Self-Calibration of Acoustic Scalar and Vector Sensor Arrays

In this work, we consider the self-calibration problem of joint calibration and direction-of-Arrival (DOA) estimation using acoustic sensor arrays. Unlike many previous iterative approaches, we propose solvers that can be readily used for both linear and non-linear arrays for jointly estimating the sensor gain, phase errors, and the source...

Bayesian Compressive Sensing For Radar Based DoA Estimation Using Sparse Sensor Arrays

Direction of Arrival (DoA) estimation is an important topic in radar application and has significant importance for advanced driver assistant systems in the automotive industry. While there is an increasing need for higher resolution and increased target detection and DoA estimation performance, such improvements often require increased hardware...

ADS-B Signal Integrity and Security Verification Using a Coherent Software Defined Radio: Mitigation of the threat of maliciously injected signals in ADS-B networks

Automatic Dependent Surveillance – Broadcast (ADS-B) is an operational enhancement as part of next-generation air transportation systems in Air Traffic Control. It enables aircraft and airport vehicles to periodically broadcast the information from their on-board equipment, like their identification, GPS location, velocity, and intent. Compared...

Joint Features Extraction for Multiple Moving Targets Using (Ultra-)Wideband FMCW Signals in the Presence of Doppler Ambiguity

This article addresses the joint estimation of range, velocity and azimuth for multiple fast-moving targets using (ultra-)wideband (UWB) frequency-modulated continuous-wave (FMCW) radar with a phased array in the presence of Doppler ambiguities. The range migration of moving targets is described with the coupling of the fast-time and slow...

Blind Calibration for Acoustic Vector Sensor Arrays

In this paper, we present a calibration algorithm for acoustic vector sensors arranged in a uniform linear array configuration. To do so, we do not use a calibrator source, instead we leverage the Toeplitz blocks present in the data covariance matrix. We develop linear estimators for estimating sensor gains and phases. Further, we discuss the...

Joint Detection and Localization of an Unknown Number of Sources Using the Algebraic Structure of the Noise Subspace

Source localization and spectral estimation are among the most fundamental problems in statistical and array signal processing. Methods that rely on the orthogonality of the signal and noise subspaces, such as Pisarenko's method, MUSIC, and root-MUSIC, are some of the most widely used algorithms to solve these problems. As a common feature,...