We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non-negative amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch between the manifold described by the parameters and its polar approximation. To quantify the improvements afforded by the proposed extensions, we evaluate six algorithms for estimation of parameters in sparse translation-invariant signals, exemplified with the time delay estimation problem. The evaluation is based on three performance metrics: estimator precision, sampling rate and computational complexity. We use compressive sensing with all the algorithms to lower the necessary sampling rate and show that it is still possible to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super-resolution algorithm. The algorithms studied here provide various tradeoffs between computational complexity, estimation precision, and necessary sampling rate. The work shows that compressive sensing for the class of sparse translation-invariant signals allows for a decrease in sampling rate and that the use of polar interpolation increases the estimation precision.

This work develops a communication theoretic model for the design and analysis at the physical layer of a reader receiver structure for passive UHF RFID. The objective is attaining multi-packet reception capabilities which in turn help the fast resolution of multiple tags through a more rapid and power efficient arbitration of the tags collisions. In particular, we derive a parametric continuous time model for the subspace of a tag signal at the noisy receiver/reader, which in addition to being affected by fading and receiver delay, exhibits wide variations in the symbol frequency and transmission delay, due to imperfections in the RFID hardware design. Our main contribution is in showing that channel fading, the difference in delay and the tags frequency dispersion can be transformed from foes to friends by exploiting them in a multipacket receiver. In fact, signals colliding from different tags are more easily separable by estimating the sensor specific variation in frequency and delay and using these estimates in a multiuser receiver. In our study, we specifically consider a successive interference cancellation algorithm followed by a maximum likelihood sequence decoder, that iteratively reconstructs one signal contribution at a time and then removes it from the received signal. Numerical simulations show that the estimates and proposed algorithm are effective in recovering collisions. The proposed algorithm is then incorporated into a numerical simulation of the Q-protocol for UHF RFID tags and is shown to be effective in providing fast and power efficient arbitration.

Existing approaches to compressive sensing of frequencysparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.

The technology of Radio Frequency IDentification (RFID) is being deployed in many applications, such as logistics and inventorying. However, a generic problem in all RFID systems is to ensure that the RFID readers can reliably read a set of RFID tags, such that the probability of missing tags stays below some acceptable value. This paper introduces statistical methods to deal with the problem of missing RFID tags. These methods are applied at the reliability layer, which initiates multiple reader sessions by invoking certain MAC-layer arbitration (anti-collision) protocols to collect tag responses. The reliability layer obtains a running estimate of the probability of having at least one tag missing. This estimate is used to detect if an additional reader session is required. We present several estimators, which can be used at the reliability layer to obtain an estimate of the probability of missed tags after R reader sessions have been carried out. These estimators are derived under idealized assumptions. However, when tested under more realistic conditions, which violate these ideal assumptions, the estimators exhibit high robustness and provide a very close approximation of the true probability of missing tags.

The technology of Radio Frequency IDentification (RFID) enables many applications that rely on passive, battery-less wireless devices. If a RFID reader needs to gather the ID from multiple tags in its range, then it needs to run an anti-collision protocol. Due to errors on the wireless link, a single reader session, which contains one full execution of the anti-collision protocol, may not be sufficient to retrieve the ID of all tags. This problem can be mitigated by running multiple, redundant reader sessions and use the statistical relationship between these sessions. On the other hand, each session is time-consuming and therefore the number of sessions should be kept minimal. We optimize the process of running multiple reader sessions, by allowing only some of the tags already discovered to reply in subsequent reader sessions. The estimation procedure is integrated with an actual tree-based anticollision protocol, and numerical results show that the reliable tag resolution algorithm attain high speed of protocol execution, while not sacrificing the reliability of the estimators used to assess the probability of missing tags.