A New Gaussian Mixture Algorithm for GMTI Tracking Under a Minimum Detectable Velocity Constraint

Published versio

An enhanced particle filtering method for GMTI radar tracking

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e. falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise related doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem

Searching for periodic sources with LIGO. II: Hierarchical searches

The detection of quasi-periodic sources of gravitational waves requires the accumulation of signal-to-noise over long observation times. If not removed, Earth-motion induced Doppler modulations, and intrinsic variations of the gravitational-wave frequency make the signals impossible to detect. These effects can be corrected (removed) using a parameterized model for the frequency evolution. We compute the number of independent corrections $N_p(\Delta T,N)$ required for incoherent search strategies which use stacked power spectra---a demodulated time series is divided into $N$ segments of length $\Delta T$, each segment is FFTed, the power is computed, and the $N$ spectra are summed up. We estimate that the sensitivity of an all-sky search that uses incoherent stacks is a factor of 2--4 better than would be achieved using coherent Fourier transforms; incoherent methods are computationally efficient at exploring large parameter spaces. A two-stage hierarchical search which yields another 20--60% improvement in sensitivity in all-sky searches for old (>= 1000 yr) slow (= 40 yr) fast (<= 1000 Hz) pulsars. Assuming 10^{12} flops of effective computing power for data analysis, enhanced LIGO interferometers should be sensitive to: (i) Galactic core pulsars with gravitational ellipticities of \epsilon\agt5\times 10^{-6} at 200 Hz, (ii) Gravitational waves emitted by the unstable r-modes of newborn neutron stars out to distances of ~8 Mpc, and (iii) neutron stars in LMXB's with x-ray fluxes which exceed $2 \times 10^{-8} erg/(cm^2 s)$. Moreover, gravitational waves from the neutron star in Sco X-1 should be detectable is the interferometer is operated in a signal-recycled, narrow-band configuration.Comment: 22 Pages, 13 Figure

Long-term variability of CO2 and O in the Mars upper atmosphere from MRO radio science data

We estimate the annual variability of CO2 and O partial density using approximately 6years of Mars Reconnaissance Orbiter (MRO) radio science data from August 2006 to January 2012, which cover three full Martian years (from the northern hemisphere summer of 28 to the northern hemisphere summer of 31). These two elements are the dominant species at the MRO periapsis altitude, constituting about 70-80% of the total density. We report the recovered annual cycle of CO2 and the annual and seasonal cycle of O in the upper atmosphere. Although no other observations are available at those altitudes, our results are in good agreement with the density measurements of the Mars Express Spectroscopy for Investigation of Characteristics of the Atmosphere of Mars, which uses stellar occultations between 60 and 130km to determine the CO2 variability, and with the Mars Global Reference Atmospheric Model 2010 for the O annual and seasonal variabilities. Furthermore, the updated model provides more reasonable MRO drag coefficients (CD), which are estimated to absorb mismodeling in the atmospheric density prediction. The higher content of dust in the atmosphere due to dust storms increases the density, so the CDs should compensate for this effect. The correlation between the drag coefficient and the dust optical depth, measured by the Mars Odyssey Thermal Emission Imaging System (THEMIS) instrument, increases from 0.4 to 0.8 with the a priori and adjusted models, respectively. The trend of CDs not only confirms a substantial improvement in the prediction of the atmospheric density with the updated model but also provides useful information for local dust storms, near MRO periapsis, that cannot be measured by the opacity level since THEMIS does not always sample the southern hemisphere evenly

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

An enhanced particle filtering method for GMTI radar tracking

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e. falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise related doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem

Active microwave sensing of the atmosphere, chapter 4

The use of active microwave systems to study atmospheric phenomena is studied. Atmospheric pollution, weather prediction, climate and weather modification, weather danger and disaster warning, and atmospheric processes and interactions are covered