分布式MIMO雷達目標(biāo)定位與功率分配研究
發(fā)布時間：2019-03-31 14:43
【摘要】：多發(fā)多收(Multiple-Input Multiple Output,MIMO)雷達是雷達領(lǐng)域研究前沿的熱點問題之一。按照天線間距分為集中式和分布式MIMO雷達,集中式MIMO雷達利用波形分集增益提高雷達系統(tǒng)的角度分辨率,分布式MIMO雷達利用空間分集增益對抗目標(biāo)的RCS閃爍。分布式MIMO雷達能夠從不同的觀測方向?qū)δ繕?biāo)進行探測,為解決常規(guī)雷達面臨的隱身目標(biāo)難以檢測以及抗干擾能力差等實際問題提供了有效解決途徑,對分布式MIMO雷達所取得的初步探索成果已經(jīng)顯示了它在目標(biāo)檢測和參數(shù)估計方面的巨大優(yōu)勢。然而,對于分布式MIMO雷達的處理技術(shù)、性能評估以及系統(tǒng)設(shè)計研究成果較少,諸多概念以及關(guān)鍵技術(shù)問題還有待深入研究。本文圍繞分布式MIMO雷達的目標(biāo)定位和功率優(yōu)化展開研究,主要致力于解決現(xiàn)有目標(biāo)定位方法和功率優(yōu)化分配方法所面臨的關(guān)鍵問題。具體地,本文的研究內(nèi)容包括如下幾個方面:第一章為緒論,闡述了課題研究背景及意義,介紹了MIMO雷達尤其是分布式MIMO雷達的研究現(xiàn)狀,分析了目前分布式MIMO雷達參數(shù)估計和功率分配面臨的問題,并指出了開展目標(biāo)定位方法和功率優(yōu)化管理研究的可行性。第二章主要研究了分布式MIMO雷達的目標(biāo)定位性能和它對雷達天線位置誤差的敏感性問題。根據(jù)似然函數(shù)與似然比的關(guān)系得到似然函數(shù),將目標(biāo)定位問題抽象為位置參數(shù)的最大似然估計問題,推導(dǎo)了了目標(biāo)位置參數(shù)的Fisher信息矩陣和克拉美-羅下界。針對雷達天線位置存在測量誤差可能導(dǎo)致定位性能下降的問題,首先利用一階泰勒近似推導(dǎo)了忽略天線位置誤差時目標(biāo)位置估計的均方誤差,然后基于統(tǒng)計獨立的思想推導(dǎo)了同時考慮天線位置誤差和目標(biāo)位置參數(shù)的聯(lián)合克拉美-羅下界,定量分析了天線位置誤差對定位精度的影響,為后續(xù)研究奠定了理論基礎(chǔ)。第三章針對單目標(biāo)定位場景,主要研究了基于半定規(guī)劃理論的分布式MIMO雷達間接定位方法。將基于雙站距離觀測的間接定位建模為非線性最小二乘問題,通過一階泰勒近似推導(dǎo)了線性化定位方法。為了從理論上保證獲得全局最優(yōu)解,借助凸優(yōu)化思想提出了基于半定規(guī)劃的目標(biāo)定位方法。該方法通過引入多余變量,將非線性最小二乘問題轉(zhuǎn)化為帶約束的凸優(yōu)化問題,然后通過半定松弛技術(shù)對非凸約束條件進行松弛再轉(zhuǎn)化為可解的半定規(guī)劃問題。在該框架下,還推導(dǎo)了存在天線位置誤差情況下的半定規(guī)劃定位方法,該方法大幅度降低了天線位置誤差帶來的影響,提高了目標(biāo)定位的穩(wěn)健性能。第四章針對多目標(biāo)定位場景,主要研究了基于稀疏重構(gòu)理論的分布式MIMO雷達直接定位方法。將基于匹配濾波信號的直接目標(biāo)定位建模為一種塊稀疏表示模型,目標(biāo)定位轉(zhuǎn)化為塊稀疏重構(gòu)問題。為解決上述問題,在塊稀疏貝葉斯學(xué)習(xí)框架下發(fā)展出一種多目標(biāo)定位方法,該方法利用塊內(nèi)的相關(guān)性,可以提高重構(gòu)精度。實驗結(jié)果表明,所提算法課不需要數(shù)據(jù)關(guān)聯(lián)處理,具有處理密集目標(biāo)、壓縮采樣等條件下定位問題的能力。另外,針對相參處理中可能存在的相位不同步問題,在塊稀疏貝葉斯學(xué)習(xí)和最大似然估計框架下提出了一種迭代算法來解決上述問題,具體來說,迭代利用最大似然估計出相位誤差和塊稀疏貝葉斯學(xué)習(xí)重構(gòu)出目標(biāo)散射系數(shù),該算法在相位失配情況下體現(xiàn)出良好的誤差校正能力和較高的定位精度。第五章主要研究了分布式MIMO雷達針對目標(biāo)定位的功率分配問題。針對間接定位模型,嚴格推導(dǎo)了目標(biāo)位置的貝葉斯Fisher信息矩陣,詳細給出了隨機可觀測性的粒子近似計算方法。以隨機可觀測性為目標(biāo)函數(shù)、發(fā)射總功率為約束條件,將功率優(yōu)化分配建模為非凸二次問題。為解決上述問題,闡述了合作博弈理論中沙普利值的概念、性質(zhì)和求解,在該框架下提出了一種基于沙普利值的功率分配方法,給出了迭代實現(xiàn)流程。該方法利用加權(quán)圖理論簡化了沙普利值的計算過程,且具有明確的物理意義。仿真結(jié)果表明,所提算法通過合理優(yōu)化分配發(fā)射功率,有效提高了目標(biāo)定位精度和資源使用效率。第六章對本文的研究工作和主要創(chuàng)新點進行了總結(jié),并指出了下一步可能的研究方向。
[Abstract]:Multiple-Input Multiple Output (MIMO) radar is one of the hot issues in radar field. According to the antenna spacing, the centralized and distributed MIMO radar is divided into a centralized MIMO radar and a distributed MIMO radar, the angular resolution of the radar system is improved by utilizing the waveform diversity gain, and the distributed MIMO radar uses the space diversity gain to fight the RCS of the target. The distributed MIMO radar can detect the target from different observation directions, and provides an effective solution for solving the practical problems such as difficult detection of the stealth target and poor anti-interference ability of the conventional radar, The results of the preliminary exploration on the distributed MIMO radar have shown the great advantages of the distributed MIMO radar in the aspects of target detection and parameter estimation. However, for distributed MIMO radar processing technology, performance evaluation and system design research results are less, many concepts and key technology problems remain in-depth study. In this paper, the research on the target location and power optimization of the distributed MIMO radar is mainly focused on solving the key problems of the existing target location method and the power optimization distribution method. In this paper, the research contents of this paper include the following aspects: the first chapter is the introduction, the background and significance of the subject research are set forth, the research status of the MIMO radar, especially the distributed MIMO radar, is introduced, and the problems of the current distributed MIMO radar parameter estimation and power distribution are analyzed. It also points out the feasibility of carrying out the research on the target location method and the power optimization