An algorithm for parameter estimation of frequency hopping emitters and their separation and grouping in unique radio networks

نویسندگان

  • Ivan P. Pokrajac
  • Miroslav L. Dukić
چکیده

1) Military Technical Institute, (VTI), Ratka Resanovića 1, 11132 Belgrade, SERBIA 2) Faculty of Electrical Engineering, Kralja Aleksandra 73, 11000 Belgrade, SERBIA Introduction ODERN radio surveillance systems have to intercept signals with unknown parameters in very complex multiple incident signal scenario in environments of high noise and interference. Furthermore, surveillance systems are often faced with the low probability-of-intercept (LPI) signals such as spread spectrum signals. Frequency hopping-FH signals are a class of spread spectrum signals. Interception of frequency hopping signals is a very complex and challenging technical problem. In typical tactical situation more classical as well as frequency hopping emitters with unknown signal parameters are active at the same time as a frequency sub-band. These unknown parameters have to be estimated from the radio signal received in the given frequency band and time observation interval. Modern radio surveillance systems have to provide detection of FH signals, estimation of direction-of-arrival (DOA), separation of FH signals from narrowband signals, separation and grouping of FH transmitters in unique radio networks. An algorithm for the estimation of FH signal parameters such as: hop duration, frequency shift and distance between two adjacent frequency channels in case when many of FH signals are more superposed on antenna array, is presented in this paper. The algorithm is based on the spatial-timefrequency signal analysis. An algorithm for separation and grouping of FH transmitters in unique radio networks is proposed and presented as well.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Robust Distributed Estimation Algorithm under Alpha-Stable Noise Condition

Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a rob...

متن کامل

Multiple Target Tracking in Wireless Sensor Networks Based on Sensor Grouping and Hybrid Iterative-Heuristic Optimization

A novel hybrid method for tracking multiple indistinguishable maneuvering targets using a wireless sensor network is introduced in this paper. The problem of tracking the location of targets is formulated as a Maximum Likelihood Estimation. We propose a hybrid optimization method, which consists of an iterative and a heuristic search method, for finding the location of targets simultaneously. T...

متن کامل

Distributed Incremental Least Mean-Square for Parameter Estimation using Heterogeneous Adaptive Networks in Unreliable Measurements

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defi...

متن کامل

Target Tracking with Unknown Maneuvers Using Adaptive Parameter Estimation in Wireless Sensor Networks

Abstract- Tracking a target which is sensed by a collection of randomly deployed, limited-capacity, and short-ranged sensors is a tricky problem and, yet applicable to the empirical world. In this paper, this challenge has been addressed a by introducing a nested algorithm to track a maneuvering target entering the sensor field. In the proposed nested algorithm, different modules are to fulfill...

متن کامل

A Surface Water Evaporation Estimation Model Using Bayesian Belief Networks with an Application to the Persian Gulf

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008