Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy and a slow convergence speed. Therefore, this paper proposes a strong tracking cubature Kalman filter (CKF) adaptive interactive multi-model (AIMM) based on multiple fading factors. Firstly, this paper analyzes the structure of the CKF algorithm, introduces the fading factor of the strong tracking algorithm into the covariance matrix of the time update and measurement update, and adjusts the filter gain online and in real time, which can reduce the decline infilter accuracy caused by model mismatch. Secondly, Singer model, “current” statistical (CS) model, and Jerk model are selected in the model set of IMM and introduced singular value decomposition (SVD) decomposition to solve the problem that Cholesky decomposition cannot be performed in the CKF due to the model dimension expansion. Last, an adaptive algorithm for the Markov matrix in the IMM is proposed. The transition probability was adaptively modified by the value of the model likelihood function to enhance the proportion of matching models. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%.

Rotating object detection in remote sensing image has made substantial progress. However, the widely used feature extraction and fusion modules cannot accurately represent the spatial location and angle information of remote sensing objects in arbitrary orientation. A detection scheme that accurately expresses spatial angle characteristics is necessary when remote sensing objects are densely distributed. This paper proposes an effective network for rotating object detection in optical remote sensing image (BDR-Net). Firstly, a feature aggregation module (FAM) is designed to refine the feature extraction of rotating objects, which can generate refined feature maps. Secondly, two-dimensional (2-D) Gaussian modeling simulates the rotation angle information of the object. Thirdly, given that the Bhattacharyya distance is mathematically symmetric and rigorous, we design a Bhattacharyya distance based learning module (BDLM). We improve the loss function to more accurately guide the regression of the relative location information of a rotating box. The effectiveness of the proposed method is validated by the results of experiments on three public datasets: DOTA, DIOR, and HRSC2016. The results of our research prove that a Bhattacharyya distance with a Gaussian distribution could be used as an approximate regression loss of the object detection model. This paper offers an original approach to combining object detection with metric learning to apply rotating object detection to instrumentation in a deployable and rapid manner.

Sparse code multiple access can provide high spectral efficiency and massive connectivity for future wireless communication. Previous research on uplink codebook design mainly focused on the Rayleigh fading channel. In this letter, the codebook design criteria for uplink Rician fading channels are explored. The probability distribution of the weighted sums of the Rician random variables from each frequency resource is derived based on the independent non-central circular complex Gaussian random variable. Then, considering the pairwise error probability, the key performance indicators of multidimensional constellations for uplink Rician channels are explored. A sub-optimal optimization approach is deployed to generate an enhanced codebook for various Rician scenarios. Finally, numerical results are provided to verify the key performance indicators in the uplink Rician fading channels, and the performance of the proposed codebook is analyzed and discussed.

Sparse code multiple access (SCMA) is a promising technique for enabling massive connectivity and high spectrum efficiency in future machine-type communication networks. However, its performance crucially depends on well-designed multi-dimensional codebooks. In this paper, we propose a novel progressive codebook optimization scheme that can achieve near-optimal performance over downlink fading channels. By examining the pair-wise error probability (PEP), we first derive the symbol error rate (SER) performance of the sparse codebook in downlink channels, which is considered as the design criterion for codebook optimization. Then, the benchmark constellation group at a single resource element is optimized with a sequential quadratic programming approach. Next, we propose a constellation group reconstruction process to assign the sub-constellations in each resource element (RE) progressively. For the current RE, the assignment of the sub-constellations is designed by minimizing the error performance of the product distance of the superimposed codewords in previous REs. The design process involves both permutation and labeling of the sub-constellations in the benchmark constellation group. Simulation results show that the proposed codebooks exhibit significant performance gains over state-of-the-art codebooks in the low signal-to-noise ratio (SNR) region over various downlink fading channels.

As a code-domain non-orthogonal multiple access technique, sparse code multiple access (SCMA) is considered as a promising technique for future wireless Internet of Things (IoT) networks. The minimum Euclidean distance (MED) and minimum product distance (MPD) have been highlighted as the key performance indicators of the codebooks in additive gaussian white noise (AWGN) and downlink Rayleigh channels respectively. In this paper, based on the mother codebook, a novel codebook design scheme is proposed to achieve better error performance in both AWGN and downlink Rayleigh channel. The problem of constructing the mother codebook is considered as the quadratic assignment problem (QAP), where the Tabu searching algorithm is employed to reduce the complexity of searching for the best permutation result. Then, the rotation matrix is adopted to find the best degrees for the generation of the constellation group, and two algorithms are proposed to assign the obtained constellation in factor graph matrix. Taking the degree optimization and the constellation assignment into joint consideration, an improved unified optimization is further explored to maximize the MED of each user. Besides, a novel polarized modulation scheme is proposed, which places the symbols in the three dimensional (3D) stokes parameters to improve the performance of the system. Finally, simulation results are provided to show the performance of the proposed codebooks, and the comparisons of symbol error performance (SER) in different codebooks are also discussed in detail.

In this paper, an improved LT code with a reverse coding framework is designed to reduce the error floor caused by low-degree information nodes. For the proposed coding scheme, a well-designed threshold is used to mark the information nodes whose degrees are less than the threshold, and these nodes will be coded reversely to connect to enough candidate check nodes. To design the optimal threshold, firstly, the information degree distribution and the check degree distribution of the improved LT code are deduced. Then, the parameter extrinsic information gain-loss-ratio (GLR) is designed to evaluate the convergence behavior of the improved LT code. Finally, the ‘slow increase region’ of the GLR is set, and the boundary value of this region is used to deduce the optimal threshold which matches with the channel state information (CSI) and decoding overhead. To make the proposed LT code not limited to a fixed code rate, we further modify the proposed scheme. The segment coding method is used to generate a redundant generator matrix, and the check nodes corresponding to this matrix can be transmitted independently and are not limited to a fixed number. Furthermore, the connection relationship between information nodes and check nodes can be easily recorded, which improves the decoding efficiency. The advantages of the improved LT code are that the degree distributions can be formulated, the convergence behavior can be predicted, and the lowest information degree can be adjusted. Simulation results show that the improved LT code can reduce the error floor by up to 4 orders of magnitude. Besides, the designed LT code outperforms the existing LT codes in literature in terms of bit error rate (BER) performance.