Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson, Kuo-Chu Chang, Ting Yuan, Lennart Svensson

This correspondence presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inherent uncertainty in probabilistic multi-object representations. Additionally, P-GOSPA retains the interpretability of GOSPA, such as its decomposition into localization, missed detection, and false detection errors in a sound and meaningful manner. Examples and simulations are provided to demonstrate the efficacy of the proposed P-GOSPA metric.

Xiaojun Chen, KC Chang, Ting Yuan

Autonomous systems pose unique challenges for sensor fusion applications. In multi-sensor scenarios, a real-time data geometric alignment system, from initial online calibration to instant data consistency evaluation, is highly desirable for effective and efficient deployment of autonomy solutions. In this paper, we present an entropy-based real-time geometric alignment system for Radar-Lidar point cloud sensor fusion. The online alignment system is targetless and relies solely on multi-sensor point cloud measurements to form an entroy-based test statistics, requiring no prior information about the perception environment. Specifically, we design a finite mixture model (FMM) as empirical probability density function (PDF) to represent environment as a probabilistic world model. A proper entropy measure of the empirical PDF according to the perception world is then introduced to evaluate the FMM randomness. It can be observed that, even in a generally nonstationary environment, both Radar and Lidar point clouds can still converge to an optimal entropy. The gradual fluctuation of this entropy measure over time can serve as a data consistency metric, enabling the detection of sudden sensor drifts. A scenario study is carried out to evaluate and validate the effectiveness and efficiency of the proposed real-time point-cloud alignment system in real world environments.

Ting Yuan, Wenqi Cao, Shuqi Zhang...

Autonomous driving poses unique challenges for vehicle environment perception systems. It is highly desirable that we utilize existing vehicle-equipped driver-assistant sensors, without hardware change, to achieve driverless performance. Current product level vehicle surround view camera module (denoted concisely as SVS) is served as a panoramic view visual aid tool for low-automation applications. With proper statistical analysis, the multiple mono-camera information can be very useful for higher vehicle intelligence without significant hardware change. In this study, we focus on lane detection and estimation from a SVS only system.

Wenqi Cao, Giorgio Picci, Anders Lindquist

We study modeling and identification of stationary processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps, one of which can be based on standard algorithms while the other is based on a deterministic least squares fit. Identifiability of the feedback system is analyzed and a unique identifiable structure is characterized. Simulations show that the proposed procedure works well in some simple examples.

Anders Lindquist

I first met Rudolf Kalman in Vienna, Austria, in the spring of 1972. I had recently finished my Ph.D. at the Royal Institute of Technology, Stockholm, Sweden, and I was invited to give a talk on my recent results in stochastic control theory at a small workshop that Kalman also attended. Apparently, Kalman was favorably impressed with my talk because he took me out for dinner the same evening and immediately invited me to come to Florida for the coming academic year. Kalman had just moved from Stanford to the University of Florida, and this is how I became his first postdoctoral associate at his new Center for Mathematical Systems Theory in the fall of 1972.

Ting Yuan, Yaakov Bar-Shalom and Xin Tian

Track-to-track fusion using estimates from multiple sensors can achieve better estimation performance than a single sensor. If the local sensors use different system models in different state spaces, the problem of heterogeneous track-to-track fusion arises. Compared with homogeneous track-to-track fusion that assumes the same system model for different sensors, the heterogeneous case poses two major challenges.