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Review of the Method for Distributed Multi-sensor Multi-target Tracking（in English）
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摘要: 多传感器多目标跟踪是信息融合领域的热点问题，其通过融合多个局部传感器数据，提高目标跟踪精度和稳定性。多传感器多目标跟踪按融合体系可分为分布式、集中式、混合式3类，其中分布式融合结构对网络通信带宽要求低、可靠性和稳定性强，广泛应用于军事、民用领域。该文聚焦分布式多传感器多目标跟踪涉及的目标跟踪、传感器配准、航迹关联、数据融合4项关键技术，主要分析了各关键技术的理论原理与适用条件，重点介绍了不完整测量条件下的空间配准与航迹关联，并给出仿真结果。最后，该文总结了现有分布式多传感器多目标跟踪关键技术存在的问题，并指出了其未来发展趋势。
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关键词:
- 分布式多传感器多目标跟踪 /
- 目标跟踪 /
- 传感器配准 /
- 航迹关联 /
- 数据融合
Abstract: Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed. -
1. 引言
外辐射源雷达是一种利用非合作辐射源(如广播电视、通讯基站、导航和通信卫星、无线局域网络等)对目标进行探测的双/多基地雷达系统。与传统主动雷达相比，具有无需频率分配、隐蔽性好、抗干扰能力强、电磁兼容性好等诸多优势 [1] 。单发单收结构的双基地外辐射源雷达理论方法及系统设计已日臻成熟，然而双基地架构存在着一些缺陷，如分辨率强烈依赖于收发站几何位置、目标散射截面积受限于目标姿态等，使其在探测稳定性和跟踪连续性上表现出不足 [2 – 6] 。采用如 图1 所示的多发多收(包括多发单收、单发多收)分布式探测体制是有效的解决方案之一。中国新一代的数字广播电视广泛采用网络化覆盖模式，如中国移动多媒体广播(China Mobile Multimedia Broadcasting, CMMB)/数字地面多媒体广播(Digital Television Terrestrial Multimedia Broadcasting, DTMB)/数字音频广播(China Digital Radio, CDR)，其发射信号普遍利用了多载波体制的正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)调制，信号带宽大，功率稳定，非常适合用作分布式外辐射源雷达的机会照射源。
分布式外辐射源雷达在系统组成和收发处理上比传统雷达复杂，还有许多特殊的关键技术问题亟待解决，首当其冲的是各单元雷达接收站与辐射源的空间、频率、时间的同步。空间同步即波束同步，是指参与目标探测的雷达接收机和辐射源的主波束必须同时指向到同一个目标空间，才能使接收到的目标回波信号最强。频率同步主要包括载波同步和采样率同步：载波同步是指接收机的本地混频振荡器频率与照射源发射机的载波频率必须一致，才能将信号准确恢复到基带；采样率同步是指接收机实际采样率与标称采样率必须一致，否则，采样率偏差的积累会导致目标跟踪结果异常。时间同步是指各发射站之间和各接收站之间均应有统一的时间基准，这是分布式雷达准确定位目标的前提。
上述三大同步问题中，空间同步较容易实现，这是因为数字广播电视基站发射天线多具有全向性，只需要人为控制各单元接收站天线波束指向同一目标空间即可。而由于发射站的不可控性，外辐射源雷达系统频率同步和时间同步的方案设计依赖于所用照射源发射系统本身的同步方式，并且系统同步性能的测试也比主动雷达更难。故本文重点讨论频率同步和时间同步的设计和测试问题。
