弹载合成孔径雷达自适应重频分析与设计

弹载合成孔径雷达(Synthetic Aperture Radar,SAR)的目标距离、视线角由于高速逼近目标而快速变化,这导致传统的固定脉冲重复频率(Pulse Repetition Frequency,PRF)(简称重频)波形难以兼顾弹载SAR雷达在成像各方面的约束条件,故需要根据当前弹体运动和弹目关系变化情况实时计算重频。详细分析了影响重频选择的各项因素,包括避免距离模糊、方位模糊、高度杂波、发射遮挡影响及SAR成像分辨率、系统相参性要求等影响因素,并设计了自适应重频计算的工作流程。某SAR雷达系统实验表明,该设计能够在实际飞行弹道条件下根据实际弹目关系自适应调整脉冲重复频率,从而更好地实现SAR雷达系统的工作性能,有效解决了固定重频波形不能适应弹载SAR工作条件的难题。     

一种步进频率信号认知雷达波形优化设计方法

认知雷达常用于完成探测、跟踪、成像及识别等多重任务,为提高其综合性能,需兼顾多方面因素研究其波形优化设计问题。基于此,提出一种基于压缩感知（CS）RIPless准则的步进频率信号认知雷达波形优化设计方法。首先,建立了目标回波信号稀疏模型,分析了其与发射信号模糊函数之间的关系。其次,根据模型中观测矩阵的构造,基于RIPless准则,将波形设计问题转化为概率分布的互相干参数及其协方差矩阵的条件数的优化问题,从而通过自适应寻优算法,获得优化的步进频率信号脉冲重复时间间隔序列和子脉冲频率序列。相较于传统方法,所提方法在信号发射与接收之间形成了信息实时反馈和信号优化重构的闭环,在高概率准确重构目标径向一维距离像的同时,也实现了发射信号模糊函数的优化。最后,仿真计算验证了所提方法的有效性。     

基于视线角序列的机动目标视线角速率计算

 依据拦截弹与机动目标间的相对位置关系，采用导引头球面模型，基于当前统计模型，实现了强跟踪状态自适应滤波，计算出了可用于制导的视线角速率。该算法利用导引头球面模型，将测角信息转换成距离信息，进行方差自适应调整和卡尔曼滤波。在只给出视线角序列信息的情况下，根据相对状态滤波结果求出所需的视线角速率。     

星载Mosaic模式SAR成像算法研究

以色列发射的第1颗SAR(合成孔径雷达)侦察卫星TECSAR中成功应用了一种新的工作模式———Mosa-ic模式,该模式兼具高分辨率和大成像测绘带的优势。给出了该模式可行的成像方案,并提出了一种解多普勒模糊的方位变调频算法,结合ECS(扩展线频调变标)算法对该模式进行成像,降低了系统对PRF(脉冲重复频率)的要求,在系统所能达到的有限PRF条件下,最大程度地增加了马赛克单元的方位无模糊成像宽度。仿真结果验证了算法的有效性。     

天基雷达监视的系统最佳化技术

为了使天基监视雷达达到最高效能,必须考虑探测装置和系统的各种参数,它们相互间的关系,以及这些参数与整个系统性能之间的关系。视区与轨道高度有关,雷达波束照射的区域在视区内变化相当大。这会影响到虚警时间、相应的虚警数和所需的检测门限。为解决距离和速度模糊,脉冲多普勒雷达必须采用多种脉冲重复频率(PRF)。对每种PRF都要作一次试检测,所以有几种“n中有m”的检测方案。为保持系统检测输出概率和虚警时间为预定值,这些检测方案会影响到每次试检测所需的检测检率。这些因素和波形效率强烈地影响系统的总驻留时间和区域覆盖速率。波形效率与多普勒脉冲串的波形持续时间与双程传播时间的比值有关。同时使用了相干     

高信噪比、高分辨宽测绘带成像

针对传统高分辨和宽测绘带以及高信噪比和宽测绘带之间的矛盾,提出一种基于脉内扫描面阵合成孔径雷达(SAR)系统的二维空域联合处理算法实现高信噪比、高分辨宽测绘带成像。文中首先建立脉内扫描面阵SAR系统模型,该系统采用低脉冲重复频率(PRF)获得宽测绘带信息,同时利用脉内扫描方式获得高信噪比的回波信号。对于低PRF采样宽多普勒谱(对应方位高分辨)引起的多普勒模糊以及脉内扫描引起的距离模糊,提出一种二维空域联合处理算法解距离和多普勒模糊,并且详细地分析了地形高度变化对解模糊算法的影响。最后,通过仿真实验验证了本文算法的有效性。     

基于神经网络的机动目标信息融合与并行自适应跟踪

基于“当前”统计模型和 BP神经网络 ,提出一种新的机动目标神经网络信息融合与并行自适应跟踪算法 ( NIFPAT)。该算法采用双滤波器并行结构 ,利用全状态反馈 ,通过 BP网络调整系统方差以适应目标的运动变化 ,具有对目标各种运动状态的良好自适应跟踪能力     

