complicates the derivation of an appropriate tracking algorithm.
In this chapter the fundamentals of BML are introduced and open questions in exist-ing work are identified to motivate the contribution of Chapters 6 and 7.
This chapter is structured as follows. In Section 5.1 the fundamentals of BML are formulated. First, the boundary conditions of BML are explained in Section 5.1.1.
Then, properties of the path propagation and methods for simulating the propaga-tion of the radio channel, that is, ray tracing simulapropaga-tions and their limitapropaga-tions for the application within BML data fusion algorithms are explained in Section 5.1.2. The frameworks of BML for simulated and real world data, consisting out of a mobile antenna array, algorithms for blind channel estimation, a sophisticated ray tracing simulation and the sensor data fusion algorithms, which are studied in this thesis, are presented in Section 5.1.3. In Section 5.2 the related work on BML is discussed.
Finally, limitations and open questions of existing work are identified and used to formulate and motivate the main contribution of the second part of this thesis in Section 5.3.
Figure 5.1:Visualization of the BML Scenario. A single OS equipped with an antenna array tries to localize and track an MS using only the received electromagnetic waves (multipaths).
Due to an explicit exploitation of the multipaths via context information coming from a ray tracer, a BML tracking algorithm is able to track and localize an MS under LoS as well as NLoS conditions.
only the physically received electromagnetic signal is processed. Hence, blind channel estimation techniques [HKT15] have to be applied for the multipath characterization.
Since the signal is not known, the absolute lengths and runtimes of the signal’s paths cannot be estimated. Instead, a multipath is characterized (among other quantities like the azimut angle of arrival (AoA) and elevation angle of arrival (EoA)) by its relative time of arrival (RToA).
2. ”...of a mobile station...” The target, which is an electromagnetic emitter and referred to as MS, moves, which implies that not only localization but also track-ing algorithms have to be derived. The electromagnetic waves are emitted radially by the MS. According to [Alg10] BML multitarget scenarios reduce without loss of generality to single target scenarios, due to the fact that the multipaths emitted by each target are separable with respect to their signal form. However, in case of non–
separability all target tracking algorithms presented in Chapter 6 can easily be applied to multitarget scenarios.
3. ”...in urban scenarios...”The challenge of BML is situated in an urban environ-ment, that is, in a densely built–up area. Since the radially emitted electromagnetic signal of the MS is transformed by reflection, diffraction and scattering during its
propagation, the antenna array of the OS receives multiple signals, which are called multipaths. In conventional localization techniques multipaths deteriorate the esti-mation process and thus target state estimates are only valid under line of sight (LoS) conditions, which implies that such approaches have a limited applicability in urban environments. In contrast to that, BML explicitly exploits the received multipaths by the incorporation of context information of the urban environment in terms of a ray tracer prediction. Due to the application of assignment techniques BML data fusion algorithms are capable to localize and track the MS either under LoS or non–line of sight (NLoS) conditions [Alg10].
4. ”...using a single moving observer station equipped with an antenna ar-ray.” A single moving OS implies that the ray tracing prediction needs to be done continuously, that a mission planning (an online estimation of the optimal OS route in terms of target state estimation accuracy) can be performed and that the carried antenna array has to be rather small. An array of antennas has to be used to resolve the multipaths and to characterized them in terms of their AoA, the EoA and the RToA. However, the number of resolvable multipaths is restricted by the number of elements of the antenna array. For example, the antenna array used for recording the real world data presented in Section 6.4 has five elements. Details on antenna arrays can be found in [Bro12] and [Alg10].
In Chapter 6 and Chapter 7 the purpose is to answer open questions with respect to the aspects of target tracking in a BML scenario. Therefore, the processing of the signal, that is in particular, the blind channel estimation of the received electromag-netic signal to characterize the multipaths in terms of their AoA, RToA and the EoA is outside the scope of this thesis. For details on the blind channel estimation the interested reader is referred to the literature. An overview over blind channel estima-tion techniques is given in [ZT95] and [TP98]. The parameter estimaestima-tion used within this thesis is presented in [HKT15], which is based on [GS96], [YS94] and [TXK94].
5.1.2 Path Propagation and Ray Tracing
According to the boundary conditions given above, the localization and tracking of an electromagnetic emitter in an urban environment implies that the emitted signal is transformed into multiple signals (each characterized by AoA, EoA and RToA). The multipath propagation is caused by essentially three physical effects, that is, reflection, diffraction and scattering. These effects are described in [Alg10, Section 2.5.2] and standard literature on the propagation of electromagnetic waves. The mathematical model of the split–up signal is usually given by the impulse response, which consists out of a sum of Dirac pulses (see [Alg10, Equation (2.1)]). A detailed discussion on the impulse response can be found in [Alg10] and the references cited therein.
