A tunable adaptive detector for distributed targets when signal mismatch occurs

Aiming at the problem of detecting a distributed target when signal mismatch occurs,this paper proposes a tunable detector parameterized by an adjustable parameter.By adjusting the parameter,the tunable detector can achieve robust or selective detection of mismatched signals.Moreover,the proposed tunable detector,with a proper tunable parameter,can provide higher detection probability compared with existing detectors in the case of no signal mismatch.In addition,the proposed tunable detector possesses the constant false alarm rate property with the unknown noise covariance matrix.

RFD-Rao and RFD-Wald tests for distributed targets with range walking effect

Two novel adaptive distributed target detectors, the range frequency domain-Rao （RFD-Rao） and range frequency domain-Wald （RFD-Wald） tests are proposed in this work. The application methods for these tests consider a partially homogeneous disturbance environment and a target range walking effect in a coherent processing interval （CPI）. The asymptotic performance of the detectors is analyzed, and the constant false alarm rate （CFAR） properties with respect to the clutter covariance matrix and power level are shown. The performances of the proposed adaptive detectors are assessed through Monte-Carlo simulations, and the results are presented to demonstrate the effectiveness of the proposed detection algorithms compared to those of similar existing detectors.

Improved AR-Based GLRT Detector for Range-Spread Targets in Compound-Gaussian Clutter without Secondary Data

The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of training data,an autoregressive（AR）-process-based covariance matrix estimator is proposed.Then,with the estimated covariance matrix the one-step generalized likelihood ratio test（GLRT） detector is designed without training data.Finally,detection performance of our proposed detector is assessed.

Processing Methods for Conical Scan Hopped-Frequency Signal

The new radar system of combination of the hopped-frequency with the conical scan is presented. According to the principle and expression of the conical scan hopped-frequency signal, the angle processing method in which angle information is obtained by taking discrete Fourier transform （DFT） for the conical envelop of each scattering centre by means of high range resolution profile （HRRP） is presented, and the corresponding formula is derived. The influence of non-ideal factors, such as amplitude fluctuation noise and system noise, leakage of time and frequency domain, unstable rotation of antenna, and missile rotation, on angle-measurement precision and the possible solving methods are also focused on. The simulation results show that the combination of the hopped-frequency and conical scan system could get satisfactory angle information, which could ensure good quality when used in practical tracking radar.

Target distribution in cooperative combat based on Bayesian optimization algorithm

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

Design of high-performance energy integrator detector for wideband radar

Target detection for wideband radar has recently received extensive attention. The classical energy integrating(EI)detector will always accumulate excess clutter or noise energy,which leads to unacceptable performance deterioration if the detection window is not selected properly. In this paper, an EI detector for the distributed targets in the Gaussian environment is proposed.First, at the stage of preparatory work, the target models are proposed, then, the problem formulation is introduced. Subsequently,in the aspect of optimizing the method of detection window search and the method of threshold setting, the detailed design stages of the proposed detector are provided. Furthermore, theoretical analyses show that the proposed detector is easy to hardware implementation, and it does not need the prior knowledge about the spatial distribution of the target scattering centers in practical radar detection application. Finally, the performance assessment conducted by Monte Carlo simulations verifies that the proposed detector outperforms the conventional detectors.

基于无线传感器网络的分散目标跟踪:实际测试平台的开发应用(英文)

In the real world,centralized tracking in a large-scale wireless sensor network (WSN) may not be feasible due to the possible failure of fusion centre and the large communication delay in forwarding measurement data to the fusion centre. Distributed target tracking techniques can be employed by tasking sensor nodes near to the target to perform sensing,target state estimation and selection of future tasking sensor nodes. In this paper,the development and implementation of a prototype ultrasonic WSN test-bed to demonstrate distributed target tracking using the Extended Kalman Filter (EKF) algorithm is described. In the test-bed,a mobile robot is used to simulate the moving target,and static/mobile sensor nodes are deployed to detect and track the target. The sensor nodes and robots are equipped with sonar and MICAZ to receive and process instructions. Experimental evaluation of a number of sensor scheduling schemes are reported which shows the superior tracking performance of our distributed competition based sensor scheduling scheme.

Underwater target feature recognition based on distribution of highlights

An algorithm for underwater target feature recognition is proposed using its highlights distribution.For an underwater target with large size and slender body,it is assumed that the heading course and the length of the target are both determined by the distribution of its highlights.By supposing that these highlights obey Gaussian mixture distribution,the feature recognition problem can be transformed into a clustering problem.Therefore,using the collinearly constrained expectation maximization algorithm,the clustering centers of these highlights can be calculated and then the estimation of the heading and length of the target can be obtained with high accuracy.The effectiveness of the proposed method is demonstrated using simulations.

A study on underwater target 2-dimensional highlight distribution

The near field of underwater target acoustic scattering is a time-space distribution.Its characteristics can be illustrated by target time(equivalent to position)-aspect 2-dimensional highlight distribution. The principle that based on acoustic channel theory is reported here.Conjoining with sea trial results, some main reasons of target scattering are reviewed. The graphs for the target 2-dimensional highlight distribution are also discussed. Finally, the physical model of target 2-dimensional highlight distribution is presented.

Super-resolution microscopy reveals the distribution of targeted molecules in the complex matrix

With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Li Junbai(李峻柏)at the CAS Key Lab of Colloid,Interface and Thermodynamics,Institute of Chemistry,Chinese Academy of Sciences,revealed the distribution of proteins in the transformation of inorganic/protein hybrid crystals by super-resolution microscopy,which