飞行小目标自适应双波门跟踪算法

波门跟踪是一种利用实际图像处理的区域范围的视觉跟踪算法。在飞行小目标信噪比较低的情况下,现有方法往往不能获得稳定的跟踪结果。针对小目标跟踪的特点,提出了一种自适应双波门的跟踪算法。该算法采用帧间差分算法检测小目标,通过质心标定修正当前帧目标的初始位置,并利用最小二乘估计预测算法实现目标范围的估计,从而实现自适应跟踪的目的。实验结果表明,即使在飞行小目标被短时遮挡情况下,利用本算法也能稳定地检测出它的位置,能根据目标的轮廓自适应地确定目标波门形状,并能有效解决目标跟踪偏移和短时丢失等问题。     

基于 SURF算法和 Kalman预测的运动目标跟踪

SURF(Speeded Up Robust Feature)特征提取方法是SIFT(Scale Invariant Feature Transform)算法的改进,具有速度快和精度高等特点,但其对于较大尺寸图像的匹配速度仍然有待提高。文章提出了一种将基于SURF特征匹配算法与卡尔曼滤波相融合的目标跟踪算法,该算法用特征点的中心近似目标最佳位置;通过卡尔曼滤波预测出当前的目标位置,建立自适应匹配窗口;最后,应用SURF算法提取窗口内的特征向量进行匹配。实验表明,该算法在目标发生大尺度旋转和缩放、部分遮挡时能够稳定跟踪,其跟踪速度比SURF算法有很大的提高。     

基于自适应Kalman滤波的导引头随动系统设计

主要研究导引头随动系统中探测器信号处理延迟的影响及其补偿控制算法。提出了一种自适应Kalman滤波延迟补偿方案,利用Kalman滤波的预测能力得到当前时刻视线角的估计值,进而得到此时的跟踪误差的估计值,取代被延迟的探测器输出进行闭环控制。考虑到导引头探测器的低更新频率、非等间隔量测等工程特点,又对上述滤波算法进行了一系列改进。仿真表明方法可以明显提高导引头在弹体扰动情况下的跟踪精度。     

Mitigating Range Ambiguity in High PRF Radar using Inter-Pulse Binary Coding

The paper proposes a way to increase the energy within a coherent processing interval (CPI) using more pulses instead of longer pulses. Long coded pulses result in masking targets at close range and poor Doppler tolerance. Increasing the number of pulses implies high pulse repetition frequency (PRF), which suffers from range ambiguity and target folding. These drawbacks of a high PRF can be mitigated by inter-pulse coding. The approach suggested here should be attractive for close and mid range applications of radar, ground penetrating radar, ultrasound imaging, and more.     

Ambiguity Functions for Quadratic Phase-Coded Pulse Trains

The matched filter ambiguity function is presented for a burst waveform composed of repeated subbursts, each one of which consists of N pulses in which the phase is varied quadratically from pulse to pulse. The resulting ambiguity function exhibits small residual ambiguities along the delay axis separated by the reciprocal of the pulse repetition frequency (PRF). A cross-ambiguity function is derived which reduces these ambiguities to zero amplitude. A third cross-ambiguity function is presented for a receiver matched to a generalized Hamming weighted repeated quadratic burst. The location in the delay/Doppler plane of the waveform ambiguities for these waveforms is compared with that of an uncoded pulse burst.     

Waveform selective probabilistic data association

An adaptive, waveform selective probabilistic data association (WSPDA) algorithm for tracking a single target in clutter is presented. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking subsystem equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimize the average total mean-square tracking error at the next time step. Semiclosed form solutions are given to the local (one-step-ahead) adaptive waveform selection problem for the case of one-dimensional target motion. A simple simulation example is given to compare the performance of a tracking system using a WSFDA based tracking filter with that of a conventional system with a fixed waveform shape and probabilistic data association (PDA) tracking filter.     

Doppler ambiguity resolution using multiple PRF

An algorithm for velocity ambiguity resolution in coherent pulsed Doppler radar using multiple pulse repetition frequencies (PRF) is presented. It relies on the choice of particular values for the PRFs. The folded frequency of the target signal is obtained by averaging the folded frequency estimates for each PRF, and a quasi maximum likelihood criterion is maximized for ambiguity order estimation. The fast implementation of this nonambiguous estimation procedure is based on the fast Fourier transform (FFT), The proposed waveform allows full exploitation of any (even) number of PRFs, which appears to be important for estimation improvement. The effects of the waveform parameters and the folded frequency estimation variance on the performance of the ambiguity order estimation procedure are evaluated theoretically and through computer simulations. Mean square error (MSE) curves are given to assess the Doppler frequency estimation accuracy. Finally, the new method is compared with a classical technique and the implementation of the algorithm in a clutter environment is addressed.     

Multiple model particle flter track-before-detect for range ambiguous radar

The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of targets is ambiguous.The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio（SNR）is high enough.In this paper,a multiple model particle flter（MMPF）based track-beforedetect（TBD）method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment.By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number（PIN）as components of the target state vector,and modeling the incremental variable of the PIN as a three-state Markov chain,the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state fltering problem.At last,the hybrid fltering problem is implemented by a MMPF-based TBD method in the Bayesian framework.Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously,and succeeds in detecting and tracking weak targets with the range ambiguous radar.Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis（MH）method in low-SNR environment.     

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation (CPPM) and linear frequency modulation (LFM), which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval (PRI) of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform (CHT) algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio (SNR) is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.     

Pulse Repetition Frequency Discriminator with Complete Harmonic Suppression

A method for selective pulse-train discrimination based on pulse repetition frequency (PRF) is presented. The technique permits acceptance of a pulse train with a desired PRF while rejecting all others, including those with pulse rates harmonically related to the desired one.