Figure 5.2: Visualization of the field strength prediction of a ray tracing simulation: For a given observer (black cross) – mobile station (antenna) constellation the color at the emitter location indicates the received field strength at the observer. Three multipaths are visualized (black solid, block dotted, gray) and the interaction points are plotted as black dots c2013 IEEE.
Ray tracers are well established in the field of network planning and the system design of mobile communication systems. Based on a city map, a ray tracer pre-dicts the set of multipaths that are emitted by the MS and received by the OS.
The ray tracer used for the evaluation of the methods proposed in Chapters 6 and 7 is computationally efficient and accurate in its prediction [HWLW03]. It is based on [HWLW99], [WHL99], [HWLW03] and the fundamentals given in [Gla89]. Figure 5.2 shows the ray tracing prediction in terms of the received field strength in an urban environment including a subset of multipaths for a fixed MS–OS constellation. Figure 6.8 visualizes a ray tracing prediction in terms of the received field strength for the city of Erlangen.
The ray tracer prediction is based on a city and building map of the investigated sce-nario. Problems for any BML localization and tracking algorithm arise if these maps do not represent the reality, which might happen due to inaccurate or non–realistic models of the buildings or the city map. But even if the map information of the ur-ban environment is perfect, static and moving obstacles like cars, trucks, pedestrians, trees, etc. cannot be modeled by the ray tracer and deteriorate the localization and tracking result. Additionally, several challenges for a correct ray tracer prediction are given by the receiver side. First, the fact that in a BML scenario a mobile OS is used implies that the antenna array cannot be arbitrary large. Thus, the number of elements and therefore also the number of resolvable multipaths is restricted.
Fur-Figure 5.3:BML framework used for simulated (single–target) scenarios. Based on the true trajectory of the MS (ground truth), a fixed OS position and the city and building map of the urban environment the ray tracer predicts for each time instance a set of electromagnetic waves. Then, the set of multipaths is used as input for the emulation of an antenna array, which creates the set of measurements for the data fusion algorithm. Within the data fusion algorithm the ray tracer prediction is used for the evaluation of the assignment–based like-lihood function. Ray tracing visualization: c2015 AWE Communications. OS model: c 2015 Saab Medav Technologies GmbH. Data fusion: c2013 IEEE.
thermore, due to fading certain predicted multipaths might not be received. All these effects have to be modeled by a ray tracer to guarantee an accurate prediction of the path propagation.
5.1.3 Blind Mobile Localization Framework
The BML frameworks studied in this thesis are closely related to the frameworks proposed in [Alg10, Figure 2.18] and consist out of four components. First, an antenna array with five–elements, which is carried by the mobile OS together with a receiver and a direction finding software is used. Obviously, this component is only needed if real world experiments are carried out. Within the simulation framework the antenna array output is emulated. Second, a blind channel estimation algorithm, presented in [HKT15], is applied to extract a set of multipaths out of the received signal. Due to the fact that no cooperation between the MS and the OS is available, the estimation characterizes a multipath by its AoA, RToA and the EoA. This component is used within the real world data framework to extract the multipaths. The third component is the ray tracer, which predicts for each OS–MS constellation the set of multipaths based upon the city and building map of the investigated environment. It finds its application within real world scenarios as a database used by the data fusion algorithm
Figure 5.4: Processing chain of a real world BML scenario. After the electromagnetic signal is received by the antenna array, that is carried by the mobile OS, the received signal is processed by the blind channel parameter estimation proposed in [HKT15], which then outputs the set of received multipaths to the data fusion algorithm. The localization and tracking is done analogously to the BML framework used for simulated scenarios. Ray tracing visualization: c2015 AWE Communications. OS model, parameter estimation visualization:
c
2015 Saab Medav Technologies GmbH. Data fusion: c2013 IEEE.
to model the likelihood function, that is, the assessment of hypothetical MS locations.
For the simulation BML framework the ray tracer is additionally used to generate multipaths. For each time instance the ray tracer predicts the set of multipaths for the true position of the MS and the OS location. This set of true multipaths is then used as an input for the antenna emulation. Finally, the fourth component of the BML framework is the tracking algorithm studied in Chapter 6. It fuses the information gathered by the other three components and yields an estimate of the target position (plus some additional information like the number of false multipaths, the probability of the target’s existence and an estimation of the covariance matrix, see Chapter 6).
The data fusion is essentially based upon the idea that the hypothetical MS position which produces the set of predicted multipaths that fits best to the set of received multipaths is the most likely MS location [Alg10]. This incorporation of context information in terms of the ray tracer prediction distinguishes this approach from classical direction finding methods that suffer from multipath propagation and do not work under NLoS conditions. The approach from [Alg10] followed in this thesis exploits the multipath propagation and works either under LoS or NLoS conditions.
In the following, the part of data fusion is studied. A detailed investigation of the remaining components of the BML framework is outside the scope of this thesis. First, related work is discussed and used to formulate the open questions that are answered
in the Chapters 6 and 7.