传统双(多)基地雷达的同步方法可分为直接法、间接法和独立式法 [7] ，其中直接法多适用于主动雷达，独立式法的同步稳定性及精度相比而言都较差，因此分布式外辐射源雷达一般采用间接法。间接法是在各雷达站分别设置一个相同的高稳定度时钟，通过定期校准时钟，用来作为时间基准实现各雷达站间的时间频率同步。校准时钟信号的授时技术有：微波授时技术、光纤授时技术、卫星授时技术、激光授时技术。这些方法各有优劣，其中微波传输距离短，光纤缺乏机动性，激光易受遮挡，而卫星作用范围广、不受视距限制、且有良好的机动性，因此在实际应用中多基地外辐射源雷达的同步方式多采用卫星授时技术。文献[8]指出，地面数字电视广播基站发射系统采用GPS接收机提供频率参考和时间参考，因此，基于此辐射源的分布式外辐射源雷达系统亦需采用GPS授时技术实现时间和频率同步。本文提出的同步方案核心是为每个单元接收站配置一个GPS接收模块，该模块为接收机提供高精度高稳定的10 MHz频率源和精确的1PPS(1 Pulse Per Second)定时信号，使系统实现频率同步和时间同步。并且，通过分析CMMB信号帧结构特点，本文提出了一种基于CMMB帧同步信号的同步性能测试方法，然后结合在江西南昌开展的分布式多站实验，通过实测数据分析，验证了系统同步的可靠性。
2. 分布式外辐射源雷达系统同步需求
2.1 频率同步
数字广播电视信号广泛采用OFDM调制，OFDM调制技术具有良好的抗多径能力，各符号间带有保护间隔且子载波间是相互正交的。外辐射源雷达系统通常设有两个通道：参考通道和监测通道，其中参考通道波束一般正对信号发射站，用来接收直达波信号；监测通道波束对准目标空间，接收目标回波信号。与常规双(多)基地雷达一样，外辐射源雷达系统工作时，发射端将基带信号调制至射频进行发射，接收端通过混频等方式将信号解调到基带，这要求各单元接收机的本地混频载波与发射端上变频调制的载波频率一致。当接收站与发射站存在载波频偏(Carrier Frequency Offset, CFO)时，系统的探测性能会受到影响：(1)使子信道之间产生干扰，影响OFDM波形信号子载波的正交性，从而会影响参考信号的重构，而参考信号的重构效果又会直接影响杂波抑制和匹配滤波的效果 [9] ；(2)影响监测信号中直达波和多径回波与参考信号之间的时域相关性，从而会影响杂波抑制效果 [9] 。
外辐射源雷达接收系统与发射系统采用的时钟源相互独立，那么，发射端数模转换的采样时钟频率与接收端模数转换的采样时钟频率不可避免地存在偏差。在实际情况下，对外辐射源雷达来说，一般认为发射系统采样时钟频率高度精确稳定。此时，采样率偏差为接收系统实际采样率与标称采样率的偏差，在时域上表现为一定时间内采样数据点数与理论值有偏差。该偏差同样对系统探测性能存在不利影响：(1)使OFDM信号帧起始位置与采样时刻不对应，导致同步算法计算结果错误，影响参考信号的重构 [10] ；(2)使杂波对消后残余能量变多，同时也会降低重构信号和目标信号的相关积累增益 [11] 。
2.2 时间同步
对于传统主动双基地雷达，准确测距的前提之一是接收系统与发射系统间有相同的时间参考，在主动多基地雷达中，还要求各发射站之间和各接收站之间时间同步。外辐射源雷达则有所不同，其发射系统不受控且发射信号未知，因此无法也无需实现接收站与发射站之间的时间同步。外辐射源雷达通过在接收端设置参考通道获取参考信号，将其提纯后与监测通道信号作匹配滤波计算来获取距离-多普勒谱。对于分布式数字广播电视外辐射源雷达系统而言，各发射站间的时间同步已由开发商完成，因此只需要考虑各接收站之间的时间同步。
分布式外辐射源雷达多站时间同步精度首先影响目标的双基距离，在时差定位时进一步影响定位精度。以单发多收结构为例。若多站时间同步精确，各单元接收站与照射源构成的双基地椭圆交于一点，即能准确定位目标 [12] 。若多站时间同步存在偏差，则各单元接收站与照射源构成的双基地椭圆并不交于一点，而是由椭圆两两相交形成一个目标区域，由此导致目标定位不精确。
分布式外辐射源雷达进行目标定位时，若每个单元接收站均能获得良好的参考信号，那么各单元接收站都能准确测得目标双基地距离。若各站存在时间同步偏差 τ ，那么，各站对目标定位偏差由目标速度 υ 与 τ 的乘积表征。然而，在实际环境中，各接收站所在位置的环境差异性很大，导致所获取的参考信号质量有好有坏，甚至于部分接收站所获取的参考信号重构效果极差而无法使用，那么需选用其中一个接收站的高质量参考信号作为其他接收站的参考信号，才能实现多站联合探测目标。此时，各站对目标定位偏差由电波传播速度c与 τ 的乘积表征 [13] 。显然，实际情况下分布式外辐射源雷达系统对各接收站时间同步精度提出了更高的要求。
3. 同步方案设计
3.1 同步系统
