Target Identification Problem Research Articles

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

AbstractThis paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.

Bayesian Regression Analysis of Spatio-temporal Location Information of Ship Electromagnetic Signal

For spatio-temporal location information of electromagnetic signals according to maritime ship targets, the regression model based on Bayesian statistics is established based on the Bayesian estimation method, and some examples are analyzed. The results show that the model reduces the error, improves the accuracy of the sample data, significantly improves the data pretreatment effect, and effectively facilitates the further modeling and analysis of the target identification problem, so as to increase the credibility of the ship target identification.

Multi-frame decision fusion based on evidential association rule mining for target identification

In the multi-sensor target identification problem involving multiple frames, it is important to fuse the potential information characterizing inherent relations among frames with uncertain decision inputs for enhancing the decision-making process. However, due to the influence of environments or other interference factors, the priori knowledge that accurately represents these relations is usually hard to obtain. To overcome this difficulty, we propose a rule mining-based multi-frame decision fusion (abbreviated as RMDF) method, in which the unknown relations can be discovered from a series of historical sensor reports in the framework of belief functions. First, to accommodate data uncertainty, new measures of evidential support and confidence are defined for a constructed multi-frame evidential database, which are generalizations of the support and confidence measures in binary and probabilistic databases. Then, with these measures, an evidential association rule mining algorithm is developed to discover the relations among frames from a series of historical reports. Finally, how these mined rules are properly combined with uncertain decision information using belief function theory is explored. The key benefit of the RMDF method is that it enables modeling the uncertain relations among frames for deriving more accurate decision results. To demonstrate the feasibility and effectiveness of our proposal, an airborne target identification problem is studied under different conditions and the numerical results show that the identification performance of our method is significantly better than the traditional expert knowledge-based method where the available knowledge is inevitably incomplete or inaccurate.

Inferring individual network edges - with application to target identification in gene networks

The paper considers the problem of inferring individual network edges from time-series data. This is the problem faced in target identification, but also important in cases where it is of interest to learn whether two specific network nodes interact directly as well as in cases where there is insufficient information to infer the full network. The proposed inference method is based on taking a geometric perspective on a corresponding regression problem. We show that, by considering the span of individual node response vectors in sample space, it is possible to identify a given edge with a label of confidence even if the available data are not informative to infer other parts of the network. Furthermore, the method points to what further experiments are needed to infer edges for which the available response data are not sufficiently informative. We demonstrate the results on a target identification problem of a nonlinear 20-gene network and show that targets can be identified independently from a single time-series experiment using significantly fewer samples than the number of nodes in the network.

Biomimetic sonar and the problem of finding deterministic targets in foliage

The ability of (bio)sonars to find targets-of-interest is often hampered by a cluttered environment. For example, naval sonars encounter difficulties finding mines partially or fully buried within sand. Such situations pose target identification challenges are much harder than target detection and resolution problems. There are many bat species which navigate and hunt in dense vegetation and thus must be able to identify targets-of-interest within clutter. Evolutionary adaptation of the bat biosonar system is likely to have resulted in the “discovery” of features that support making distinctions between clutter and echoes of interest. The most well-established case is given by cf-fm bats that use Doppler shifts caused by the wingbeat of a flying insect prey to identify the prey in foliage. Other bat species have been shown to use a passive sonar approach that is based on unique prey-generated acoustic signals. Some of the most interesting cases can be found in bat species that are successful in finding preys that apparently does not emit any distinguishing sounds themselves and would hence limit the bats to an active-sonar approach. Such bat species could provide model species for new ways in which the target-identification problem in clutter can be solved with active sonar.

ISAR geometry, signal model, and image processing algorithms

Inverse synthetic aperture radar (ISAR) technique provides high-resolution images of observed moving objects. However, shadow-occluded effects and noise, which results in lack of fractions of the object geometry, often corrupt ISAR images. To facilitate the image analysis and to solve the target identification problem in most practical applications additional image-processing procedures are strongly required. The aim of the present research is based on ISAR geometry and signal modelling to develop procedures improving quality of images and extract the silhouette contour line of the ISAR image, the main geometric parameter in the target recognition process. The problem is solved in two main stages. First, ISAR geometric parameters are described and expressions for linear frequency modulated signal modelling and imaging are derived. Second, image-processing algorithms for ISAR image improving and contour line extraction are developed. All stages of image-processing and reconstructing algorithms are illustrated by results of numerical experiments.

