Adaptive track initiation remains a crucial component of many modern multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A naive construction of this adaptive birth set density results in an exponential number of newborn components in the number of sensors. A truncation procedure was provided that leverages a Gibbs sampler to truncate the birth density, reducing the complexity to quadratic in the number of sensors. However, only a limited discussion has been provided on additional algorithmic techniques that can be employed to substantially reduce the complexity in practical tracking applications. In this paper, we propose five efficiency enhancements for the labeled random finite sets multi-sensor adaptive birth procedure. Simulation results are provided to demonstrate their computational benefits and show that they result in a negligible change to the multi-target tracking performance.

Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance.

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor $\delta$-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.

The multi-armed bandit formalism has been extensively studied under various attack models, in which an adversary can modify the reward revealed to the player. Previous studies focused on scenarios where the attack value either is bounded at each round or has a vanishing probability of occurrence. These models do not capture powerful adversaries that can catastrophically perturb the revealed reward. This paper investigates the attack model where an adversary attacks with a certain probability at each round, and its attack value can be arbitrary and unbounded if it attacks. Furthermore, the attack value does not necessarily follow a statistical distribution. We propose a novel sample median-based and exploration-aided UCB algorithm (called med-E-UCB) and a median-based ϵ-greedy algorithm (called med-ϵ-greedy). Both of these algorithms are provably robust to the aforementioned attack model. More specifically we show that both algorithms achieve O(log T) pseudo-regret (i.e., the optimal regret without attacks). We also provide a high probability guarantee of O(log T) regret with respect to random rewards and random occurrence of attacks. These bounds are achieved under arbitrary and unbounded reward perturbation as long as the attack probability does not exceed a certain constant threshold. We provide multiple synthetic simulations of the proposed algorithms to verify these claims and showcase the inability of existing techniques to achieve sublinear regret. We also provide experimental results of the algorithm operating in a cognitive radio setting using multiple software-defined radios.

Rapid growth of radio traffic in the unlicensed spectrum has led to challenges in securing sufficient resources for reliable communications. This problem is compounded by the emergence of uncooperative and even adversarial users which can interfere with the network fidelity of existing secondary users. In this paper, we propose a DSA policy using a decentralized sample-median based exploration-aided UCB (DMA med-E-UCB) and a decentralized sample-median based epsilon greedy (DMA med-E-greedy). Both algorithms are applied in a distributed spectrum sharing cognitive radio network and able to defend against an adversarial attacker with arbitrarily large interference power. We model the proposed spectrum access problem as a multi-armed bandit and show that both algorithms are robust to adversarial attacks. Provided that the attack occurs infrequently, the regret achieved by both algorithms is O(logT) with high probability, where T is the number of sequential channel access attempts. We show that our algorithms outperform other standard distributed policies and verify our results using an over-the-air software-defined-radio testbed.

This paper addresses the problem of decentralized, outdoor formation coordination with multiple quadcopters. The problem is formulated as a receding horizon, mixed-integer non-linear program (RH-MINLP). Each quadcopter solves this RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while coordinating its position in a desired formation with other quadcopters. Constraints on quadcopter kinematics, dynamics, collision avoidance, wireless communication connectivity, and geometric formations are modeled. Communication connectivity is modeled as a constraint on maximum separation distance based on a minimum viable received signal strength in the presence of path loss attenuation. The resulting RH-MINLP is non-convex, and is solved using an outer-approximation branch and bound solver with a warm-starting scheme. The framework is validated via Hardware-in-the-Loop (HITL) and outdoor flight test with up to 6 quadcopters. Results demonstrate the effect of number of quadcopters and formation type on total transit time. Average radio packet loss statistics during transit indicate robust network performance for a round robin communication scheduling scheme.

Passive multi-target tracking applications require the integration of multiple spatially distributed sensor measurements to distinguish true tracks from ghost tracks. A popular multi-target tracking approach for these applications is the particle filter implementation of Mahler's probability hypothesis density (PHD) filter, which jointly updates the union of all target state space estimates without requiring computationally complex measurement-to-track data association. Although this technique is attractive for implementation in computationally limited platforms, the performance benefits can be significantly overshadowed by inefficient sampling of the target birth particles over the region of interest. We propose a multi-sensor extension of the adaptive birth intensity PHD filter described in (Ristic, 2012) to achieve efficient birth particle sampling driven by online sensor measurements from multiple sensors. The proposed approach is demonstrated using distributed time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements, in which we describe exact techniques for sampling from the target state space conditioned on the observations. Numerical results are presented that demonstrate the increased particle density efficiency of the proposed approach over a uniform birth particle sampler.

This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a certain positive constant. The convergence analysis of the proposed algorithms and upper bounds on the error probability are provided for both known and unknown number of clusters. More importantly, it is shown that the probability of error decays exponentially as the sample size of each data sequence goes to infinity, and the error exponent is only a function of the pairwise KS distances of the distributions. the analysis is validated by simulation results.

Reverberation chambers are closed reflective spaces that can emulate highly reverberant electromagnetic environments. The electromagnetic environment is primarily determined by the size of the cavity, effective conductivity, and leakage via apertures. In this effort, we investigate the performance of wireless OFDM communications in relation to the latter two by controlling the loading of a reverberation chamber and the effective aperture into a coupled cavity. A software defined radio measurement platform was used to assess the communication performance through a selection of link-level metrics including error vector magnitude, post processing signal-to-noise ratio, and throughput. The degradation of link quality is quantified for increasingly diffuse environments, as well as the improvement when leveraging a maximal ratio combining receiver diversity scheme. The link quality was found to improve in both the reverberation chamber and the coupled cavity for larger effective apertures. This result was analyzed using a time-dependent model for RF propagation in coupled cavities.