一般情况下，多基地外辐射源雷达系统接收站与发射站的空间位置分布稀疏且相距较远，采用全球卫星导航系统(Global Navigation Satellite System, GNSS)授时技术是较符合实际且精度较高的同步方案 [14] 。其中，GPS是4种主要GNSS之一，应用最广泛，同时考虑到目前数字广播电视基站发射系统普遍采用基于GPS的同步方式，因此，本文设计的基于数字广播电视分布式外辐射源雷达系统同步方案选用GPS接收模块，为接收机提供精确的同步频率源和时钟源，使各接收站联合形成一个统一的雷达探测网。系统如 图2 所示，GPS接收模块获取GPS定位与时间信息，并产生与GPS卫星时间高度同步的1PPS和用1PPS驯服恒温晶振而得到的高精度高稳定性10 MHz时钟信号。把该驯服时钟作倍频处理后，用作雷达接收机的工作时钟，使分布式外辐射源雷达系统实现频率同步。同时，各单元接收机响应1PPS信号上升沿，产生启动采样指令，使分布式外辐射源雷达系统实现时间同步。
3.2 GPS接收模块
系统选用的GPS接收模块是某款集成了恒温高稳晶振OCXO和高精度授时型GPS OEM板，采用大规模集成电路和GPS频率测控技术，产生并发送精确稳定的定时信号(1PPS)和频率信号(10 MHz)，为系统提供高精度的时间和频率参考信号。 表1 为输出10 MHz频率信号参数， 表2 为1PPS信号参数。
表 1 10 MHz频率信号参数Table 1. Parameters of the 10 MHz signal参数 参数值 准确度 <10 –12 (24小时平均值) 保持精度 <5×10 –12 (GPS断开，24小时内) 每秒稳定度 <10 –11 相位噪声 –80 dBc/Hz@1 Hz; –140 dBc/Hz@1 kHz 表 2 1PPS信号参数Table 2. Parameters of the 1PPS signal参数 参数值 授时精度 30 ns (RMS) 上升沿时间 <10 ns 占空比 1:1 4. 同步测试原理
4.1 辐射源信号选择
在前述的国内3种已投入商用的数字广播电视中，CMMB因其特殊的信号帧结构 [15] ，可以用来验证分布式外辐射源雷达系统同步性能。CMMB信号帧结构如 图3 所示，定义1帧信号时长为1 s，并将其划分为40个时隙，每个时隙长25 ms，又可划分为1个CMMB信标和53个OFDM符号。其中信标是同步的关键，它包括1个发射机标识信号(TxID)以及2个内容完全一样的同步信号，同步信号存在于每个时隙的起始位置，时长为409.6 μs。OFDM符号的有效子载波分配为数据子载波、离散导频和连续导频，其中连续导频中包含时隙号信息，离散导频包含384个子载波。
4.2 频率同步测试原理
4.2.1载波同步 利用CMMB同步信号的时域相关性和频域共轭特性可估计接收机系统的CFO [16] 。先对采样数据进行符号粗同步，找到CMMB时隙中同步信号的大概位置，然后利用同步信号进行CFO估计，计算出采样信号CFO值。
符号粗同步是利用加窗检测峰值的方法捕获同步信号的大致位置 [17] ，表达式为：
R(n)=|n+Nsync∑i=nx(i)×x∗(i+Nsync)|,1≤n≤2N (1) 其中， n 对应采样点的序号， N sync为同步信号的长度， x ( i )是采样得到CMMB信号， N 为1个时隙的CMMB信号长度。滑动相关后的峰值点位置即为粗同步所捕获的同步信号的起始位置。
捕获到同步信号位置后，取出两个同步信号为 x 1( n )和 x 2( n )，在存在载波偏差 Δ f 的情况下， x 1( n )和 x 2( n )表达为：
x1(n)=sync(n)exp(j2πΔfnTb/Nb+jΔφ) +awgn(nTb/Nb) (2) x2(n)=sync(n)exp(j2πΔf(n+Nb)Tb/Nb+jΔφ)+awgn(nTb/Nb) (3) 其中，sync( n )为发射端产生的同步信号， T b为同步信号时长， N b为同步信号采样点数， Δφ 为信号中固定相位偏转，awgn( n )为噪声。信号信噪比较高时，忽略噪声的影响，对 x 1( n )和 x 2( n )按式(1)运算有：
R=Nb∑n=1x∗1(n)x2(n)=exp(j2πΔfTb)Nb∑n=1|sync(n)|2 (4) 对 R 求相位得angle( R )，则频偏表达式为：
Δf=angle(R)2πTb (5) angle( R )的范围为[– π , π )，归一化频偏值范围为[–0.5, 0.5)，将这种方法估计得到的频偏值称作小数倍频偏，所估计频偏值最大为同步信号子载波间隔的一半，当频率偏差继续增大时，需再估计整数倍频率偏差 ΔF 。在粗同步有 m 个样值偏差的情形下，此时 x 2( n )写为：
x2(n)=sync(n−m)exp(j2πΔF(n−m)Tb/Nb+jΔφ)+awgn(nTb/Nb) (6) 忽略噪声影响，对式(6)作FFT处理，得到频域同步信号为：