Abstract C158: A chemoproteomics strategy for target identification and lead compound optimization using chloroalkane derivatized compounds

Abstract The Identification and validation of drug targets is an industry wide challenge. There is a very clear and urgent need for technologies that can identify the interaction partners of small molecules. Chemical proteomics is one technology that has attracted attention as a solution to the drug target identification problem. Here we present a new approach utilizing a chloroalkane (CA) moiety capture handle, which can be chemically attached to small molecules to isolate their respective protein partners. In general derivatization of small molecules with the CA moiety does not impact their cell permeability and has limited impact on potency, allowing for phenotypic assays of the derivatized compound to be performed. The retention of cell permeability also allows for the capture process to be performed from live cells treated with the CA-compound. This process is also compatible with competition assays, which can be used to evaluate and compare other compounds exhibiting a similar mode of action. Here we present a study using Dasatinib-CA, a modified kinase inhibitor and potent anti-cancer drug which binds to a broad range of kinases. First we performed target enrichment experiments by incubating K562 cells with the modified Dasatinib (Dasatinib-CA). Cells were lysed and the Dasatinib-CA, together with the bound targets, was rapidly captured onto magnetic resin coated with HaloTag. Unmodified compound was used to competitively elute putative interacting proteins. Secondly using the same assay format we evaluated the relative target affinities of Dasatinib-CA versus competing molecules. Competition assays were performed by incubating multiple mixtures of Dasatinib analogues and Dasatinib-CA at varying relative concentrations. Eluted proteins were processed using SDS-PAGE and in-gel digestion. For target identification experiments peptides were analyzed using label free mass spectrometry. For the competition assays digested material was labeled with Tandem Mass Tags (TMT) 10plex reagents. Peptides were analyzed using nanoscale LC-MS/MS coupled with a Q Exactive mass spectrometer (Thermo). Protein identification and quantitation was performed with MaxQuant (MaxQuant.org) and data validation and visualization was performed using Perseus (Perseus-framework.org). Using this approach we identified over 30 kinases, including known membrane associated and membrane protein targets. This work presented here highlights a new method for chemical proteomics and demonstrates utility of the platform to enable target identification and to evaluate competitor molecules. Citation Format: Michael Ford, Richard Jones, Ravi Amunugama, Danette Daniels, Rachel Ohana, Sergiy Levin, Thomas Kirkland, Marjeta Urh, Keith Wood. A chemoproteomics strategy for target identification and lead compound optimization using chloroalkane derivatized compounds. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C158.

Some perspectives on network modeling in therapeutic target prediction.

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

Two-stage flux balance analysis of metabolic networks for drug target identification

BackgroundEfficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.ResultsIn this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.ConclusionsOur method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.

Manipulating the Steady State of Metabolic Pathways

Metabolic pathways show the complex interactions among enzymes that transform chemical compounds. The state of a metabolic pathway can be expressed as a vector, which denotes the yield of the compounds or the flux in that pathway at a given time. The steady state is a state that remains unchanged over time. Altering the state of the metabolism is very important for many applications such as biomedicine, biofuels, food industry, and cosmetics. The goal of the enzymatic target identification problem is to identify the set of enzymes whose knockouts lead the metabolism to a state that is close to a given goal state. Given that the size of the search space is exponential in the number of enzymes, the target identification problem is very computationally intensive. We develop efficient algorithms to solve the enzymatic target identification problem in this paper. Unlike existing algorithms, our method works for a broad set of metabolic network models. We measure the effect of the knockouts of a set of enzymes as a function of the deviation of the steady state of the pathway after their knockouts from the goal state. We develop two algorithms to find the enzyme set with minimal deviation from the goal state. The first one is a traversal approach that explores possible solutions in a systematic way using a branch and bound method. The second one uses genetic algorithms to derive good solutions from a set of alternative solutions iteratively. Unlike the former one, this one can run for very large pathways. Our experiments show that our algorithms' results follow those obtained in vitro in the literature from a number of applications. They also show that the traversal method is a good approximation of the exhaustive search algorithm and it is up to 11 times faster than the exhaustive one. This algorithm runs efficiently for pathways with up to 30 enzymes. For large pathways, our genetic algorithm can find good solutions in less than 10 minutes.

Iodine scanning of a phenazine inhibitor of vacuolar sorting

Affinity reagents are often used to address the target identification problem in chemical genetics. The design of such reagents so that the linker does not occlude interactions with protein targets is an ongoing challenge. This work describes a systematic approach to synthesize derivatives of a bioactive that should avoid interference with binding to targets and be readily converted to affinity reagents.

Target identification in biological systems using network connectivity information from literature mining databases

We address the automated drug target identification problem for pharmaceutical research. It is often the case in pharmaceutical industry to bring a new promising target to clinical trials only to find that it has serious safety concerns or lack of efficacy. A gene downstream or upstream in the pathway can be a remedy, however, finding such an alternative target using existing in-silico or bench tools can be extremely labor-intensive. Recently, increasing amounts of information and observations have been compiled from different areas of biological research and deposited on databases. In this work we propose a novel computational method to quantify indirect relationships between the objects of biological research of interest by using existing relationships from text mining databases to automate the search for novel biological targets. We applied our method to analyze 9575 proteins in Ariadne database and create a rank-ordered list of proteins that are most similar to the original query. We also compared our method with the Jaccard similarity index for link prediction performance. Our method outperformed the Jaccard method in predicting the existing links for 9575 proteins in the database.

