IRCI Free Range Reconstruction for SAR Imaging with Arbitrary Length OFDM Pulse

Our previously proposed OFDM with sufficient cyclic prefix (CP) synthetic aperture radar (SAR) imaging algorithm is inter-range-cell interference (IRCI) free and achieves ideally zero range sidelobes for range reconstruction. In this OFDM SAR imaging algorithm, the minimum required CP length is almost equal to the number of range cells in a swath, while the number of subcarriers of an OFDM signal needs to be more than the CP length. This makes the length of a transmitted OFDM sequence at least almost twice of the number of range cells in a swath and for a wide swath imaging, the transmitted OFDM pulse length becomes long, which may cause problems in some radar applications. In this paper, we propose a CP based OFDM SAR imaging with arbitrary pulse length, which has IRCI free range reconstruction and its pulse length is independent of a swath width. We then present a novel design method for our proposed arbitrary length OFDM pulses. Simulation results are presented to illustrate the performances of the OFDM pulse design and the arbitrary pulse length CP based OFDM SAR imaging.Comment: 29 pages, 10 figures, regular pape

Space-Time Transmit-Receive Design for Colocated MIMO Radar

This chapter deals with the design of multiple input multiple-output (MIMO) radar space-time transmit code (STTC) and space-time receive filter (STRF) to enhance moving targets detection in the presence of signal-dependent interferences, where we assume that some knowledge of target and clutter statistics are available for MIMO radar system according to a cognitive paradigm by using a site-specific (possible dynamic) environment database. Thus, an iterative sequential optimization algorithm with ensuring the convergence is proposed to maximize the signal to interference plus noise ratio (SINR) under the similarity and constant modulus constraints on the probing waveform. In particular, each iteration of the proposed algorithm requires to solve the hidden convex problems. The computational complexity is linear with the number of iterations and polynomial with the sizes of the STTW and the STRF. Finally, the gain and the computation time of the proposed algorithm also compared with the available methods are evaluated

Robust Distributed Fusion with Labeled Random Finite Sets

This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $\delta$-GLMB, marginalized $\delta$-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.Comment: 17pages, 23 figure