Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

Implementation of tracking algorithms for multistatic systems

Due to the increased prevalence of ubiquitous communication technologies and the reduced cost of electronic components, there is an increasing interest in developing networked radar systems. Such networked radar systems offer potential benefits in robustness as well as improvements in performance for detection, tracking and classification. As a branch of applied computer sciences sensor data fusion addresses the ability to process this vast quantity of information, generated by multiple sources, in an effective way. The purpose of this thesis is to validate the tracking algorithms implemented, to determine whether they are capable of identifying and tracking two closely spaced targets, to determine the capability of the system to track a target that moves with fast maneuvers as well as the ability to handle a potential simultaneous attack from both the air and the sea. We present a method for multiple target tracking using multiple sensors both for passive and active sensors. Firstly, regarding active radar, we describe an algorithm for combining range-Doppler data from multiple sensors to perform multi-target tracking. In particular we considered the problem of very poor azimuth resolution. In this case more than two sensors are needed to triangulate target tracks and techniques like multilateration are needed to overcome the problem. Then two tracking algorithms for bistatic DVB-T passive radar based on the Extended Kalman Filter (for single target tracking) and on the Kalman filter (for multiple target tracking), exploiting measurement of bistatic range and bistatic velocity of a target are described. Also the direction of arrival of the target is estimated through beamforming and then used in the tracking model. The algorithms have been tested and validated by using real data

Technological Perspectives of Countering UAV Swarms

Conventional AD systems have been found less effective for countering UAVs and loitering munitions. Thishas necessitated the development of counter-UAV systems with different functionalities. A cluster of armed UAVsas swarm formations has further rendered the conventional AD systems far from effective, emphasizing the need to consider countering swarms as the most crucial element in new-generation aerial threat mitigation strategies. In this paper, the capabilities of UAV swarms and vital military assets exposed to such attacks are identified. To protect the vital assets from aerial swarm threats, ideal system characteristics of a counter-UAV (C-UAV) swarm system to overcome the challenges are discussed. Currently available acquisition & engagement technology is analyzed and the application of these systems to counter swarm applications is brought out. New requirements are discussed and a conceptual design of a layered system is derived which can handle a large spectrum of aerial threats including a swarm of UAVs. This system is expected to have a higher rate of engagement and can be designed with low-cost network-integrated systems

A Radar-Enabled Collaborative Sensor Networking Integrating COTS Technology for Surveillance and Tracking

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (\u3c$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios

Tracking a non-cooperative target using a Doppler radar wireless sensor network

Tracking or target localization is used in a wide range of important tasks from knowing when your flight will arrive to ensuring your mail is received on time. Tracking provides the location of resources enabling solutions to complex logistical problems. Wireless Sensor Networks (WSN) create new opportunities when applied to tracking, such as more flexible deployment and real-time information. When radar is used as the sensing element in a tracking WSN better results can be obtained; because radar has a comparatively larger range both in distance and angle to other sensors commonly used in WSNs. This allows for less nodes deployed covering larger areas, saving money. In this report I implement a tracking WSN platform similar to what was developed by Lim, Wang, and Terzis. This consists of several sensor nodes each with a radar, a sink node connected to a host PC, and a Matlab© program to fuse sensor data. I have re-implemented their experiment with my WSN platform for tracking a non-cooperative target to verify their results and also run simulations to compare. The results of these tests are discussed and some future improvements are proposed