Synthesis of a Biotin-Labeled Quorum-Sensing Molecule: Towards a General Method for Target Identification

The synthesis of bacterial quorum-sensing regulator N-(3-oxohexanoyl)-l-homoserine lactone (OHHL) and biotin-tagged OHHL is reported. The latter will be applied to developing a general method to address the ‘target identification problem’ in chemical genetics.

Small-Molecule Reagents for Cellular Pull-Down Experiments

Affinity purification of interacting proteins from cellular extracts is a powerful technique for identifying the cellular targets of small molecules. Affinity matrix-based small-molecule reagents are usually prepared by conjugating small molecules of interest to a solid support such as agarose. This protocol describes an efficient and robust method to immobilize small molecules containing a primary alcohol, a common functional group in small molecules, especially in small molecules prepared using diversity-oriented synthesis. This method comprises one element of a systematic approach to the target identification problem in chemical biology.

Fusion of disparate identity estimates for shared situation awareness in a network-centric environment

Network-centric warfare (NCW) and the interoperability of joint and coalition forces are among the future warfighting concepts identified by defence. To realise the goals of interoperability and shared situation awareness for NCW, it has long been acknowledged that data fusion is a key enabling technology. Typically, however, distributed data fusion, which is relevant to NCW, and the fusion of disparate types of uncertain data, which is relevant to interoperability, have been investigated separately. Ideally, for shared situation awareness, the system should be capable of performing both aspects of data fusion. In this paper, these facets of data fusion are considered in unison for the automatic target identification problem. In particular, novel Bayesian and generalised Bayesian algorithms are formulated for fusing estimates of target identity generated by local heterogeneous data fusion systems in a network, each of which expresses target identity estimates as either finite probability distributions or Dempster–Shafer belief functions. An example drawn from the literature is used to illustrate the algorithms and their relative performances are assessed in the context of the example to identify issues of possible relevance to distributed target identification in a more general setting. (© Commonwealth of Australia 2005.)

Classifying Acoustic Signatures Using the Sequential Probability Ratio Test

Acoustic sensors can provide real time information about moving targets. The acoustic information is typically processed sequentially, allowing the sequential probability ratio test (SPRT) to be used as the basis to solve the target identification problem. The SPRT keeps gathering observations only as long as the statistical test has a value between the upper stopping boundary and the lower stopping boundary. When the test goes above the upper boundary or below the lower boundary, the system can make a decision. The desired false alarm error rate and the desired missed detection error rate determine the upper and lower stopping boundaries. We present extensions to the sequential probability ratio test to handle problems of dependence, contamination, and the unknown class. We also present results for using the SPRT for target identification using acoustic information.

Radar target identification using one-dimensional scattering centres

Identification concerning different types of radar targets can be achieved by using various radar signatures, such as one-dimensional (1-D) range profiles, 2-D radar images, and 1-D or 2-D scattering centres on a target. To solve the target identification problem, the authors utilise 1-D scattering centres, which correspond to the highest peaks in the 1-D range profile. The proposed approach obtains scale and translational-invariant features based on the central moments from the distribution of the 1-D scattering centres on the target; these 1-D scattering centres can be extracted from various techniques such as the inverse fast Fourier transform (IFFT), fast root-multiple signal classification (fast root-MUSIC), total least squares-Prony (TLS-Prony), generalised eigenvalues utilising signal subspace eigenvectors (GEESE), and the matrix-pencil (MP) algorithm. The information redundancy contained in these features, as well as their dimensions, are further reduced via the Karhunen-Loeve transform, followed by adequate scaling of the computed central moments. The resulting small dimensional and redundancy-free feature vectors are classified using the Bayes classifier. Finally, this new strategy for radar target identification is demonstrated with data measured in the compact range facility, and the above five different techniques for 1-D scattering centre extraction are compared and investigated in the context of target identification.

A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion

This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory.

A multifeature decision space approach to radar target identification

This work focuses on the use of actual radar sensor data to construct a multifeature decision space formulation to the target identification (TID) problem. The decision space concepts are classical and the basic target features used can be extracted from the data provided by standard radar sensor operating modes. The target features used in the decision process along with the construction of a useful statistical description of each feature for a given target are presented. A multidimensional formulation of the decision space and the decision logic is also presented leading to a versatile multifeature based algorithm. The algorithm performance has been evaluated on live data and the results are reported.

Application of signal subspace algorithms to scattered geometric optics and edge-diffracted signals

This paper addresses a signal processing problem arising from a high-frequency radar scattering problem. Signal subspace algorithms apply to signals which are a sum of sinusoids and are widely studied in this context. We consider the application of existing signal subspace algorithms to signals of a more general type. We analyze the accuracy of model parameters estimated and show that the signal subspace algorithms are in a certain sense robust. Thus these algorithms have potential for solving a signal processing problem arising in conventional radar target identification problems. >