The capabilities of a sensing system and its implementation are among the critical factors to determine the overall performance and the subsequent public acceptance of automated/autonomous vehicles. However, the uncertainty for system to work and maintain safety under undesirable conditions, such as inclement weather or low illumination, is still an obstacle to commonly accepted and legitimate applications. The safety of the automated/autonomous vehicle is in many ways determined by the performances of the subsystems. In this paper, two accuracy metrics are proposed to qualitatively assess the safety performance that each sensor can contribute. Upon the analysis of the limitations of all individual sensors, we present several novel approaches to improve the vehicle sensing architecture for achieving more reliable perception and eventually, all-condition safety.

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cramér-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: 1) a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and 2) a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.

It has been widely recognized that structure information helps in sparse signal recovery. In this paper, a general form of block structure is considered, which is often referred to hierarchically sparse model. It is assumed that the unknown sparse signal can be divided into blocks, and a block contains either all zero components or a fraction of nonzero components. This model sits between the standard sparse model (without block structure) and the strict block sparse model (all entries in nonzero blocks are nonzero). The focus of this paper is to analyze the convex optimization approach to recover hierarchically sparse signals. The technique we employed is based on the approximated message passing framework and the associated state evolution. The minimum number of measurements required for exact recovery, also known as phase transition (PT), has been quantified in an asymptotic region. We show that the PT depends on two parameters: the fraction of nonzero components in nonzero blocks, and the uniformity of the magnitudes of nonzero components. Based on the PT analysis, we characterize the regions at which the convex optimization methods designed for the standard, hierarchically, and block sparse models are optimal respectively.

Location-awareness is essential for many wireless network applications in both civil and military sectors. In this paper, we determine the localization accuracy of narrowband localization systems in which each mobile agent is equipped with an antenna array. Due to non-coherent estimators, the phases of the received signals can only be exploited for angle-of-arrival (AOA) estimation but not time-of-arrival (TOA). Based on such estimators, we derive the fundamental localization accuracy in terms of the squared position error bound (SPEB) in far-field harsh multipath environments. Moreover, we characterize the effects of the geometry of anchors and array antennas on the localization accuracy, yielding the criteria for optimal array design and network deployment. Our analysis exploits all the TOA and AOA information in the received waveform for localization using narrowband array-based systems, and the resulting SPEB serves as a fundamental limit for such systems.

Feature selection in Fingerprint-based localization systems is of great importance, because of its capability to reduce the overhead in handling high- dimensional data while ensuring positioning accuracy. Several methods for such a task have been proposed, but they either do not consider the correlation between features, or propose an inefficient method to deal with the correlation. The study in this paper proposes a novel feature selection scheme based on the Sparse multi-class SVM (MSVM) technique which can address the feature selection problem via efficiently handling the correlation among them. The scheme first rules out several "unimportant" features via a simple criterion for scalability, and then selects a portion of the remaining features by controlling the sparsity of the optimization results of the Sparse MSVM. The method is applied to a realistic GSM-based fingerprinting localization system and the experimental results show that it outperforms several previous ones via reducing the mean localization error by about 20%.

This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.

In this paper, we propose a new algorithm, TLS-FOCUSS, for sparse recovery for large underdetermined linear systems, based on total least square (TLS) method and FOCUSS(FOCal Underdetermined System Solver). The problem of sparse recovery when perturbations appear in both the measurements and the dictionary (sensing matrix) is considered. FOCUSS algorithm is extended with main idea of TLS to reduce the impact of the perturbation of dictionary on the performance of sparse recovery. The simulation results illustrate the advantage of TLS-FOCUSS on accuracy and stability compared with ordinary FOCUSS algorithm.

In this letter, a novel radial velocity estimation and range shift compensation algorithm is proposed for high-range resolution profiling of moving targets in stepped-frequency (SF) radar. Compared to traditional methods, this algorithm is based on a more precise signal model, and can therefore achieve much higher estimation accuracy. Furthermore, the range shift problem caused by target motion can be resolved without alterations to the radar waveform. The performance of this algorithm is demonstrated using simulated and experimental results.

This letter presents a theoretical comparative study of the performance bounds for point and extended target tracking. As a result of the additional information provided by the high-resolution sensors, the posterior Cramer-Rao lower bound (PCRLB) of the kinematic target states is proven to be always smaller under the extended target tracking framework than the point target model. Three assumptions about the two kinds of target models, which are satisfied in most target tracking problems, are presented as a sufficient condition of the inequality. This comparison result suggests the use of the extended target model to potentially achieve better performance in tracking applications. The superior performance bound is also illustrated with an elliptically modeled extended target tracking example.

A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our method utilizes the echo statistics on spatial-temporal plane, which is extracted from sample data of only ONE training range cell with Compressed Sensing techniques, to construct a new estimator of covariance matrix, and build the optimal detector based on it. Full description of CS-STAP is given. Numerical result on real data has provided the evidence for great potential of CS-STAP as a effective approach when clutter is non-stationary because it need much less training data compared with common STAP methods.

A method for evaluating both the robustness and detection performance of constant false alarm rate (CFAR) detectors is presented in this paper, which is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. The robustness of different kinds of CFAR detector can be evaluated and compared by calculating the first derivative of the false alarm probability (FAP) at an underlying distribution, which is named IF-FAP. From the analysis of IF-FAP, several conclusions are given here: the robustness of the detector can be asymptotically represented by the IF of the clutter power estimator, while the detection performance can be approximately evaluated by the asymptotic variance of the clutter power estimator which is an explicit functional of the corresponding IF.

The traditional CFAR processors are based on the sliding-window concept, which have substantial performance degradation under nonhomogeneity. Owing to temporal processing and the exploitation of the local homogeneity of the map cell, the clutter-map procedure acquires enhanced robustness with little CFAR losses. In this paper, a Gaussian biparametric clutter-map constant false alarm rate (GBCM-CFAR) processor is proposed which merges the clutter-map technique and noncoherent integration together. It can approximately achieve CFAR independent of the original clutter distribution. The performance in the presence of fast point targets is assessed, in the examples of Weibull and lognormal clutter, in order to elicit the effect of the system parameters. Its performance is close to that of the optimum Neyman-Pearson detector with little CFAR losses in homogeneous environments. It is also suitable to deal with the nonhomogeneous situation.