and the implications of our results on those applications, and complement our study by demonstrating the potential of L1-analysis for such problems

DEVELOPMENT OF AN AUTOMATED GAS-LEAKAGE MONITORING SYSTEM WITH FEEDBACK AND FEEDFORWARD CONTROL BY UTILIZING IOT

Liquefied Petroleum Gas (LPG) is used in many ranges of applications like home and industrial appliances, in vehicles and as a propellant and refrigerator. However, leakage of LPG produces hazardous and toxic impact on human begins and other living creatures. There by, the authors developed a system to monitor the LPG gas leakage and make alert to users of it. In this research, MQ-6 gas sensor is used for sensing the level of gas concentration of a closed volume; and to monitor the consequences of environmental changes an IoT platform has been introduced. Robust control along with cloud based manual control has been applied so that the gas leakage can be prevented in the response of either feedback or feedforward commands individually. It switches on the specified relays to control the level of gas concentration in the time of leakage the excess gas in times of leakage. It rechecks the value again and again if it crosses 300 ppm it will setup a relay-based switching on control mechanism using Thingspeak cloud. The controller used here is Node-MCU v:1.0. This research provides design approach on both software and hardware. Hence an embedded system comprising of Relay switches, Embedded C++, Gas sensor, Temperature & Humidity sensor along with Internet of Things (IoT) is fabricated to meet the objectives of the current research