management. The second chapter mainly studies the target positioning performance of the distributed MIMO radar and its sensitivity to the position error of the radar antenna. The likelihood function is obtained according to the relation between the likelihood function and the likelihood ratio, and the target location problem is abstracted as the maximum likelihood estimation problem of the position parameter, and the Fisher information matrix and the Cramer-Luo lower bound of the target position parameter are derived. Aiming at the problem that the measurement error in the position of the radar antenna can lead to the degradation of the positioning performance, the first-order Taylor approximation is used to derive the mean square error of the target position estimation when the position error of the antenna is ignored, Then, based on the idea of statistical independence, the lower bound of the combination of the position error of the antenna and the parameter of the target position is also taken into account, and the influence of the position error of the antenna on the positioning accuracy is quantitatively analyzed, and the theoretical foundation is laid for the follow-up research. In the third chapter, a distributed MIMO radar indirect location method based on the semi-definite programming theory is mainly studied for the single-target location scenario. In this paper, the nonlinear least-square problem is modeled by the indirect positioning based on the double-station distance observation, and the linear positioning method is derived by the first-order Taylor approximation. In order to obtain the global optimal solution theoretically, the target location method based on semi-definite programming is proposed by means of convex optimization. In this method, the non-linear least square problem is transformed into a constrained convex optimization problem by introducing the redundant variable, and then the non-convex constraint condition is relaxed and then converted into a solvable semi-definite programming problem through the semi-fixed relaxation technique. In that framework, the semi-definite plan positioning method in the case of the position error of the antenna is also derived, and the influence of the position error of the antenna is greatly reduced, and the robust performance of the target positioning is improved. In the fourth chapter, the direct location method of the distributed MIMO radar based on the sparse reconstruction theory is mainly studied for the multi-objective positioning scene. The direct target location based on the matched filtered signal is modeled as a block sparse representation model, and the target location is transformed into a block sparse reconstruction problem. In order to solve the above problems, a multi-object positioning method is developed under the block sparse Bayesian learning framework, which can improve the reconstruction precision by utilizing the correlation between the blocks. The experimental results show that the proposed algorithm does not need the data association processing, and has the ability to deal with the positioning problem under the condition of intensive target, compressed sampling and the like. In addition, an iterative algorithm is proposed to solve the above-mentioned problems in the framework of block sparse Bayesian learning and maximum likelihood estimation for the phase non-synchronization problem that may exist in the coherent processing, in particular, The target scattering coefficient is reconstructed by using the maximum likelihood estimation out of phase error and the block sparse Bayesian learning, and the algorithm exhibits good error correction capability and high positioning accuracy in the case of phase mismatch. The fifth chapter mainly studies the power distribution problem of the distributed MIMO radar aiming at the target location. In this paper, the Bayesian Fisher information matrix of the target position is derived strictly for the indirect positioning model, and the approximate calculation method of the random observability is given in detail. Taking the random