X2(k)=exp(jΔφ)exp(j2πkm/Nb)⋅SYNC(⟨k−ΔF/Tb⟩Nb) (7) 其中，SYNC为sync对应的频域信号。由式(7)可见，整数倍的频率偏差会造成频域数据的循环移位，样值偏差则会引起频域数据的线性相位旋转。利用频域同步信号前后采样点共轭特性消除样值偏差后，将接收的频域同步信号与本地已知的同步信号进行循环移位相关，则可以求出整数倍频率偏差 ΔF 的大小， ΔF + Δf 即系统的总载波频偏。
4.2.2 采样率同步 采样率不准确会引起实际采样数据点数与理论值偏差。对于CMMB信号，当标称采样率为10 MHz时，理论上一帧信号有10 7 个采样数据。由上述载波频偏估计算法算得采样信号的CFO后，先补偿该CFO，然后进行符号精同步可以得到同步信号准确位置，计算相邻信号帧第1个同步峰间隔的数据点数，与理论值比较，即可验证采样率同步性能。
补偿采样信号的CFO后，进行符号精同步。文献[ 16]已证明，CMMB时域同步信号 x b( k )具有共轭对称的特性：
x∗b(k)=xb(Nb−k),k≠0 (8) 且同步信号一共存在4对长为1024点的共轭对称的数据块，基于共轭数据块的相关结果呈现离散单峰的特性，可以准确地找到时隙同步位置。选取第1和第4个共轭数据块作相关：
R(k)=1024−1∑n=0xb(k+n)xb(k+4096−n) (9) k 为滑动相关的起始点。同步信号的能量为：
P(k)=1024−1∑n=0(|xb(k+n)|2+|xb(k+4096−n)|2) (10) 最强峰值对应准确的同步位置 k ML为：
kML=argmaxk(|R(k)|2/P(k))2 (11) 4.3 时间同步测试原理
发射站信号到达各单元接收站的时间差与相应收发对间距离差成正比关系，比例系数为电波传播速度。通过GPS接收模块获取发射站与接收站的位置信息，可计算得收发站间距离差，进而计算得信号到达各单元接收站的理论时间差。然后通过分析各单元接收站的采样数据，计算出信号到达各单元接收站的量测时间差。比较量测时间差与理论时间差即可验证系统时间同步性能。
采样数据序列经过精同步处理后得到的最强峰位置 k ML与采样数据起点的时间差为：
Δt=kML/fs (12) 其中， f s为系统采样率。以 图4 所示站位场景为例，发射站标记为Tx，选取两个单元接收站，分别标记为Rx m , Rx n 。取距Tx最远的单元接收站Rx m 为参考，接收站Rx m , Rx n 采样数据中同一帧中同一时隙的同步信号相关峰值位置分别记为 k ML, m , k ML, n 。 k ML, m , k ML, n 与各自采样数据起点的时间差记为 Δtm , Δtn , 那么信号到达Rx m , Rx n 量测时间差为：
Δtmn=Δtm−Δtn=kML,m−kML,nfs (13) 通过GPS定位信息可以获知Rx m , Rx n 到Tx的基线距离差 ΔLmn ，电波传播速度记为c，取值3×10 8 m/s，由此计算得信号到达Rx m , Rx n 的理论时间差 Δt′mn 为：
Δt′mn=ΔLmnc (14) Δtmn 与 Δt′mn 的误差 εmn 为：
εmn=|Δtmn−Δt′mn| (15) 5. 分布式多站实验及结果
5.1 实验场景
为测试该分布式外辐射源雷达系统同步性能，武汉大学于2016年7月在江西省南昌市开展了多站实验。实验系统包含3个单元接收站和1个非合作照射源，系统参数如 表3 所示。实验站位场景图如 图5 所示，其中3个接收站单元Rx1, Rx2, Rx3分别位于南昌大学前湖校区、华东交通大学、江西农业大学，照射源Tx位于江西省电视台，各接收站参考通道天线均正对照射源，监测通道天线指向同一目标区域。根据GPS获取的各站点经纬度计算得Rx1, Rx2, Rx3距照射源Tx的距离量测值分别为12.714 km, 9.081 km, 13.749 km。
表 3 实验系统参数Table 3. Parameters of the experiment system参数 参数值 Rx1位置 (E115.7941°, N28.6643°) Rx2位置 (E115.8653°, N28.7406°) Rx3位置 (E115.8277°, N28.7676°) Tx位置 (E115.9234°, N28.6767°) 采样率 10 MHz 载波中心频率 714 MHz 信号带宽 8 MHz 5.2 实测数据处理结果
