Sahar Movaghati

Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering

Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particle filter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different road branches after reaching to a road junction. In this approach, first, EKF traces a road until a stopping criterion is met. Then, instead of terminating the process, the results are passed to the PF algorithm which tries to find the continuation of the road after a possible obstacle or to identify all possible road branches that might exist on the other side of a road junction. For further improvement, we have modified the procedure for obtaining the measurements by decoupling this process from the current state prediction of the filter. Removing the dependence of the measurement data to the predicted state reduces the potential for instability of the road-tracing algorithm. Furthermore, we have constructed a method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity.

Characterizing the path coverage of random wireless sensor networks

Wireless sensor networks are widely used in security monitoring applications to sense and report specific activities in a field. In path coverage, for example, the network is in charge of monitoring a path and discovering any intruder trying to cross it. In this paper, we investigate the path coverage properties of a randomly deployed wireless sensor network when the number of sensors and also the length of the path are finite. As a consequence, Boolean model, which has been widely used previously, is not applicable. Using results from geometric probability, we determine the probability of full path coverage, distribution of the number of uncovered gaps over the path, and the probability of having no uncovered gaps larger than a specific size. We also find the cumulative distribution function (cdf) of the covered part of the path. Based on our results on the probability of full path coverage, we derive a tight upper bound for the number of nodes guaranteeing the full path coverage with a desired reliability. Through computer simulations, it is verified that for networks with nonasymptotic size, our analysis is accurate where the Boolean model can be inaccurate.

Particle-Based Message Passing Algorithm for Inference Problems in Wireless Sensor Networks

Optimal distributed estimation algorithms are usually not practical for wireless sensor networks (WSNs). This is because, in a general setup, these algorithms have high computational and data communication costs. Thus, sub-optimal algorithms that use quantized data and are based on linear and Gaussian approximations have been proposed in the literature. Such approximations are not always applicable. In this paper, we propose a distributed estimation method based on the well-known sum-product algorithm. To maintain a feasible complexity for WSNs, the sum-product update rules are reformulated using particle filtering. We consider the problem of distributed target tracking based on quantized data in a WSN. After deriving the factor graph representation of this tracking problem, we apply our proposed algorithm. We then study its performance based on the number of quantization bits, the number of particles and the measurement noise.

Energy-Efficient Quantization for Parameter Estimation in Inhomogeneous WSNs

The estimation of an unknown parameter using distributed measurements is an essential problem in many wireless sensor network (WSN) applications. In a WSN, the shortage of resources, specially the energy in the sensors, acts as a constraint on the design of estimation algorithms. The existing studies on this problem have developed algorithms that try to limit the total energy in the whole network, yet not considering each sensors energy consumption individually. We develop an estimation algorithm for inhomogeneous environments considering the transmission load of individual sensors.

Using Unscented Kalman Filter for Road Tracing from Satellite Images

The extended Kalman filter with profile matching has been employed to extract road maps in satellite images. This algorithm suffers from several drawbacks that result in its poor performance in difficult situations. To improve its performance in those situations, like junctions and varying road characteristics, we propose to use the unscented Kalman filter which can deal better with the nonlinearity of the road model. Additionally, we use an approach to dissociate the system measurements from the current state prediction of the Kalman filter. This method removes the potential for the instability of the algorithm. Finally, we introduce a technique based on clustering of the road profiles to properly maintain a database on various road characteristics along the way. This way we provide a means to continue tracking even when the road profile undergoes significant variations.

Distributed Binary Quantization of a Noisy Source in Wireless Sensor Networks

In distributed (decentralized) estimation in wireless sensor networks, an unknown parameter must be estimated from some noisy measurements collected at different sensors. Due to limited communication resources, these measurements are typically quantized before being sent to a fusion center, where an estimation of the unknown parameter is calculated. In the most stringent condition, each measurement is converted to a single bit. In this study, we propose a distributed quantization scheme which is based on single-bit quantized data from each sensor and achieves high estimation accuracy at the fusion centre. We do this by designing some local binary quantizers which define a multithreshold quantization rule for each sensor. These local binary quantizers are initially designed so that together they mimic the functionality of a multilevel quantizer. Later, their design is improved to include some error-correcting capability, which further improves the estimation accuracy from the sensors’ binary data. The distributed quantization formed by such local binary quantizers along with the proper estimator proposed in this work achieves better performance, compared to the existing distributed binary quantization methods, specially when fewer sensors with low measurement noise are available.

On Path Coverage of Wireless Sensor Networks

Path coverage is one of the applications of wireless sensor networks where the network is responsible for monitoring a path and detecting any object that crosses it. Here, we study the path coverage of a randomly deployed wireless sensor network when the path length and number of sensors are finite (thus the widely used Boolean model is not applicable). More specifically, we find the probability of complete path coverage, the distribution of the number of uncovered gaps, and the probability of having all the gaps smaller than a given size. Furthermore, we investigate the distribution of the covered length of the path. The accuracy of our analysis is also verified through computer simulations, where it is observed that for non-asymptotic cases the Boolean model can be inaccurate.