基于射頻信號(hào)及信道狀態(tài)信息的被動(dòng)式目標(biāo)定位方法研究
發(fā)布時(shí)間：2018-11-27 10:28
【摘要】：以無(wú)線信號(hào)感知為中心的被動(dòng)式目標(biāo)定位,以其無(wú)需給待定位目標(biāo)攜帶任何設(shè)備的優(yōu)點(diǎn),成為了許多應(yīng)用中最受歡迎的技術(shù)之一,如入侵檢測(cè)、老人健康監(jiān)測(cè)等。與傳統(tǒng)基于紅外、視頻、光學(xué)感知的被動(dòng)式目標(biāo)定位方法相比,基于無(wú)線信號(hào)的被動(dòng)式目標(biāo)定位優(yōu)點(diǎn)在于：無(wú)線信號(hào)覆蓋范圍廣,一般不存在死角問(wèn)題,可以工作在有遮擋、非視距的場(chǎng)景下,對(duì)光線要求和遮擋物不敏感,可以全天候晝夜工作。近年來(lái)隨著WiFi等無(wú)線設(shè)備在人們?nèi)粘Ｉ钪械钠占?使得基于無(wú)線信號(hào)的被動(dòng)式目標(biāo)定位成為技術(shù)主流。盡管基于無(wú)線信號(hào)的被動(dòng)式目標(biāo)定位在國(guó)際范圍內(nèi)取得了重要的進(jìn)展,然而在實(shí)際應(yīng)用中,現(xiàn)有方法還存在如下四個(gè)主要的問(wèn)題。第一,為達(dá)到高精度的多目標(biāo)定位,現(xiàn)有被動(dòng)式目標(biāo)定位算法需要采集大量目標(biāo)對(duì)無(wú)線信號(hào)強(qiáng)度(received signal strength, RSS)造成的擾動(dòng)改變值,導(dǎo)致了高能量消耗,制約了系統(tǒng)在實(shí)際環(huán)境下的可用性。第二,現(xiàn)有被動(dòng)式目標(biāo)定位方法,均假設(shè)先驗(yàn)獲取的某類(lèi)目標(biāo)的RSS擾動(dòng)先驗(yàn)知識(shí)數(shù)據(jù)可以適用于其他種類(lèi)目標(biāo)。然而,實(shí)際應(yīng)用中不同種類(lèi)目標(biāo)的形狀、散射面等屬性不同,使得定位時(shí)得到的某類(lèi)目標(biāo)的RSS擾動(dòng)數(shù)據(jù)與先驗(yàn)獲得的其他類(lèi)目標(biāo)知識(shí)庫(kù)失配,造成較大的定位誤差。第三,僅依賴(lài)RSS信號(hào)的被動(dòng)式定位技術(shù)盡管已經(jīng)取得了重要的進(jìn)展,但是由于RSS信號(hào)本身的不穩(wěn)定性,其定位精度仍然十分有限。第四,現(xiàn)有基于通用設(shè)備的高精度被動(dòng)式定位方法大都依賴(lài)指紋庫(kù),導(dǎo)致人力代價(jià)高的問(wèn)題。因此,探尋真實(shí)場(chǎng)景下更為普適的被動(dòng)式目標(biāo)定位方法具有重大意義和價(jià)值。本文從目標(biāo)對(duì)無(wú)線信號(hào)擾動(dòng)的不同角度出發(fā),分析了包括目標(biāo)對(duì)信號(hào)幅度、相位、信道狀態(tài)信息的影響規(guī)律,以低能量消耗、目標(biāo)種類(lèi)自適應(yīng)、高精度、低代價(jià)為目標(biāo),提出了四種不同真實(shí)場(chǎng)景下具有可用性的被動(dòng)式目標(biāo)定位方法。本文工作的主要?jiǎng)?chuàng)新研究如下：(i)針對(duì)現(xiàn)有被動(dòng)式目標(biāo)定位算法需采集大量觀測(cè)值導(dǎo)致高能量消耗的問(wèn)題,本文提出一種基于壓縮感知的低能耗高精度多目標(biāo)被動(dòng)式定位方法E-HIPAo根據(jù)多目標(biāo)位置在物理空間中具有的固有稀疏性,將多目標(biāo)定位問(wèn)題建模為壓縮感知稀疏恢復(fù)問(wèn)題。利用壓縮感知理論,通過(guò)收集少量RSS觀測(cè)數(shù)據(jù)用于目標(biāo)位置的估計(jì),降低通信代價(jià)和能量消耗。提出自適應(yīng)正交匹配追蹤算法,在無(wú)需先驗(yàn)預(yù)知目標(biāo)個(gè)數(shù)的情況下,精確估計(jì)出多目標(biāo)的位置。大量真實(shí)實(shí)驗(yàn)結(jié)果表明,E-HIPA相比于現(xiàn)有的方法,能夠在降低能量消耗的前提下保證高精度多目標(biāo)定位,因此更適用于真實(shí)部署應(yīng)用場(chǎng)景。(ii)針對(duì)實(shí)際應(yīng)用中需要針對(duì)每一類(lèi)目標(biāo)建立先驗(yàn)知識(shí)庫(kù)導(dǎo)致人力消耗巨大的問(wèn)題,本文進(jìn)一步提出一種可遷移壓縮感知的多目標(biāo)被動(dòng)式定位方法TLCS。借鑒遷移學(xué)習(xí)思想構(gòu)建遷移函數(shù),在不同種類(lèi)目標(biāo)間進(jìn)行指紋庫(kù)遷移,保證了遷移后的不同種類(lèi)目標(biāo)的RSS干擾改變值在潛在特種空間具有分布相似性,使得不同種類(lèi)目標(biāo)共享相同一個(gè)先驗(yàn)數(shù)據(jù)庫(kù),從而避免了對(duì)每類(lèi)目標(biāo)多次人工勘測(cè)標(biāo)定帶來(lái)的部署開(kāi)銷(xiāo)和人力消耗。嚴(yán)謹(jǐn)?shù)睦碚摲治龊痛罅空鎸?shí)實(shí)驗(yàn)表明,TLCS方法不僅保證了高定位精度,而且極大降低了多次學(xué)習(xí)先驗(yàn)指紋庫(kù)的人力消耗代價(jià)。(iii)針對(duì)現(xiàn)有被動(dòng)式目標(biāo)定位方法在多徑效應(yīng)影響下無(wú)法精確定位目標(biāo)的問(wèn)題,本文提出基于多徑信號(hào)空間譜分析的高精度被動(dòng)式目標(biāo)定位方法D-Watch。與傳統(tǒng)方法均假設(shè)多徑信號(hào)是不利因素而不同,D-Watch利用豐富的多徑信號(hào)空間譜信息來(lái)識(shí)別目標(biāo)的方位角,綜合多個(gè)方位角可以定位目標(biāo)位置。為了提高目標(biāo)方位角估計(jì)精度,本文對(duì)對(duì)多徑信號(hào)空間譜的相關(guān)矩陣進(jìn)行特征值分解,利用信號(hào)子空間和噪聲子空間的正交性,通過(guò)最優(yōu)化理論對(duì)相位誤差進(jìn)行校正,使得可以獲取精確的多徑信號(hào)空間譜信息,達(dá)到了多經(jīng)環(huán)境下高精度被動(dòng)式定位目標(biāo)的目的。大量真實(shí)實(shí)驗(yàn)表明,D-Watch能夠利用細(xì)粒度的多徑信號(hào)空間譜進(jìn)行分米級(jí)精度定位,突破了傳統(tǒng)方法無(wú)法再多徑環(huán)境下精度定位目標(biāo)的瓶頸。(iv)針對(duì)現(xiàn)有基于指紋的被動(dòng)式目的定位方法,因面臨場(chǎng)景變換需要重新采集指紋庫(kù)而導(dǎo)致人力代價(jià)高的問(wèn)題,本文提出一種基于信道狀態(tài)模型的低成本高精度被動(dòng)式目標(biāo)定位方法LiFS。利用不同子載波的信道狀態(tài)信息受到多徑影響不同的特性,提取出未受多徑影響的“干凈”子載波,從而構(gòu)建了精確的“信道狀態(tài)-目標(biāo)位置”關(guān)聯(lián)模型。將所有鏈路的位置以及“干凈”子載波的信道狀體信息作為模型的輸入,利用不同子載波上的關(guān)聯(lián)模型來(lái)精確計(jì)算目標(biāo)位置,避免了大量指紋庫(kù)訓(xùn)練的代價(jià)問(wèn)題。大量真實(shí)實(shí)驗(yàn)表明,LiFS在現(xiàn)有WiFi設(shè)備上,實(shí)現(xiàn)了低成本、高精度的被動(dòng)式目標(biāo)定位,因此更適用于真實(shí)場(chǎng)景下的定位需求。
[Abstract]:The passive target location, which is perceived as a center by the wireless signal, is one of the most popular technologies in many applications, such as the intrusion detection, the health monitoring of the elderly, and the like, without the advantage of carrying any equipment to the target to be located. Compared with the traditional passive target positioning method based on infrared, video and optical sensing, the passive target positioning method based on the wireless signal has the advantages that the wireless signal coverage range is wide, It is not sensitive to the light requirements and the shelter, and can work all the clock and night. In recent years, with the popularization of