5.2.1 频率同步——载波同步 分析各站连续100帧实测数据，每一帧做一次频偏估计，计算得到接收系统的载波频偏如 图6 所示。可以看出，接收系统的载波频偏小于1 Hz。文献[ 8]提出，单频网中各发射机频率精度偏差为1 Hz是可行的，因此本设计实现的载波同步精度满足基于此的分布式外辐射源雷达的同步需求。通过后续CFO补偿可以进一步减小该CFO对杂波抑制和目标检测的影响。
5.2.2 频率同步——采样率同步 分析各站连续100帧实测数据，计算相邻信号帧的第1个同步峰间隔采样点数与理论值10 7 的偏差，结果如 图7 所示，偏差均为0。这表明，接收站实际采样率相对标称采样率的误差远小于10 –7 ，完全满足基于数字广播电视外辐射源雷达接收系统的采样率同步需求。
5.2.3 时间同步对 3个接收站多次同时启动采样的数据做同步分析，以距照射源Tx最远的接收站Rx3的同步峰位置为参考，接收站Rx1, Rx2与Rx3同步峰间隔采样点数分别稳定为35, 156，三站同步峰相对位置如 图8 所示，根据式(13)计算信号到达Rx1, Rx2, Rx3的量测时间差 Δt31 , Δt32 ：
Δt31=(kML3−kML1)/fs=35107s=3.5×10−6s Δt32=(kML3−kML2)/fs=156107s=1.56×10−5s 根据GPS定位信息，以接收站Rx3为参考，根据式(14)计算可得信号到达Rx1, Rx2, Rx3的理论时间差为：
Δt′31=ΔL31c=13.749km−12.714kmc=3.450×10−6s Δt′32=ΔL32c=13.749km−9.081kmc=1.556×10−5s 将量测时间差与理论时间差对比，三站时间同步误差分别为：
ε31=|Δt31−Δt′31|=5×10−8s ε32=|Δt32−Δt′32|=4×10−8s 可以看出，该误差量级为10 –8 ，且误差值非常接近GPS接收模块的标称定时精度，表明系统时间同步设计性能良好，符合基于数字广播电视分布式外辐射源雷达接收系统的同步需求。需要指出的是，上述分析所得误差不仅来源于多站同步时间误差，还来源于GPS接收模块定位误差和接收机采样的时间量化误差，但此结果仍能说明该分布式外辐射源雷达系统的时间同步性能良好。
图 8 三站同步信号相对位置Figure 8. Relative positions of the synchronization signals received by the receivers6. 结束语
本文针对数字广播电视发射系统的同步原理，设计并实现了基于此照射源的分布式外辐射源雷达系统同步方案。该方案采用GPS接收模块为接收系统提供精确稳定的10 MHz频率源和1PPS定时信号，以实现雷达接收站与照射源、接收站与接收站之间的时间、频率同步。然后利用CMMB信号帧结构的特殊性，提出了一种同步性能测试方法，结合实验实测数据分析，验证了该同步方案基本满足分布式外辐射源雷达系统的同步需求。另外，分布式外辐射源雷达系统工作时，往往默认发射站之间同步性能良好，然而实际中难免存在发射站间频率和时间异步情况，此时，利用本文设计的同步性能良好的接收系统，通过文中提出的测试方法，同样可以测试发射站间的同步情况。
致谢: 论文相关实验得到南昌大学王玉皞教授、赵志欣博士、洪升博士的协助，在此表示感谢！ -
图 1 分布式多传感器多目标跟踪流程图
Figure 1. Flowchart of distributed multi-sensor multi-target tracking
图 5 Registration results before and after registration in the WGS84 coordinate system [ 82]
表 1 典型的多目标跟踪方法性能对比
Table 1. Performance comparison of different multi-target tracking methods
多目标跟踪类型 跟踪方法 跟踪精度 运算量 数据关联类 GNN 低 低 JPDA 中等 中等 JIPDA 中等 中等 MHT 高 高 随机有限集类 PHD 低 低 CPHD 中等 中等 TPHD 中等 低 TCPHD 中等 中等 MeMBer 中等 中等 PMBM 高 中等 GLMB 高 高 LMB 高 中等 
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表 2 多传感器时间配准方法性能对比
Table 2. Comparison of multi-sensor time registration methods
