To address the defocusing problem faced by random stepped frequency (RSF) radars, this paper presents a complementary code cancellation (CCC) method to estimate the target velocity and achieve motion compensation. The proposed CCC method is capable of eliminating the coupling effect between range profile and target velocity. In this paper, we first give a block diagram of RSF radar modulated by the M-sequence generated pseudorandom code, and introduce the baseband sampling echo model of moving target. Then, the velocity estimation accuracy is derived to show the sensitivity of high-resolution range profile to target velocity. Subsequently, the CCC method is proposed and also investigated in the application of multi-target scenario. Performance analyses demonstrate that the proposed method can satisfy the estimation accuracy requirement and improve the signal to noise ratio by the velocity accumulation. Finally, simulations show that the method is effective and more computationally efficient than the existing popular methods.

Dual-axis rotational inertial navigation system (DRINS) can achieve self-calibration of error parameters through a reasonable rotation scheme. However, the traditional self-calibration methods rely on the external reference information, which fail in global navigation satellite system (GNSS)-denied environments. In long-endurance marine navigation applications, carriers are usually equipped with multiple sets of DRINS. The geometric relationship between the DRINSs can serve as constraint observation for error parameter estimation. Therefore, this article proposes a collaborative calibration method with the navigation information fusion of dual DRINSs in GNSS-denied environments. Considering scale factor error, installation error, gyro drift, accelerometer bias, and accelerometer size-effect error introduced by the rotation of dual DRINSs, a 66-D Kalman filter is established based on the geometric constraint observation. Then, a novel collaborative calibration scheme is designed through analyzing the principles of error state observability for dual DRINSs. Simulations and experiments show that all error parameters can be precisely estimated by the proposed method with the designed calibration scheme and the calibration accuracy could satisfy the demand of long-endurance marine navigation.

Because of the non-orthogonal configuration of multi-sensors, the redundant inertial navigation system (INS) has a more complex error model compared with the traditional orthogonal INS, and the complexity of sensors configuration also increases the difficulty of error separation. Based on sufficient analysis of the error principle of redundant IMUs, a generalized high-accuracy calibration modeling method which is suitable for filtering method systematic calibration is summarized in this paper, and it has been applied to an RIMU prototype consisting of four ring laser gyros (RLGs) and four quartz accelerometers. Through the rotational excitation of the three-axis turntable in the laboratory, the high-precision filtering method systematic calibration of the RIMU is achieved, and static navigation and dynamic vehicle test experiments are also carried out. The experimental results reflect that the positioning accuracy can be obviously improved by using this new systematic calibration error model and the validity of this modeling method is also verified.

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

The modulation frequencies of random stepped frequency radars are randomly distributed over the expected specific bandwidth. The characteristics of random modulation frequencies make it difficult to synthesize the high-resolution range profile fleetly. A zero-padding inverse discrete Fourier transform method is proposed to address the problem. Theoretical analysis shows that the proposed method has the same signal-to-noise ratio improvement performance as the classical methods, but relatively lower computational complexity. The experimental results validate the effectiveness and efficiency of the proposed method.