Gabor Filter Approach to Joint Feature Extraction and Target Recognition

This paper presents a new approach of improving automatic target recognition (ATR) performance by tuning adaptively the Gabor filter. The Gabor filter adopts the network structure of two layers, and its input layer constitutes the adaptive nonlinear feature extraction part, whereas the weights between output layer and input layer constitute the linear classifier. From the statistic property of high-resolution range profile (HRRP), its extracted nonstationarity degree of features is tracked to extract the discriminative features of Gabor atoms. Two experimental examples show that the Gabor filter approach with simple structure has higher recognition rate in radar target recognition from HRRP as compared with several existing methods.     

Performance Compparison of Optimum and Conventional MTI and Doppler Processors

The performance of an optimum radar signal processor and more conventional techniques (such as MTI, adaptive MTI, and cqherent integration) are compared. A mathematical method is suggested and applied to several cases of practical interest. A number of operative conditions are discovered in which the conventional processing techniques give very poor performance and the optimum radar processor becomes necessary.     

Design of a practical tracking algorithm with radar measurements

The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem     

A mean level adaptive detector using nonconcurrent data

Convergence results for a mean level adaptive detector (MLAD) are presented. The MLAD consists of an adaptive matched filter (for spatially correlated inputs) followed by a mean level detector (MLD). The optimal weights of the adaptive matched filter are estimated from one batch of data and applied to a statistically independent batch of nonconcurrent data. The threshold of the MLD is determined from the resultant data. Thereafter a candidate cell is compared against this threshold. Probabilities of false alarm and detection are derived as a function of the threshold factor, the order of the matched filter, the number of independent samples per channel used to calculate the adaptive matched filter weights, the number of samples used to set the MLD threshold, and the output signal-to-noise power ratio of the optimal matched filter. A number of performance curves are shown and discussed     

应用卡尔曼滤波的机载雷达跟踪系统

本文论述将滤波理论应用于机载雷达中对单个目标进行距离、速度、方位角和高低角跟踪的多环反馈系统。首先根据目标和天线的相对运动建立控制四坐标跟踪环所需的状态矢量微分方程,然后推导相应的非线性滤波算法。最后给出计算机的模拟结果。计算机模拟的结果清晰地说明采用最佳滤波的系统性能比通常的有很大改善,并且这种瞄准轴坐标系的最佳系统对目标的随机机动是不灵敏的。 本文所讨论的方法和得出的结论可以延用到地面雷达、舰载雷达以及其他有源和无源的跟踪系统。     

Adaptive Clutter Filtering Using Autogressive Spectral Estimation

A new adaptive filter to reject clutter is derived using autoregressive spectral analysis techniques. The adaptive filter performs open. Ioop processing, resulting in a shorter transient response, and is therefore suitable for radar waveforms containing only a small number of samples. A number of examples including application to ballistic missile defense are presented to demonstrate the performance capabilities of the new adaptive filter.     

Efficient robust AMF using the FRACTA algorithm

The FRACTA algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists nonhomogeneous clutter, jamming, and dense target clusters. Further developments of the FRACTA algorithm are presented here in which the focus is on the robust, efficient implementation of the FRACTA algorithm. Enhancements to the FRACTA algorithm include a censoring stopping mechanism, an alternative data blocking approach for adaptive power residue (APR) censoring, and a fast reiterative censoring (RC) procedure. Furthermore, a coherent processing interval (CPI) segmentation scheme for computing the adaptive weights is presented as an alternative approach to computing the adaptive matched filter (AMF) weight vector that allows for lower sample support and reduced computational complexity. The enhanced FRACTA algorithm, denoted as FRACTA.E, is applied to the KASSPER I challenge datacube which possesses dense ground target clusters that are known to have a significant deleterious effect on standard adaptive matched filtering (AMF) processors. It is shown that the FRACTA.E algorithm outperforms and is considerably more computationally efficient than both the original FRACTA algorithm and the standard sliding window processing (SWP) approach. Furthermore, using the KASSPER I datacube, the FRACTA.E algorithm is shown to have the same detection performance as the clairvoyant algorithm where the exact range-dependent clutter covariance matrices are known.     

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.     