配准类型 配准方法 配准精度 计算量 目标运动状态 插值类 内插外推 较低 低 匀速 曲线插值 中等 较高 匀速\非匀速 曲线拟合 中等 较高 匀速\非匀速 参数估计类 最小二乘 中等 中等 匀速 卡尔曼滤波 较高 较高 匀速\非匀速
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表 3 多传感器非合作目标空间配准方法分类
Table 3. Classification of multi-sensor spatial registration methods based on non-cooperative targets
配准方法 实时性 是否能估计
目标位置参与传感器
个数传感器类型 RTQC 离线 否 两个 同类 LS 离线 否 两个 同类 GLS 离线 否 两个 同类 EML 离线 是 两个 同类 KF 在线 是 多个 同类 RFS 在线 是 多个 同类 MLR 离线 是 多个 同类\异类 RBER 离线 是 多个 同类\异类
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表 5 多传感器估计融合方法对比
Table 5. Comparison of multi-sensor estimation fusion methods
估计融合方法 是否考虑
航迹相关计算量 融合
精度传感器
类型简单凸组合融合 否 低 低 同类 Bar-Shalom-Campo融合 是 较高 较高 同类 基于MAP 是 较高 较高 同类 CI融合 是 较高 较高 同类 GCI融合 是 较高 较高 同类/异类 AA融合 是 较高 较高 同类/异类 基于EKF融合 否 较高 较高 同类/异类 基于UKF融合 否 较高 较高 同类/异类 基于PF融合 否 高 高 同类/异类
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表 6 典型的多传感器多目标跟踪方法性能对比
Table 6. Performance comparison of multi-sensor multi-target tracking methods
多目标跟踪类型 融合准则 跟踪方法 运算量 跟踪精度 数据关联类 CI融合 JPDA 中等 中等 简单凸组合融合 MHT 高 中等 随机有限集类 GCI/GA融合 PHD 低 低 CPHD 中等 中等 MeMBer 中等 中等 GLMB 高 高 LMB 中等 高 AA融合 PHD 低 低 CPHD 中等 中等 MeMBer 中等 中等 GLMB 高 高 LMB 中等 高
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表 1 Performance comparison of different multi-target tracking methods
Types of multiple target tracking Tracking methods Tracking accuracy Computational complexity Data association GNN Low Low JPDA Medium Medium JIPDA Medium Medium MHT High High Random finite set PHD Low Low CPHD Medium Medium TPHD Medium Low TCPHD Medium Medium MeMBer Medium Medium PMBM High Medium GLMB High High LMB High Medium
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表 2 Comparison of multi-sensor time registration methods
Registration types Registration methods Registration accuracy Computational complexity Target motion state Interpolation class Interpolation and extrapolation Lower Lower Uniform Curve interpolation Medium Higher Uniform \ Non-uniform Curve fitting Medium Higher Uniform \ Non-uniform Parameter estimation Least squares Medium Medium Uniform Kalman filter Higher Higher Uniform \ Non-uniform
