Dynamic Cramer-Rao bound for target tracking in clutter

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.     

Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.     

机器人自动制孔中绝对定位误差的分析与补偿

由于机器人绝对定位精度相对较低,无法直接满足自动制孔的孔位精度要求。为了提高机器人自动制孔的孔位精度,对机器人绝对定位误差进行了研究。首先,阐述了绝对定位误差的来源和产生过程,并通过理论分析和相关试验,证明了绝对定位误差会对机器人基坐标系的平移分量和姿态变换分量产生不同程度的影响。然后,为了补偿由于基坐标系标定不准确所引起的坐标转换误差,从飞机曲面构造原理角度,提出了一种基于误差Coons曲面函数的补偿方法。制孔试验表明,采用基于误差Coons曲面函数的补偿方法,可以使得坐标转换误差得到有效的补偿。机器人自动制孔的孔位平均位置误差为0.205mm,最大位置误差为0.343mm,满足孔位精度在0.5mm以内的要求,实现了机器人自动化精确制孔。     

Global node selection for localization in a distributed sensor network

This work considers the problem of selecting the best nodes for localizing (in the mean squared (MS) position error sense) a target in a distributed wireless sensor network. Each node consists of an array of sensors that are able to estimate the direction of arrival (DOA) to a target. Different computationally efficient node selection approaches that use global network knowledge are introduced. Performance bounds based on the node/target geometry are derived, and these bounds help to determine the necessary communication reach of the active nodes. The resulting geolocation performance and energy usage, based on communication distance, is evaluated for a decentralized extended Kalman filter (EKF) that is exploiting the different selection approaches.     

Local node selection for localization in a distributed sensor network

This paper discusses a new localized resource manager for a wireless sensor network of bearings-only sensors. Specifically, each node uses knowledge of the target under surveillance to determine whether it should actively collect measurements and how far to disseminate the data in order for the sensor network to maintain track of the target. At each node, the resource manager requires only knowledge of the relative location to the target for itself and the active nodes from the previous snapshot. The decentralized strategy represents a modification to the global node selection (GNS) method that exploits knowledge of the location of all nodes in the network. Simulations show that despite the lack of global network knowledge, the new localized management method is almost as effective as GNS in terms of balancing the tradeoff between energy usage and localization accuracy.     

美国展开未来航空科技领先地位保卫战

自二战结束以来,美国在世界航空技术领域一直是无可争议的领先者. 然而,美国在世界航空技术领域的霸主地位正在受到来自联合起来的欧洲的挑战.欧盟国家为与美国航空工业巨头进行抗衡已抛开欧洲各国间存在的贸易壁垒和保护机制,正联合起来探索新的航空技术和解决方案.     

相控阵雷达搜索方式下波位编排与优化

分析不同波位编排方式对相控阵雷达能量需求与搜索性能的影响,给出了搜索方式下较为优化的波位编排方式。然后在分析相控阵雷达波束展宽效应的基础上,提出了非均匀划分扫描空域并对波束展宽效应进行补偿的方法,对波位编排方式进一步优化。最后利用该方法仿真分析了波束宽度与天线增益的变化,结果表明可有效补偿波束展宽效应带来的影响。     

Probability of error for BPSK modulation from electrostaticinterference [in orbital situation]

In an orbital situation any spacecraft is bombarded by charged particles that span a broad range of energies. Some of the particles may be trapped within the materials of the spacecraft. Recent data indicates that an S-band phased-array antenna on a spacecraft may experience electrostatic discharges from such ion deposition at the elements of the array. These discharges may induce RF interference of sufficient magnitude to degrade transponder performance. This issue is addressed by considering the degradation to a binary phase shift keying (BPSK) signal. A simple model for the electrostatic phenomenon is described, and error probabilities are evaluated. The discharges and induced pulse rates are shown to be well below the interference level     

A practical filter error method for aerodynamic parameter estimation of aircraft in turbulence

It is common for aircraft to encounter atmospheric turbulence in flight tests. Turbulence is usually modeled as stochastic process noise in the flight dynamics equations. In this paper, parameter estimation of nonlinear dynamic system with both process and measurement noise was studied, and a practical filter error method was proposed. The linearized Kalman filter of first-order approximation was used for state estimation, in which the filter gain, along with the system parameters and the initial states, constituted the parameter vector to be estimated. The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method, and the sensitivities of observations to unknown parameters were calculated by finite difference approximation. Some practical aspects of the method application were discussed. The proposed filter error method was validated by the flight simulation data of a research aircraft. Then, the method was applied to the flight tests of a subscale aircraft, and the aerodynamic stability and control derivatives were estimated. All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach. It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.     

Systematic centroid error compensation for the simple Gaussian PSF in an electronic star map simulator

Simulated star maps serve as convenient inputs for the test of a star sensor, whose standardability mostly depends on the centroid precision of the simulated star image, so it is necessary to accomplish systematic error compensation for the simple Gaussian PSF（or SPSF, in which PSF denotes point spread function）. Firstly, the error mechanism of the SPSF is described, the reason of centroid deviations of the simulated star images based on SPSF lies in the unreasonable sampling positions（the centers of the covered pixels） of the Gaussian probability density function. Then in reference to the IPSF simulated star image spots regarded as ideal ones, and by means of normalization and numerical fitting, the pixel center offset function expressions are got, so the systematic centroid error compensation can be executed simply by substituting the pixel central position with the offset position in the SPSF. Finally, the centroid precision tests are conducted for the three big error cases of Gaussian radius r = 0.5, 0.6, 0.671 pixel, and the centroid accuracy with the compensated SPSF（when r = 0.5） is improved to 2.83 times that of the primitive SPSF, reaching a 0.008 pixel error, an equivalent level of the IPSF. Besides its simplicity, the compensated SPSF further increases both the shape similarity and the centroid precision of simulated star images, which helps to improve the image quality and the standardability of the outputs of an electronic star map simulator（ESS）.