Detection of long-duration narrowband processes

Detecting long, weak signals that are narrowband but of unknown frequency structure is an important signal processing challenge, with many applications in remote sensing and process monitoring. An ad hoc scheme is developed. Its stages include the discrete Fourier transform (DFT), a multiresolution decomposition in the frequency domain, and a generalized likelihood ratio test (GLRT). The computational load is light, and the performance is remarkably good. This is so not just in the original narrowband situation, but also, due to an inherent adaptivity to the data, in the detection of signals that are relatively broadband in nature. Generalizations are given to constant false alarm rate (CFAR) operation in both prewhitened and unwhitened cases, and to the detection of multiband signals. As regards the last, it is discovered that there is little loss from overestimating the number of bands     

PMHT: problems and some solutions

The probabilistic multihypothesis tracker (PMHT) is a target tracking algorithm of considerable theoretical elegance. In practice, its performance turns out to be at best similar to that of the probabilistic data association filter (PDAF); and since the implementation of the PDAF is less intense numerically the PMHT has been having a hard time finding acceptance. The PMHT's problems of nonadaptivity, narcissism, and over-hospitality to clutter are elicited in this work. The PMHT's main selling-point is its flexible and easily modifiable model, which we use to develop the "homothetic" PMHT; maneuver-based PMHTs, including those with separate and joint homothetic measurement models; a modified PMHT whose measurement/target association model is more similar to that of the PDAF; and PMHTs with eccentric and/or estimated measurement models. Ideally, "bottom line" would be a version of the PMHT with clear advantages over existing trackers. If the goal is of an accurate (in terms of mean square error (MSE)) track, then there are a number of versions for which this is available.     

跨音速三维多升力面气动设计

发展了一种跨音速多升力面的气动设计方法和设计程序,它基于已成功应用于亚、跨音速机翼设计和亚音速双翼面设计的"余量修正迭代"概念.当升力面上出现超音速区和激波时自动引用迎风格式对控制方程进行修正.开发了一系列接口程序,包括目标压力设计程序.由此气动设计程羊、TAU程序以及相应的接口程序建立了跨音速多升力面气动反设计软件系统.用两个鸭翼-机翼构型验证了设计方法和设计程序,结果表明在高跨音速下设计迭代有很好的收敛性.     

Tracking with distributed sets of proximity sensors using geometric invariants

We propose a new approach to forming an estimate of a target track in a distributed sensor system using very limited sensor information. This approach uses a central fusion system that collects only the peak energy information from each sensor and assumes that the energy attenuates as a power law in range from the source. A geometrical invariance property of the proximity of the distributed sensors relative to a target track is used to generate potential target track paths. Numerical simulation examples are presented to illustrate the practicality of the technique.     

Modelling and design of wing tip devices at various flight conditions using a databased aerodynamic prediction tool

A design study of wing tip devices at high and low speeds is described. The basis of the design study is an equivalent drag approach containing both aerodynamic drag gain and structural weight penalty. A comprehensive parameter study is carried out using a rapid aerodynamic prediction tool named Lift and Drag Component Analysis (LIDCA). Adding to an available lifting-line method a databased module for airfoil data is employed that uses results of two-dimensional flow simulations by multidimensional interpolation. Detailed validation studies of the method at high lift and high speed have demonstrated good accuracy. RANS computations of the selected wing tip designs confirm the predicted benefits at cruise condition. The results of the most effective wing tip designs are analysed at both flight conditions. Finally, options for improving the performance at take-off are suggested.     

Bearings-only target motion analysis with acoustic propagation models of uncertain fidelity

The augmented bearings-only target motion analysis (TMA) problem arises when the bearing measurements of the classical bearings-only TMA problem are augmented with received signal-to-noise ratio (SNR) measurements. A combined acoustic propagation and sensor (CAPS) performance prediction model specifying the conditional density of the SNR measurements is assumed given; however, mismatch may exist between the CAPS model and the real world. We present a novel "missing data" formulation of the augmented bearings-only TMA problem using an empirical maximum a posteriori (EMAP) method for target parameter estimation, and show that it provides a natural and straightforward technique for mitigating CAPS model mismatch. The EMAP approach leads to an iteratively reweighted, linear least-squares algorithm for solving both the augmented bearings-only TMA problem and the classical (nonaugmented) bearings-only TMA problem. Examples are provided.