Sequential Detection With Mutual Information Stopping Cost

The understanding of maneuvering forces is invaluable to the warfighter, as it enhances understanding of enemy force structure and disposition, provides cues to potential enemy actions, and expedites targeting of time critical targets. Airborne ground moving target indicator (GMTI) radars are a class of highly-effective, all-weather, wide-area senors that aid in the surveillance of these moving ground vehicles. Unfortunately conventional GMTI radars are incapable of identifying individual vehicles, and techniques for exploiting information imbedded within GMTI radar reports are limited. The Defense Advanced Research Projects Agency (DARPA) Moving Target Exploitation (MTE) program is working to mitigate these deficiencies by developing, integrating, and evaluating a suite of automated and semi-automated technologies to classify moving targets and units, and to provide indications of their activities. These techniques include: aid in the interpretation of GMTI data to provide moving force structure analysis, automatic tracking of thousands of moving ground vehicles, 1-D target classification based upon high-range- resolution (HRR) radar profiles, and 2-D target classification based upon moving target imaging (MTIm) synthetic aperture radar (SAR). This paper shall present the MTE concept and motivation and provide an overview of results to date.

Utilizing digital beam forming (DBF) on transmit and receive, the multiple-input multiple-output (MIMO) radar can be applied to ground moving target indication (GMTI) and can provide an efficient way to improve the GMTI performance. The operating pattern is very significant for the MIMO radar GMTI. For the traditional GMTI pattern, there is a conflict of the wider swath and narrower clutter bandwidth. The observation area can be broadened with MIMO techniques by scanning. This paper designs an effective pattern with the simultaneous transmission of multi-phase-centre multi-sub-band signals as well as beam scanning in each pulse, which provide an efficient way to settle the conflict. By beam scanning in pulse, the new pattern has wider azimuth swath, shorter revisiting period, larger coherent processing interval (CPI) number and higher SCNR. By MIMO technology, more equivalent phase centres and longer efficient baselines can be formed, so the performance of GMTI can be improved. The principle of space-time-frequency adaptive processing (STFAP) is analysed, simulation results are presented to prove the superiority of MIMO radar in clutter suppression and GMTI. Analysis of the results indicates improved detection performance with the new pattern compared to the conventional approach.

Multidimensional waveform encoding is a new multiple-input multiple-output (MIMO) technique for remote sensing. The orthogonality is acquired by digital beamforming in elevation. It has a potential to enhance ground moving target indication (GMTI) performance due to the larger along-track virtual array of the radar system. This paper proposed two modes for GMTI based on multidimensional waveform encoding: intrapulse azimuth beam scanning and intrapulse azimuth encoding. Compared to the phased-array GMTI systems, the mode with intrapulse azimuth beam scanning has improved area search rates, and the mode with intrapulse azimuth encoding has improved minimal detectable velocity performance. Theoretical analysis and simulation results demonstrate that the proposed modes have better performance than the phased-array GMTI systems.

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

Ground moving target indicator (GMTI) RADAR system frequently incorporates space-time adaptive processing for successful moving targets discrimination in a highly cluttered environment. The minimum detectable velocity (MDV) of the target gets affected due to the added clutter impairments such as foliage penetration and angle independent channel mismatch. A novel clutter model is postulated in this work that includes assessment enacted in view of both angle and Doppler clutter impairments at a time instead of their individual analysis as in the reported literature. The performance of the GMTI RADAR in terms of the signal-to-interference-plus-noise ratio loss is evaluated using the extrusive beamforming methods namely sample matrix inversion and principal component canceller. In high decorrelated clutter environment, the later outperforms in terms of the MDV, making use of a lesser number of training snapshots. The approach is validated with mathematical modelling and numerical simulations.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data.

We show how a single reflector antenna with a multimode feed horn can be used in a ground moving target indication (GMTI) radar. In particular, we demonstrate the simultaneous detection and estimation of angular location of a ground moving target via adaptive cancellation of ground clutter

The KASSPER project is a Defense Advanced Research Projects Agency (DARPA) program which has the goal of improving the performance of ground moving target indicator (GMTI) radar systems by incorporating external sources of knowledge into the signal processing chain. The KASSPER real-time signal processing architecture is a radar system scheduling and signal processing framework that is being developed at Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). This paper discusses the design of the architecture, knowledge handling issues. resource scheduling issues, the current state of the prototype implementation of the framework, and the current state of the project's real-time processor testbed.

