This study deals with an improvement of radar cross section (RCS) of stealth target model such as F‐117A with a higher aspect vision, and proposes a new detection technique based on stratospheric balloon‐borne netted radar system. Normally, it is very difficult to statistically model a stealth target by conventional analytically probability density function (pdf) expressions. Thus, a novel non‐parametric detection technique using time difference of arrival (TDOA) and physical optics (PO) approximation method is proposed. Finally, Legendre orthogonal polynomials (LOP) using to reconstruct the pdf of stealth RCS predicted by PO. The potential problem of proposed scheme is instability of the platform caused by sudden gusts of winds. The balloon flight control is studied in detail by considering external force of wind that is typical random process with Dryden turbulence spectrum. The proposed geometrical structure of radar system is composed of one transmitter and multiple receivers. To achieve high accuracy of locating a stealth target, the proposed scheme uses accuracy RCS measurement to compute Stealth target position. Simulations demonstrate that proposed scheme using one transmitter and multiple balloon‐borne receivers give much higher location accuracy comparing to other geometrical systems because of increasing the PO – scattered field with higher aspect angles.

The simultaneous imaging and tracking of drones and small UAVs is one of the most challenging problems in inverse synthetic aperture radar (ISAR) signal processing and has received significant attention recently. Wide-angle ISAR imaging may offer better resolution and richer geometrical features. Aiming at this issue, we propose a new algorithm for multiple extended drone targets (EDTs) detection and tracking from wide-angle ISAR images. Since a conventional tracking algorithm using a random matrices model (RMM) is not accurate enough to reconstruct a drone's extension by one ellipse, an appropriate technique needs to be developed for ISAR imaging and tracking of EDTs. This paper models the EDT's extension as multiple ellipses with a time-varying orientation angle and solves the resulting interference problem involving data association between the nonlinear measurements and sub-ellipses. Recently, the multi-Bernoulli (MB) filter for extended targets has been proposed with the RMM approach for linear ellipsoidal target tracking by additional state variables. However, the implementation of the MB-RMM filter for nonlinear non-Gaussian models and the Doppler effect has not been presented, especially for ISAR images. We consider linearizing the nonlinear observations to solve the nonlinearity due to the Doppler effect, target motion, and wide-angle ISAR images. We presented a new robust Sub-RMM-MB-TBD filter to obtain the estimated orientation of EDT's shape after using the range-only motion compensation that uses both range and Doppler information. Moreover, the authors applied the k-space algorithm to generate ISAR image. Simulation results demonstrate this remarkable performance.

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non‐linear Gaussian system based on track‐before‐detect (TBD) approach. The sequential Monte Carlo cardinality‐balanced multi‐target multi‐Bernoulli (SMC‐CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)‐CBMeMBer filter which employs a third‐degree spherical‐radical cubature rule has been proposed to handle the non‐linear models, the CK‐CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC‐CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK‐CBMeMBer filter based on random matrix model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM‐ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape, and orientation. Therefore, they introduce a filter composed of sub‐ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter.