Advanced detection algorithms for passive radar
Abstract
Passive radar is a kind of bistatic radar with no dedicated transmitters. That is, only non-cooperative transmitters, known as illum,inators of opportunity, are employed. The absence of transmitters results in very simple and low-cost systems, which has ignited the, increasing interest of the last decades. Nevertheless, these advantages come at a cost, since we have no control over the transmitt,ed signals, which may likely degrade the system s performance. To partly overcome this issue, many works consider a setup where an a,dditional reference channel is available, which improves the performance at the expense of requiring more advanced detection algorit,hms.In this project, we shall first derive optimal invariant detectors for the two-channel multiple-input multiple-output (MIMO) hyp,othesis test. Moreover, the use of non-cooperative transmitters results in a lack of knowledge about the transmitted signal, but the,re may be some characteristics to benefit from. This is the typical scenario when communication systems are used as illuminators. We, will therefore devise algorithms that exploit some features of communication signals, such as temporal correlation and/or cyclostat,ionarity. Commonly, the passive radar problem is addressed in a batch fashion, i.e., the target is either present or not. However, w,e will also consider the problem from an online point of view and take into account that a target may appear/disappear abruptly. Thi,s case will be studied using the theory of change-point detection, for which we must develop techniques specifically designed for th,e two-channel MIMO detection problem and that exploit the features of communication signals. Finally, we will also investigate the c,ase of an unknown number of illuminators and more than one reference channel. Concretely, we will study how the previous detectors s,hould be adapted to include these additional features and also if the additional reference channels provide significant improvements,.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 04, 2022
- Source ID
- N629092312002
Entities
People
- David RamÃrez
Organizations
- Office of Naval Research
- United States Navy
- Universidad Carlos III de Madrid