A Survey on Trust Metrics for Autonomous Robotic Systems

This paper surveys the area of Trust Metrics related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems

Using Both GPS L1 C/A and L1C: Strategies to Improve Acquisition Sensitivity

The upper L-Band will be the only frequency band with two different GPS civil signals available to users at the same carrier frequency with the legacy L1 C/Afcode signal and the new L1C signal. The null-to-null bandwidth of the C/A code signal is 2.046 MHz. The TMBOC modulation of the L1C signal creates bandwidth of 4.092 MHz between the outer nulls of the largest spectral lobes in the split-spectrum signal. Without the need to have two separate radio-frequency chains in the front-end of a GPS receiver, using the GPS C/A and L1C signals will improve acquisition sensitivity with limited additional complexity. This paper explores various techniques for joint acquisition of GPS L1C and L1 C/A code signals. First, the nominal received power of these two signals is discussed along with the power split parameters required for optimal combining. Next, a model for the composite C/A code and L1C signal is presented. The optimal detector for joint acquisition is then derived and simulation results provided. Finally, sub-optimal, but more efficient, techniques are proposed and their performance evaluated by comparing the detection probabilities at a fixed false alarm rate

Analysis of L1C Acquisition by Combining Pilot and Data Components over Multiple Code Periods

One of the new features of modern GNSS signals is that they generally have a pilot component and data component. A unique aspect of the GPS L1C signal is that it has an unequal power split between the pilot and data components. Various papers have pro- posed channel combining techniques to acquire modern GNSS signals using both components. In this paper, the optimal detector for GPS L1C acquisition over multiple code periods without knowledge of the navigation data or overlay code phase is derived. A variation of semi-coherent integration technique (non- coherently combining the 10 msec coherent combinations) that accounts for the unequal power split be- tween the data and pilot components is proposed. Single trial detection and false alarm probabilities are used to compare performance of this semi-coherent integration with unequal power compensation to the optimal detector as well as to noncoherent combining and single channel acquisition on the pilot component only. Simulation results show that the semi-coherent integration with unequal power compensation slightly outperforms both the semi-coherent integration detector without compensating for unequal power and the noncoherent combining detector

Maximum-likelihood GPS parameter estimation

ABSTRACT: Recently we proposed an acquisition process for a maximum-likelihood GPS receiver that considers the joint processing of all GPS satellite waveforms. The resulting estimator was shown to provide an elegant solution to the near -far problem and to perform better than the suboptimal sliding-correlator estimator. However, the proposed acquisition model included only the code search, which estimates just the time of arrival (TOA) between a GPS satellite and a maximum-likelihood GPS receiver. In this paper we enhance the acquisition process by including the estimation of Doppler along with the estimation of the TOA, which results in a two-dimensional Doppler and code search. A maximum-likelihood GPS receiver would require only one front-end hardware section for processing all GPS signals in view, thus simplifying the entire architecture of a GPS receiver. An assessment based on theoretical performance and simulation results indicates that a maximum-likelihood GPS receiver can achieve an order-of-magnitude performance improvement relative to a sliding-correlator GPS receiver. Simulation data will be validated in the near future using GPS acquisition data from the Novatel ProPack AG-G2ϩDB9-RT2 , and the results of this work will be presented in a future publication

A Solution to the Recursive Generalized Eigenvalue Problem

Dr. Matthew Bromberg is an independent consultant. For the last 6 years Dr. Bromberg has been involved in the research and development of array processing algorithms for reuse enhancement for wireless communication systems and for interference mitigation for both commercial and military applications while he was at Radix Technologies. Dr. Bromberg was a key inventor of the technology that led to the formation and funding of Beam Reach Networks and the formation of Protean Radio Networks. He has authored several papers and patents in this area. Abstract Previously, we have discussed a recursive solution to the vector normal equation utilizing the recursive Cholesky and Modified Gram-Schmidt Orthogonalization (MGSO) algorithms. Previously, we have also discussed a blind adaptive approach for detection and extraction of signals of interest in the presence of noise and interference without relying on preamble or training sequences. The heart of the blind adaptive algorithm is based upon solving the recursive generalized eigenvalue problem, the solution of which is discussed in this paper based on the recursive Cholesky or QR factors and the Householder and QL algorithm with implicit shifts. Even though, the solution to the recursive generalized eigenvalue problem is slightly more efficient than the solution to the direct generalized eigenvalue when all the eigenvalues and eigenvectors are required, the improvement is more noticeable when only one eigenvalue and the corresponding eigenvector are required. Therefore, the solution that is proposed here serves well for recursive generalized eigenvalue problems that require only one eigenvalue and the corresponding eigenvector

Analysis of coherent combining for GPS L1C acquisition

The GPS L1C signal is similar to other modern GNSS signals in that it has a pilot component and a data component. However, a unique aspect of this signal is an unequal power split between these two components which are transmitted in phase. Channel combining techniques to acquire modern GNSS signals using both components have previously been proposed. In this paper, the single trial detection and false alarm probabilities for GPS L1C acquisition using coherent channel combining over a single spreading code period are analytically derived taking into account the unique aspects of this signal. The optimal detector for GPS L1C acquisition in additive white Gaussian noise is also derived. A coherent combining technique with unequal power compensation is proposed for dual-component GNSS signals, such as GPS L1C, that have an unequal power split. Simulation results are used to verify the analytical results and to compare the performance of the optimal detector with coherent channel combining as well as noncoherent channel combining. It is shown that coherent channel combining with unequal power compensation outperforms the other detectors and nearly approaches the performance of the optimal detector