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表 3 Classification of multi-sensor spatial registration methods based on non-cooperative targets
Registration methods Real-time Can the target position be estimated? Number of sensors Sensor type RTQC Offline No Two Homogeneous LS Offline No Two Homogeneous GLS Offline No Two Homogeneous EML Offline Yes Two Homogeneous KF Online Yes Multiple Homogeneous RFS Online Yes Multiple Homogeneous MLR Offline Yes Multiple Homogeneous \ Heterogeneous RBER Offline Yes Multiple Homogeneous \ Heterogeneous
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表 5 Comparison of multi-sensor estimation fusion methods
Estimation fusion method Whether to consider track correlation Computational
complexityFusion accuracy Sensor type Simple convex combination fusion No Low Low Homogeneous Bar-Shalom-Campo fusion Yes Higher Higher Homogeneous MAP fusion Yes Higher Higher Homogeneous CI fusion Yes Higher Higher Homogeneous GCI fusion Yes Higher Higher Homogeneous \ Heterogeneous AA fusion Yes Higher Higher Homogeneous \ Heterogeneous EKF fusion No Higher Higher Homogeneous \ Heterogeneous UKF fusion No Higher Higher Homogeneous \ Heterogeneous PF fusion No High High Homogeneous \ Heterogeneous
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表 6 Performance comparison of multi-sensor multi-target tracking methods
Types of multiple target tracking Fusion criteria Tracking methods Computational complexity Tracking accuracy Data association CI Fusion JPDA Medium Medium Simple convex combination fusion MHT High Medium Random finite set GCI/GA fusion PHD Low Low CPHD Medium Medium MeMBer Medium Medium GLMB High High LMB Medium High AA fusion PHD Low Low CPHD Medium Medium MeMBer Medium Medium GLMB High High LMB Medium High
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