The rapid development of Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) is threatening ground moving targets. The key against it is the efficient jamming technology. This paper introduces SAR-GMTI at first. Then, RF noise jamming, noise AM jamming, noise FM jamming and noise convolution jamming are also introduced and analyzed. Finally this paper illustrates the jamming effect by computer simulation which contains velocity estimation and detection probability. The results show that under the proper JSR condition, the active noise jamming can jam SAR-GMTI effectively, and under the same JSR condition, noise convolution jamming has a more efficient jamming effect than those non-coherent jamming obviously.

Nous proposons une methode « operationnelle » pour le probleme de tests d’hypotheses composites convexes lorsque les distributions sous-jacentes possedent des queues lourdes. Elle s’appuie sur la separation euclidienne des ensembles convexes et peut etre vue comme une extension de la methode developpee dans (Electron. J. Stat. 9 (2015) 1645–1712 ; Electron. J. Stat. 10 (2016) 2204–2242) pour l’etude des tests d’hypotheses reposant sur des techniques d’optimisation convexe. En particulier, nous montrons comment construire des tests quasi-optimaux pour des familles de distributions qui sont majorees, dans un sens precis, par une distribution symetrique quasi-spherique et etudions la relation entre les tests bases sur la separation euclidienne et les tests utilisant des potentiels. Nous appliquons la methodologie proposee au probleme de la detection sequentielle et decrivons sa mise en oeuvre pour la detection sequentielle de ruptures dans l’entree d’un systeme dynamique.

This paper considers a statistical signal processing problem involving agent-based models of financial markets, which at a microlevel are driven by socially aware and risk-averse agents. These agents trade (buy or sell) stocks at each trading instant by using the decisions of all previous agents (social learning) in addition to a private (noisy) signal they receive on the value of the stock. We are interested in the following: 1) modelling the dynamics of these risk averse agents and 2) sequential detection of a market shock based on the behaviour of these agents. Structural results that characterize social learning under a risk measure, conditional value-at-risk (CVaR), are presented and formulation of the Bayesian change point detection problem is provided. The structural results exhibit two interesting properties: 1) risk averse agents herd more often than risk neutral agents and 2) the stopping set in the sequential detection problem is nonconvex. The framework is validated on data from the Yahoo! Tech Buzz game dataset and it is revealed that 1) the model identifies the value changes based on agent’s trading decisions. 2) Reasonable quickest detection performance is achieved when the agents are risk-averse.

Registration of two dimensional to three dimensional orthopaedic medical image data has important applications particularly in the area of image guided surgery and sports medicine. Fluoroscopy to computer tomography (CT) registration is an important case, wherein digitally reconstructed radiographs derived from the CT data are registered to the fluoroscopy data. Traditional registration metrics such as intensity-based mutual information (MI) typically work well but often suffer from gross misregistration errors when the image to be registered contains a partial view of the anatomy visible in the target image. Phase-based MI provides a robust alternative similarity measure which, in addition to possessing the general robustness and noise immunity that MI provides, also employs local phase information in the registration process which makes it less susceptible to the aforementioned errors. In this paper, we propose using the complex wavelet transform for computing image phase information and incorporating that into a phase-based MI measure for image registration. Tests on a CT volume and 6 fluoroscopy images of the knee are presented. The femur and the tibia in the CT volume were individually registered to the fluoroscopy images using intensity-based MI, gradient-based MI and phase-based MI. Errors in the coordinates of fiducials present in the bone structures were used to assess the accuracy of the different registration schemes. Quantitative results demonstrate that the performance of intensity-based MI was the worst. Gradient-based MI performed slightly better, while phase-based MI results were the best consistently producing the lowest errors.

An exploration of mutual information based on emotion–cause pair extraction

Various parameters can affect the ground moving target indicator (GMTI) radar performance. One such parameter that may often be overlooked is that of the unintended polarization of the antenna, e.g., the cross-polarization response. This paper discusses the issue of cross-polarization on clutter attenuation performance for GMTI radars.

The problem of joint sequential change detection and isolation in a multichannel system is considered. It is assumed that a disruption occurs at some unknown time, and changes the distributions of the observations in an unknown subset of channels. The problem is to quickly detect the change, and at the same time to reliably isolate the affected channels. A novel scheme is proposed for this task, which admits a recursive structure, is scalable with respect to the number of channels, and does not require any prior information about the change-point. Its performance is analyzed in the special case that the number of affected channels is known. Specifically, explicit critical values are obtained for the control of the false alarm rate and the conditional probability of wrong isolation below arbitrary levels to be prescribed by the practitioner. Finally, the asymptotic optimality of the average detection delay of the proposed scheme is established as the error probabilities go to 0 and the effect of the prior distribution for the change point vanishes in the limit.