NEW METHOD FOR REDUCED RANK STAP—NON CLUTTER CHANNEL METHOD

Space- time adaptive processing(STAP) is aleading technology candidate for improving detec-tion performance of advanced airborne early warn-ing radar.In practical radar systems,the optimumfully adaptive space- time processing[1] cannot al-ways be implemented because of the computationalcomplexity,so the design of suboptimum proces-sors has been one of the key topics in STAP.Sev-eral reduced- rank STAP methods have been pro-posed in recent years.For example,based on thegeneralized sidelobe…     

Design and analysis of a knowledge-aided radar detector for doppler processing

In this paper we discuss the combined use of a priori information and adaptive signal processing techniques for the design and the analysis of a knowledge-aided (KA) radar receiver for Doppler processing. To this end, resorting to the generalized likelihood function (GLF) criterion (both one-step and two-step), we design and assess data-adaptive procedures for the selection of training data. Then we introduce a KA radar detector composed of three elements: a geographic-map-based data selector, which exploits some a priori information concerning the topography of the observed scene, a data-adaptive training selector which removes dynamic outliers from the training data, and an adaptive radar detector which performs the final decision about the target presence. The performance of the KA algorithm is analyzed both on simulated as well as on real radar data collected by the McMaster University IPIX radar. The results show that the new KA system achieves a satisfactory performance level and can outperform some previously proposed adaptive detection schemes     

Multistatic adaptive pulse compression

A new technique denoted as multistatic adaptive pulse compression (MAPC) is introduced which exploits recent work on adaptive pulse compression (APC) in order to jointly separate and pulse compress the concurrently received return signals from K proximate multistatic radars operating (i.e., transmitting) within the same spectrum. For the return signal from a single pulse of a monostatic radar, APC estimates the particular receive filter for a given range cell in a Bayesian sense reiteratively by employing the matched filter estimates of the surrounding range cell values as a priori knowledge in order to place temporal (i.e., range) nulls at the relative ranges occupied by large targets and thereby suppress range sidelobes to the level of the noise. The MAPC approach generalizes the APC concept by jointly estimating the particular receive filter for each range cell associated with each of several concurrently-received radar return signals occupying the same spectrum. As such, MAPC is found to enable shared-spectrum multistatic operation and is shown to yield substantial performance improvement in the presence of multiple spectrum-sharing radars as compared with both standard matched filters and standard least-squares mismatched filters     

Doppler Compensation & Single Pulse Imaging using Adaptive Pulse Compression

The effects of target Doppler are addressed in relation to adaptive receive processing for radar pulse compression. To correct for Doppler-induced filter mismatch over a single pulse, the Doppler-compensated adaptive pulse compression (DC-APC) algorithm is presented whereby the respective Doppler shifts for large target returns are jointly estimated with the illuminated range profile and subsequently incorporated into the original APC adaptive receive filter formulation. As a result, the Doppler-mismatch-induced range sidelobes can be suppressed thereby regaining a significant portion of the sensitivity improvement that is possible when applying adaptive pulse compression (APC) without the existence of significant Doppler mismatch. In contrast, instead of compensating for Doppler mismatch, the single pulse imaging (SPI) algorithm generalizes the APC formulation for a bank of Doppler-shifted matched filters thereby producing a sidelobe-suppressed range-Doppler image from the return signal of a single radar pulse which is applicable for targets with substantial variation in Doppler. Both techniques are based on the recently proposed APC algorithm and its generalization, the multistatic adaptive pulse compression (MAPC) algorithm, which have been shown to be effective for the suppression of pulse compression range sidelobes thus dramatically increasing the sensitivity of pulse compression radar.     

Maximizing the Usable Bandwidth of MTI Signal Processors

A technique is presented for maximizing the percentage of usable Doppler bandwidth throughout which a radar return can be detected while maintaining an acceptable clutter suppression. The technique employs the weighted Chebyshev approximation to the design of a transversal high-pass digital filter which has an optimal passband ripple for a given number of filter weights and associated integration gain consistent with the required increase in signal-to-noise ratio needed for acceptable probabilities of detection and false alarm. Conventional approaches to the design of a movingtarget arget indictor (MTI) filter which maximizes the improvement factor by clutter suppression typically improve the signal-to-background noise ratio over less than 50 percent of the range between dc and the pulse-repetition frequency fT. This technique can increase the usable bandwidth to 80 percent or more of fT. Two examples are included which utilize parameter values from the Army Missile Command's experimental radar and demonstrate the interactive influence of such filter parameters as the number of weights, passband ripple and bandedge, and stopband attenuation and cutoff.     