wireless devices such as WiFi in the daily life of people, the passive target positioning based on the wireless signal becomes the main technology. Although the passive target location based on the wireless signal has made important progress in the international range, in practical applications, there are four main problems in the prior art. First, in order to achieve the multi-target positioning with high accuracy, the present passive target positioning algorithm needs to acquire the disturbance change value caused by a large number of targets to the wireless signal strength (RSS), resulting in high energy consumption and the availability of the system in the actual environment. secondly, the prior passive target positioning method is used for assuming that the prior knowledge data of the RSS disturbance of a certain type of target acquired a priori can be applied to other kinds of targets. However, the shape, scattering surface and other properties of different kinds of objects in the practical application are different, so that the RSS disturbance data of some kind of target obtained at the time of positioning is not matched with the prior knowledge base of other classes, resulting in a large positioning error. Third, although the passive positioning technology relying on the RSS signal has made important progress, the positioning accuracy of the RSS signal is still very limited due to the instability of the RSS signal itself. Fourth, the existing high-precision passive positioning method based on general equipment mostly relies on the fingerprint library, resulting in high labor cost. Therefore, it is of great significance and value to find a more suitable passive target location method in real scene. In this paper, the influence of the target on the signal amplitude, phase and channel state information is analyzed from different angles of the target to the wireless signal disturbance. The target is low energy consumption, target type adaptation, high accuracy and low cost. A passive target location method with availability in four different real scenarios is presented. the main innovation of the work is as follows: (i) the problem of high energy consumption is caused by the acquisition of a large number of observation values for the existing passive target positioning algorithm, In this paper, a high-precision multi-target passive positioning method based on compression-sensing is proposed, which is based on the inherent sparsity of the multi-target location in the physical space, and the multi-target location problem is modeled as the compression-aware sparse recovery problem. By using the compression-aware theory, the communication cost and energy consumption are reduced by collecting a small amount of RSS observation data for estimation of the target position. A self-adaptive orthogonal matching tracking algorithm is proposed, and the position of the multi-target can be accurately estimated without a priori knowledge of the number of the targets. A large number of real-time experiments show that, compared with the existing method, E-HIPA can ensure high-precision multi-target positioning on the premise of reducing energy consumption, so that the E-HIPA is more suitable for real-deployment application scenarios. (ii) The need to establish a priori knowledge base for each class of objects in the actual application results in significant human consumption. In this paper, a multi-target passive location method TLCS is proposed. using the migration learning idea to construct a migration function, carrying out fingerprint library migration among different kinds of objects, ensuring that the RSS interference change value of different kinds of objects after the migration has the distribution similarity in the potential special space, so that different kinds of objects share the same prior database, so as to avoid the deployment cost and the manpower consumption caused by multiple manual survey and calibration of each class of targets. The rigorous theoretical analysis and a large number of real experiments show that the TLCS method not only ensures the high positioning accuracy, but also greatly reduces the labor consumption cost of the prior fingerprint library for many times. (iii) Aiming at the problem that the existing passive target positioning