observability as the objective function, the total power is the constraint condition, and the power optimization assignment is modeled as the non-convex quadratic problem. In order to solve the above problems, the concept, nature and solution of the Shapley's value in the cooperative game theory are set forth, and a power distribution method based on the value of Shapley is proposed in this framework, and the iterative realization process is given. The method simplifies the calculation process of the value of the Shapley by using the weight graph theory, and has a clear physical meaning. The simulation results show that the proposed algorithm can effectively improve the target positioning accuracy and resource efficiency by reasonably optimizing the distributed transmission power. In the sixth chapter, the research work and main innovation point of this paper are summarized, and the possible research direction is pointed out.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN958
本文編號：2451007
[Abstract]:Multiple-Input Multiple Output (MIMO) radar is one of the hot issues in radar field. According to the antenna spacing, the centralized and distributed MIMO radar is divided into a centralized MIMO radar and a distributed MIMO radar, the angular resolution of the radar system is improved by utilizing the waveform diversity gain, and the distributed MIMO radar uses the space diversity gain to fight the RCS of the target. The distributed MIMO radar can detect the target from different observation directions, and provides an effective solution for solving the practical problems such as difficult detection of the stealth target and poor anti-interference ability of the conventional radar, The results of the preliminary exploration on the distributed MIMO radar have shown the great advantages of the distributed MIMO radar in the aspects of target detection and parameter estimation. However, for distributed MIMO radar processing technology, performance evaluation and system design research results are less, many concepts and key technology problems remain in-depth study. In this paper, the research on the target location and power optimization of the distributed MIMO radar is mainly focused on solving the key problems of the existing target location method and the power optimization distribution method. In this paper, the research contents of this paper include the following aspects: the first chapter is the introduction, the background and significance of the subject research are set forth, the research status of the MIMO radar, especially the distributed MIMO radar, is introduced, and the problems of the current distributed MIMO radar parameter estimation and power distribution are analyzed. It also points out the feasibility of carrying out the research on the target location method and the power optimization management. The second chapter mainly studies the target positioning performance of the distributed MIMO radar and its sensitivity to the position error of the radar antenna. The likelihood function is obtained according to the relation between the likelihood function and the likelihood ratio, and the target location problem is abstracted as the maximum likelihood estimation problem of the position parameter, and the Fisher information matrix and the Cramer-Luo lower bound of the target position parameter are derived. Aiming at the problem that the measurement error in the position of the radar antenna can lead to the degradation of the positioning performance, the first-order Taylor approximation is used to derive the mean square error of the target position estimation when the position error of the antenna is ignored, Then, based on the idea of statistical independence, the lower bound of the combination of the position error of the antenna and the parameter of the target position is also taken into account, and the influence of the position error of the antenna on the positioning accuracy is quantitatively analyzed, and the theoretical foundation is laid for the follow-up research. In the third chapter, a distributed MIMO radar indirect