Occlusions can degrade object tracking performance in sensor imaging systems. This paper describes a robust approach to object tracking that fuses video frames with RF data in a Bayes-optimal way to overcome occlusion. We fuse data from these heterogeneous sensors, and show how our approach enables tracking when each modality cannot track individually. We provide the mathematical framework for ...

Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage ecosystem. location of incident hotspot can sometimes have impact early fire detection systems. With advancement intelligent sensor-based control technologies, multi-sensor da...

A statistical signal processing approach to multisensor image fusion is presented for concealed weapon detection (CWD). This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the model parameters and the fused image. We demonstrate...

Due to the communication delays and the time differences in the fusion systems, there exists the asynchronous multisensor information fusion problem instead of the synchronous one. Firstly the continuous-time system is discretized in the fusion center and all the sensor nodes with different sample intervals, then it is found that the measurement noises are correlated with each other and also co...

The recent advances of GPS/IMU have made it possible to perform the multisensor, multi-resolution and multi-spectral data fusion utilizing GIS technique. In the present study we utilized the Airborne Digital Sensor (ADS40) image and Digital Surface Model (DSM) by pixel matching techniques and studied the data fusion capability with LIDAR data from Airborne Laser Scanner (ALS40) and filtered Dig...

The fusion of multisensorial data is a common practice to identify a world model when data coming from a single sensor is unreliable. A possible approach to multisensor data fusion consists in identifying common interpretations of data coming from different sensors, and to integrate the information represented as symbols related to the possible interpretations. In this paper, we present an arch...

Remote sensing delivers multimodal and –temporal data from the Earth’s surface. In order to cope with these multidimensional data sources and to make the most of them, image fusion is a valuable tool. It has developed over the past few decades into a usable image processing technique for extracting information of higher quality and reliability. As more sensors and advanced image fusion techniqu...

We will present case studies in the fusion of di erent sensory types on arm and mobile robots We will rst present a system in which an infrared heat sensor and ultrasonic sensors mounted on a mobile robot are used together to detect human intruders In this case mul tisensor fusion is necessitated by the fact that each sensor by itself is inadequate for the mission The signals from the di erent ...

In this paper, a novel approach based on total variation and markov random field is proposed for pixel-level image fusion. In the proposed approach, fusion is posed as an inverse problem and an image formation model is used as the forward model. Considering the spatial correlation of sensor selectivity factor, total variation (TV) and markov random field (MRF) model are employed to estimate the...

The integration and fusion of information, from a combination of different types of instruments (sensors), is often used in the design of control systems. Typical applications that can benefit the use of multiple sensors are industrial tasks, military command, mobile robot navigation, multi-target tracking, and aircraft navigation. In recent years, there has been growing interest to fuse multis...