Adaptive detection probability for mmWave 5G SLAM

In 5G simultaneous localization and mapping (SLAM), estimates of angles and delays of mm Wave channels are used to localize the user equipment and map the environment. The interface from the channel estimator to the SLAM method, which was previously limited to the channel parameters estimates and their uncertainties, is here augmented to include the detection probabilities of hypothesized landmarks, given certain a user location. These detection probabilities are used during data association and measurement update, which are important steps in any SLAM method. Due to the nature of mm Wave communication, these detection probabilities depend on the physical layer signal parameters, including beamforming, precoding, bandwidth, observation time, etc. In this paper, we derive these detection probabilities for different deterministic and stochastic channel models and highlight the importance of beamforming

Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method

Low-Complexity 5G Slam with CKF-PHD Filter

In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypothesis density (PHD) map representation exhibits high complexity. This paper proposes a new implementation method for the 5G SLAM using message passing (MP) and the cubature Kalman filter (CKF). We demonstrate that the proposed method significantly reduces the complexity while retaining the SLAM accuracy of the RBPF-PHD approach

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.Comment: This work has been accepted in the IEEE Transactions on Wireless Communication

Cooperative mmWave PHD-SLAM with Moving Scatterers

Using the multiple-model~(MM) probability hypothesis density~(PHD) filter, millimeter wave~(mmWave) radio simultaneous localization and mapping~(SLAM) in vehicular scenarios is susceptible to movements of objects, in particular vehicles driving in parallel with the ego vehicle. We propose and evaluate two countermeasures to track vehicle scatterers~(VSs) in mmWave radio MM-PHD-SLAM. First, locally at each vehicle, we generate and treat the VS map PHD in the context of Bayesian recursion, and modify vehicle state correction with the VS map PHD. Second, in the global map fusion process at the base station, we average the VS map PHD and upload it with self-vehicle posterior density, compute fusion weights, and prune the target with low Gaussian weight in the context of arithmetic average-based map fusion. From simulation results, the proposed cooperative mmWave radio MM-PHD-SLAM filter is shown to outperform the previous filter in VS scenarios

PMBM-Based SLAM Filters in 5G mmWave Vehicular Networks

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them

A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness. Large bandwidths combined with large antenna arrays provide unparalleled delay and angle resolution, allowing high accuracy localization but also building up a map of the environment. Even the most basic filter intended for simultaneous localization and mapping exhibits high computational overhead since the methods rely on sigma point or particle-based approximations. In this paper, a first order Taylor series based Gaussian approximation of the filtering distribution is used and it is demonstrated that the developed extended Kalman probability hypothesis density filter is computationally very efficient. In addition, the results imply that efficiency does not come with the expense of estimation accuracy since the method nearly achieves the position error bound.submittedVersionPeer reviewe