method cannot accurately position the target under the influence of the multi-diameter effect, the invention provides a high-precision passive target positioning method D-Watch based on the multi-path signal space spectrum analysis. Compared with the traditional method, the multi-path signal is assumed to be a disadvantage, and the D-Watch uses the abundant multi-path signal space spectrum information to identify the azimuth of the target, and the comprehensive multiple azimuth angles can be used for positioning the target position. In order to improve the accuracy of the target azimuth estimation, the correlation matrix of the multi-path signal space spectrum is decomposed, the orthogonality of the signal subspace and the noise subspace is utilized, the phase error is corrected by the optimization theory, so that the accurate multi-path signal space spectrum information can be obtained, and the purpose of the high-precision passive positioning target under the multi-channel environment is achieved. A large number of real experiments show that the D-Watch can use the fine-grained multi-path signal space spectrum to position the sub-meter-level precision, and breaks through the bottleneck that the traditional method can not accurately locate the target under the multi-path environment. (iv) Aiming at the problem of high labor cost due to the need to re-acquire the fingerprint library in the face of the scene transformation, a low-cost high-precision passive target positioning method based on the channel state model is proposed for the existing passive object positioning method based on the fingerprint. The channel state information of different sub-carriers is influenced by the multi-path to different characteristics, and the 鈥渃lean鈥，
本文編號(hào)：2360422
[Abstract]:The passive target location, which is perceived as a center by the wireless signal, is one of the most popular technologies in many applications, such as the intrusion detection, the health monitoring of the elderly, and the like, without the advantage of carrying any equipment to the target to be located. Compared with the traditional passive target positioning method based on infrared, video and optical sensing, the passive target positioning method based on the wireless signal has the advantages that the wireless signal coverage range is wide, It is not sensitive to the light requirements and the shelter, and can work all the clock and night. In recent years, with the popularization of wireless devices such as WiFi in the daily life of people, the passive target positioning based on the wireless signal becomes the main technology. Although the passive target location based on the wireless signal has made important progress in the international range, in practical applications, there are four main problems in the prior art. First, in order to achieve the multi-target positioning with high accuracy, the present passive target positioning algorithm needs to acquire the disturbance change value caused by a large number of targets to the wireless signal strength (RSS), resulting in high energy consumption and the availability of the system in the actual environment. secondly, the prior passive target positioning method is used for assuming that the prior knowledge data of the RSS disturbance of a certain type of target acquired a priori can be applied to other kinds of targets. However, the shape, scattering surface and other properties of different kinds of objects in the practical application are different, so that the RSS disturbance data of some kind of target obtained at the time of positioning is not matched with the prior knowledge base of other classes, resulting in a large positioning error. Third, although the passive positioning technology relying on the RSS signal has made important progress, the positioning accuracy of the RSS signal is still very limited due to the instability of the RSS signal itself. Fourth, the existing high-precision passive positioning method based on general equipment mostly relies on the fingerprint library, resulting in high labor cost. Therefore, it is of great significance and value to find a more suitable passive target location method in real scene. In this paper, the influence of the target on the signal amplitude, phase and channel state information