Due to the duality between source coding and channel coding, high-performance channel codes are often adopted for addressing source coding problems. In this paper, we investigate the design and analysis of protograph low-density parity-check (P-LDPC) codes in lossy source coding systems. First, we propose a novel lossy source coding architecture based on P-LDPC codes, and empirically verify that although high-performance channel codes perform exceptionally well in their intended domain, they are not inherently optimal for lossy compression. To facilitate analysis, we introduce the lossy compression protograph extrinsic information transfer (LC-PEXIT) algorithm, which aids in evaluating the rate-distortion (RD) performance of P-LDPC codes. Additionally, the LC-PEXIT algorithm allows for the prediction of key parameters, such as the prior coefficientPand the reinforcement rateR, both of which critically influence system performance. We further propose a design algorithm for lossy P-LDPC codes and provide two sets of design examples. Experimental results show strong alignment between the predicted and actual values ofPandRf, with the designed P-LDPC codes exhibiting superior RD performance compared to classical P-LDPC and state-of-the-art ultra-sparse LDPC (US-LDPC) codes. In particular, the designed P-LDPC codes over benchmark codes increases the system coding efficiency of up to approximate 70% and achieves a performance gain of 1.69 ~ 3.26 dB, making it highly effective for lossy compression applications.

With the continuous development of UAV technology, the application of UAV swarm in the military field is gradually becoming the focus of research around the world. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues such as a single point of failure brought by centralized management. Because of the advantages of distributed, tamper-proof, and traceability, blockchain is applied to UAV swarm to solve some of the security problems caused by centralized management. However, due to the limitations of its consensus algorithm Practical Byzantine Fault Tolerance (PBFT), its communication complexity will increase rapidly with the increase of the number of nodes, which also leads to the poor scalability of the algorithm and can only be applied to small-scale networks. To use the PBFT algorithm in large-scale networks such as UAV swarm, a dynamic and highly trusted PBFT (DTPBFT) algorithm is proposed in this paper. Firstly, a new consensus algorithm model including consensus layer and verification layer is designed. Then, a consensus node election scheme based on trust mechanism is proposed under this model. The trust degree of nodes in the blockchain network is comprehensively evaluated by selecting several representative indicators, and the weight factor of each indicator is calculated by entropy weight method. It not only reduces the communication complexity, but also ensures the reliability and dynamic update of consensus nodes. Experiments show that when the number of UAVs is 200, the consensus time of DTPBFT is 0.24 s, which indicates that this algorithm can support large-scale UAV swarm without causing communication congestion, so it has good scalability. In addition, experiments also show that DTPBFT can tolerate more than 13 malicious nodes, which improves the fault tolerance rate of PBFT.