location method based on the semi-definite programming theory is mainly studied for the single-target location scenario. In this paper, the nonlinear least-square problem is modeled by the indirect positioning based on the double-station distance observation, and the linear positioning method is derived by the first-order Taylor approximation. In order to obtain the global optimal solution theoretically, the target location method based on semi-definite programming is proposed by means of convex optimization. In this method, the non-linear least square problem is transformed into a constrained convex optimization problem by introducing the redundant variable, and then the non-convex constraint condition is relaxed and then converted into a solvable semi-definite programming problem through the semi-fixed relaxation technique. In that framework, the semi-definite plan positioning method in the case of the position error of the antenna is also derived, and the influence of the position error of the antenna is greatly reduced, and the robust performance of the target positioning is improved. In the fourth chapter, the direct location method of the distributed MIMO radar based on the sparse reconstruction theory is mainly studied for the multi-objective positioning scene. The direct target location based on the matched filtered signal is modeled as a block sparse representation model, and the target location is transformed into a block sparse reconstruction problem. In order to solve the above problems, a multi-object positioning method is developed under the block sparse Bayesian learning framework, which can improve the reconstruction precision by utilizing the correlation between the blocks. The experimental results show that the proposed algorithm does not need the data association processing, and has the ability to deal with the positioning problem under the condition of intensive target, compressed sampling and the like. In addition, an iterative algorithm is proposed to solve the above-mentioned problems in the framework of block sparse Bayesian learning and maximum likelihood estimation for the phase non-synchronization problem that may exist in the coherent processing, in particular, The target scattering coefficient is reconstructed by using the maximum likelihood estimation out of phase error and the block sparse Bayesian learning, and the algorithm exhibits good error correction capability and high positioning accuracy in the case of phase mismatch. The fifth chapter mainly studies the power distribution problem of the distributed MIMO radar aiming at the target location. In this paper, the Bayesian Fisher information matrix of the target position is derived strictly for the indirect positioning model, and the approximate calculation method of the random observability is given in detail. Taking the random observability as the objective function, the total power is the constraint condition, and the power optimization assignment is modeled as the non-convex quadratic problem. In order to solve the above problems, the concept, nature and solution of the Shapley's value in the cooperative game theory are set forth, and a power distribution method based on the value of Shapley is proposed in this framework, and the iterative realization process is given. The method simplifies the calculation process of the value of the Shapley by using the weight graph theory, and has a clear physical meaning. The simulation results show that the proposed algorithm can effectively improve the target positioning accuracy and resource efficiency by reasonably optimizing the distributed transmission power. In the sixth chapter, the research work and main innovation point of this paper are summarized, and the possible research direction is pointed out.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN958
【參考文獻】
相關(guān)期刊論文 前2條
1 賈高偉;常文革;;分布式雷達空間目標(biāo)定位系統(tǒng)性能分析[J];雷達科學(xué)與技術(shù);2010年03期
2 ;ORTHOGONAL DISCRETE FREQUENCY-CODING WAVEFORM DESIGN FOR MIMO RADAR[J];Journal of Electronics(China);2008年04期
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