is analyzed from different angles of the target to the wireless signal disturbance. The target is low energy consumption, target type adaptation, high accuracy and low cost. A passive target location method with availability in four different real scenarios is presented. the main innovation of the work is as follows: (i) the problem of high energy consumption is caused by the acquisition of a large number of observation values for the existing passive target positioning algorithm, In this paper, a high-precision multi-target passive positioning method based on compression-sensing is proposed, which is based on the inherent sparsity of the multi-target location in the physical space, and the multi-target location problem is modeled as the compression-aware sparse recovery problem. By using the compression-aware theory, the communication cost and energy consumption are reduced by collecting a small amount of RSS observation data for estimation of the target position. A self-adaptive orthogonal matching tracking algorithm is proposed, and the position of the multi-target can be accurately estimated without a priori knowledge of the number of the targets. A large number of real-time experiments show that, compared with the existing method, E-HIPA can ensure high-precision multi-target positioning on the premise of reducing energy consumption, so that the E-HIPA is more suitable for real-deployment application scenarios. (ii) The need to establish a priori knowledge base for each class of objects in the actual application results in significant human consumption. In this paper, a multi-target passive location method TLCS is proposed. using the migration learning idea to construct a migration function, carrying out fingerprint library migration among different kinds of objects, ensuring that the RSS interference change value of different kinds of objects after the migration has the distribution similarity in the potential special space, so that different kinds of objects share the same prior database, so as to avoid the deployment cost and the manpower consumption caused by multiple manual survey and calibration of each class of targets. The rigorous theoretical analysis and a large number of real experiments show that the TLCS method not only ensures the high positioning accuracy, but also greatly reduces the labor consumption cost of the prior fingerprint library for many times. (iii) Aiming at the problem that the existing passive target positioning method cannot accurately position the target under the influence of the multi-diameter effect, the invention provides a high-precision passive target positioning method D-Watch based on the multi-path signal space spectrum analysis. Compared with the traditional method, the multi-path signal is assumed to be a disadvantage, and the D-Watch uses the abundant multi-path signal space spectrum information to identify the azimuth of the target, and the comprehensive multiple azimuth angles can be used for positioning the target position. In order to improve the accuracy of the target azimuth estimation, the correlation matrix of the multi-path signal space spectrum is decomposed, the orthogonality of the signal subspace and the noise subspace is utilized, the phase error is corrected by the optimization theory, so that the accurate multi-path signal space spectrum information can be obtained, and the purpose of the high-precision passive positioning target under the multi-channel environment is achieved. A large number of real experiments show that the D-Watch can use the fine-grained multi-path signal space spectrum to position the sub-meter-level precision, and breaks through the bottleneck that the traditional method can not accurately locate the target under the multi-path environment. (iv) Aiming at the problem of high labor cost due to the need to re-acquire the fingerprint library in the face of the scene transformation, a low-cost high-precision passive target positioning method based on the channel state model is proposed for the existing passive object positioning method based on the fingerprint. The channel state information of different sub-carriers is influenced by the multi-path to different characteristics, and the 鈥渃lean鈥，
本文編號(hào)：2360422
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