Adaptive Tracking Filter for Maneuvering Targets

A general method of continually restructuring an optimum Bayes-Kalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om; puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter.     

MTI Optimization in a Multiple-Clutter Environment

A means of optimizing a moving target indicator (MTI) filter for rejecting several types of clutter, which are generated by different mechanisms such as by rain or the ground, is formulated. lt is found that the optimal performance of such a filter depends on the spectral density functions, average radar cross sections, and the relative mean Doppler frequencies of each type of clutter. lt is shown that the optimal improvement factor of such a filter is bounded by the weighted average (weighted in accordance with the radar cross sections of the clutter types) of the improvement factor for the individual clutter type. lt is also shown that the improvement factor of such a filter is a function of the relative mean Doppler frequency f0 between the clutter types. As f0 increases, the performance of the MTI system degrades. The worst improvement factor occurs when f0 is equal to half of the radar pulse-repetition frequency (PRF).     

CTD MTI Radar Filter with Charge-Transfer Inefficiency Compensation

The design and implementation of a second-order nonrecursive moving target indication (MTI) radar filter using commercially available charge-transfer devices as delay lines are described. A simple technique is included to compensate for the device charge-transfer in-efficiency and its sensitivity is analyzed. Experimental laboratory tests and results in an operating radar system are reported showing the good performance of the realized MTI radar filter.     

一种基于可观测度分析的SINS/GPS自适应-反馈校正滤波新方法

 针对机载捷联惯导系统(SINS)/全球定位系统（GPS）组合导航系统不完全可观测导致滤波器精度下降甚至发散的问题,提出了一种基于系统状态可观测度分析的自适应反馈校正滤波新方法。该滤波方法改进了系统可观测度的归一化处理方法,将归一化处理后的系统状态可观测度作为反馈因子,对SINS系统进行自适应反馈校正。最后,将该方法应用于机载合成孔径雷达(SAR)运动补偿用SINS/GPS组合导航系统中,飞行试验结果表明该方法在系统不完全可观测的情况下有效地提高了导航精度。     

基于OTR和SHT的射频隐身雷达信号设计

为了提高雷达的射频(RF)隐身性能,结合最优匹配照射-接收机(OTR)理论与序贯假设检验(SHT)方法,提出了一种新的射频隐身雷达信号设计方法。通过发射信号了解外界环境信息,然后反馈这些信息给雷达系统,系统根据这些信息自适应设计雷达发射信号,形成一个闭环系统。以雷达目标识别为具体应用,实验仿真表明,设计的雷达信号自适应变化,减小了信号间的相关性,并且减少了照射次数,降低了辐射功率,从而实现了雷达系统的射频隐身性能。     

Space-time adaptive radar performance in heterogeneous clutter

Traditional analysis of space-time adaptive radar generally assumes the ideal condition of statistically independent and identically distributed (IID) secondary data. To the contrary, measured data suggests realistic clutter environments appear heterogeneous and so the secondary data is no longer IID. Heterogeneity leads to mismatch between actual and estimated covariance matrices, thereby magnifying the loss between the adaptive implementation and optimum condition. Concerns regarding the impact of clutter heterogeneity on space-time adaptive processing (STAP) warrant further study. To this end, we propose space-time models of amplitude and spectral clutter heterogeneity, with operational airborne radar in mind, and then characterize expected STAP performance loss under such heterogeneous scenarios. Simulation results reveal loss in signal-to-interference plus noise ratio (SINR) ranging between a few tenths of a decibel to greater than 16 dB for specific cases     

一种机载雷达两级降维空时二维自适应处理方法

 提出了一种机载相控阵雷达杂波抑制的两级降维空时自适应处理(STAP)方法,即：先根据杂波分布先验信息进行空时局域化(JDL)降维处理,然后对局域化输出进行多级维纳滤波(MWF),实现二次降维。该方法综合了固定结构和自适应结构降维技术的优点,将JDL处理引入到MWF中,从而有效降低MWF的杂波自由度。计算机仿真和理论分析表明本文方法比JDL自适应处理方法和全空时MWF方法具有更小的运算量,对阵元随机幅相误差具有很好的容差能力,是一种稳健的两级降维自适应处理方法。最后,基于仿真和实测数据的实验验证了算法的有效性。