Some consequences of the commercialization of satellite remote sensing

Remote-sensing technology developments will influence the flow and requirements of remote-sensing data until the end of this century. Sensing-upon-request modes of operation are becoming regular practice and, in combination with commercial sales, are leading to contracts in which copyright clauses and other conditions influence the distribution of remote-sensing data. Moreover, the introduction of programmable and directable sensor systems is making it easier to gather more data over certain areas at the cost of less or none over others, leading to potential indirect censorship. The development of a media market for remote-sensing products also brings a threat of actual censorship on the release and use of imagery. The effects of the technology developments and commercialization policies require a careful consideration of whether or not the present international remote-sensing regime, as governed by the UN principles relating to remote sensing of the Earth from space, can continue unmodified in future.     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.     

Compact frequency standard using atoms trapped on a chip

We present a compact atomic frequency standard based on the interrogation of magnetically trapped ⁸⁷Rb atoms. Two photons, in the microwave and radiofrequency domain excite the atomic transition. At a magnetic field of 3.23 G this transition from ∣F = 1, m_F = −1〉 to ∣F = 2, m_F = 1〉 is 1st order insensitive to magnetic field variations. Long Ramsey interrogation times can thus be achieved, leading to a projected stability in the low 10⁻¹³ at 1 s. This makes this device a viable alternative to LITE and HORACE as a good candidate for replacing or complementing the rubidium frequency standards and passive hydrogen masers already on board of the GPS, GLONASS, and GALILEO satellites. Here we present preliminary results. We use an atom chip to cool and trap the atoms. A coplanar waveguide is integrated to the chip to carry the Ramsey interrogation signal, making the physics package potentially as small as (5 cm)³. We describe the experimental apparatus and show preliminary Ramsey fringes of 1.25 Hz linewidth. We also show a preliminary frequency stability σ_y = 1.5 × 10⁻¹²τ^−1/2 for 10 < τ < 10³ s. This represents one order of magnitude improvement with respect to previous experiments.     

A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.     

The deontology of the conquest of space / de la deontologie de la conquete de l''espace: Proceedings from a conference, Casablanca, 1–4 march 1984 Academy of the Kingdom of Morocco, Rabat, Morocco, 1984, 178 pages

Professor Caesar Voûte reports on the 34th Pugwash Conference on Science and World Affairs, Björkliden, Sweden, 9–14 July 1984: ‘1984 and Beyond: Science, Security and Public Opinion’.     

The future of earth observations in Central and Eastern Europe

The Central and Eastern Europe are characterized by a high theoretical and scientific level in space research and technology, including remote sensing, but inexperience in practical applications. The emerging private sector lacks management experience and needs appropriate training. Other constraints are inadequate administrative structures, a lack of coordination, and an outdated technology in general. In his report on the results of a study carried out for the European Commission, consultant and emeritus professor of the International Institute for Aerospace Survey and Earth Sciences, Caesar Voûte, outlines the new forms of European and regional cooperation being promoted. Agriculture, yield prediction and agricultural statistics, land use studies, environmental monitoring and environmental management are identified as future high priority operational applications of remote sensing and geographical information systems (GIS). Hazard management and marine observations also need to be developed.     

Space focus at Pugwash

Professor Caesar Voûte reports on the 34th Pugwash Conference on Science and World Affairs, Björkliden, Sweden, 9–14 July 1984: ‘1984 and Beyond: Science, Security and Public Opinion’.     

Novel data association schemes for the probability hypothesis density filter

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.     

Sequential Monte Carlo methods for multitarget filtering with random finite sets

Random finite sets (RFSs) are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for data fusion. Although the foundation has been established in the form of finite set statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multitarget filtering is not yet practical due to the inherent computational hurdle. Even the probability hypothesis density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multitarget posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multitarget filter. In addition, an SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving nonlinear nonGaussian dynamics. Convergence results for these filters are also established.