Multitarget Joint Delay and Doppler Shift Estimation in Bistatic Passive Radar

Bistatic passive radar (BPR) system does not transmit any electromagnetic signal unlike the active radar, but employs an existing Illuminator of opportunity (IO) in the environment, for instance, a broadcast station, to detect and track the targets of interest. Therefore, a BPR system is comprised of two channels. One is the reference channel that collects only the IO signal, and the other is the surveillance channel which is used to capture the targets\u27 reflected signals. When the IO signal reflected from multiple targets is captured in the surveillance channel (SC) then estimating the delays and Doppler shifts of all the observed targets is a challenging problem. For BPR system, the signal processing algorithms developed so far models the IO waveform as a deterministic process and discretizes the delays and Doppler shifts parameters. In this thesis, we deal with the problem of jointly estimating the delays and Doppler shifts of multiple targets in a BPR system (i.e., a two channel system) when the unknown IO signal is modeled as a correlated stochastic process. Unlike the previous work, we take all the delays and Doppler shifts as continuous-valued parameters to avoid straddle loss due to discretization and propose a computationally efficient Expectation-Maximization (EM) based algorithm that breaks up the complex multidimensional maximum likelihood optimization problem into multiple separate optimization problems. The EM algorithm jointly provides the estimates of all the delays and Doppler shifts of the targets along with the estimate of each target\u27s component signal in the SC and the estimate of the unknown IO signal. We also derive the Cramer-Rao lower bound for the considered multitarget estimation problem with stochastic IO signal. Numerical simulations are presented where we compare our proposed EM-based multi-target estimator with the widely used conventional cross correlation estimator under different multitarget environments

Canonical correlations for target detection in a passive radar network

In this work, we consider a two-channel multiple-input multiple-output (MIMO) passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of unknown basis, which is correlated with the subspace signal in the reference channel. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, p-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown M × p matrices at each of the M-element arrays. The noises in each channel have arbitrary spatial correlation. We derive the generalized likelihood ratio test (GLRT) statistic and show it is a monotone function of canonical correlations between the reference and surveillance channels

DVB-S based passive polarimetric ISAR – methods and experimental validation

In this work, we focus on passive polarimetric ISAR for ship target imaging using DVB-S signals of opportunity. A first goal of the research is to investigate if, within the challenging passive environment, different scattering mechanisms, belonging to distinct parts of the imaged target, can be separated in the polarimetric domain. Furthermore, a second goal is at verifying if polarimetric diversity could enable the formation of ISAR products with enhanced quality with respect to the single channel case, particularly in terms of better reconstruction of the target shape. To this purpose, a dedicated trial has been conducted along the river Rhine in Germany by means of an experimental DVB-S based system developed at Fraunhofer FHR and considering a ferry as cooperative target. To avoid inaccuracies due to data-driven motion compensation procedures and to fairly interpret the polarimetric results, we processed the data by means of a known-motion back-projection algorithm obtaining ISAR images at each polarimetric channel. Then, different approaches in the polarimetric domain have been introduced. The first one is based on the well-known Pauli Decomposition. The others can be divided in two main groups: (i) techniques aimed at separating the different backscattering mechanisms, and (ii) image domain techniques to fuse the polarimetric information in a single ISAR image with enhanced quality. The different considered techniques have been applied to several data sets with distinct bistatic geometries. The obtained results clearly demonstrate the potentialities of polarimetric diversity that could be fruitfully exploited for classification purposes

Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks

Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept

Two-Channel Passive Detection Exploiting Cyclostationarity

This paper addresses a two-channel passive detection problem exploiting cyclostationarity. Given a reference channel (RC) and a surveillance channel (SC), the goal is to detect a target echo present at the surveillance array transmitted by an illuminator of opportunity equipped with multiple antennas. Since common transmission signals are cyclostationary, we exploit this information at the detector. Specifically, we derive an asymptotic generalized likelihood ratio test (GLRT) to detect the presence of a cyclostationary signal at the SC given observations from RC and SC. This detector tests for different covariance structures. Simulation results show good performance of the proposed detector compared to competing techniques that do not exploit cyclostationarity

Passive detection of correlated subspace signals in two MIMO channels

In this paper, we consider a two-channel multiple-input multiple-output passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension, but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of the same dimension, but unknown basis, which is correlated with the subspace signal in the reference channel. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, p-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown M × p matrices at each of the M-element arrays. The noises in each channel have spatial correlation models ranging from arbitrarily correlated to independent with identical variances. We provide a unified framework to derive the generalized likelihood ratio test for these different noise models. In the most general case of arbitrary noise covariance matrices, the test statistic is a monotone function of canonical correlations between the reference and surveillance channels.I. Santamaría and J. Vía have received funding from Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U. under projects TEC2013-47141-C4-3-R (RACHEL), TEC2016-75067-C4-4-R (CARMEN) and TEC2016-81900-REDT (KERMES). The research of Haonan Wang was partially supported by NSF grant DMS-1521746

On Detection and Ranking Methods for a Distributed Radio-Frequency Sensor Network: Theory and Algorithmic Implementation

A theoretical foundation for pre-detection fusion of sensors is needed if the United States Air Force is to ever field a system of distributed and layered sensors that can detect and perform parameter estimation of complex, extended targets in difficult interference environments, without human intervention, in near real-time. This research is relevant to the United States Air Force within its layered sensing and cognitive radar/sensor initiatives. The asymmetric threat of the twenty-first century introduces stressing sensing conditions that may exceed the ability of traditional monostatic sensing systems to perform their required intelligence, surveillance and reconnaissance missions. In particular, there is growing interest within the United States Air Force to move beyond single sensor sensing systems, and instead begin fielding and leveraging distributed sensing systems to overcome the inherent challenges imposed by the modern threat space. This thesis seeks to analyze the impact of integrating target echoes in the angular domain, to determine if better detection and ranking performance is achieved through the use of a distributed sensor network. Bespoke algorithms are introduced for detection and ranking ISR missions leveraging a distributed network of radio-frequency sensors: the first set of bespoke algorithms area based upon a depth-based nonparametric detection algorithm, which is to shown to enhance the recovery of targets under lower signal-to-noise ratios than an equivalent monostatic radar system; the second set of bespoke algorithms are based upon random matrix theoretic and concentration of measure mathematics, and demonstrated to outperform the depth-based nonparametric approach. This latter approach shall be shown to be effective across a broad range of signal-to-noise ratios, both positive and negative

Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.

Ambiguity Function Analysis and Direct-Path Signal Filtering of the Digital Audio Broadcast (DAB) Waveform for Passive Coherent Location (PCL)

This research presents an ambiguity function analysis of the digital audio broadcast (DAB) waveform and one signal detection approach based on signal space projection techniques that effectively filters the direct-path signal from the receiver target channel. Currently, most Passive Coherent Location (PCL) research efforts are focused and based on frequency modulated (FM) radio broadcasts and analog television (TV) waveforms. One active area of PCL research includes the search for new waveforms of opportunity that can be exploited for PCL applications. As considered for this research, one possible waveform of opportunity is the European digital radio standard DAB. For this research, the DAB performance is analyzed for application as a PCL waveform of opportunity. For this analysis, DAB ambiguity function calculations and ambiguity surface plots are created and evaluated. Signal detection capability, to include characterization of time-delay and Doppler-shift measurement accuracy and resolution, is investigated and determined to be quite acceptable for the DAB wavefor